Journal of Computation Physics and Earth Science (JoCPES)
type: channel
description: The Journal of Computation Physics and Earth Science (JoCPES) publishes cutting-edge research on computational physics, material science, meteorology, climatology, geophysics, and more. It offers a platform for original research articles in interdisciplinary topics like data analysis, AI, and IoT.
publisher: Yayasan Kita Menulis - JoCPES
language: en
publication name: Journal of Computation Physics and Earth Science (JoCPES)
issn: 2776-2521
copyright: License Terms This work is licensed under the Creative Commons Attribution-ShareAlike 4.0 International License (CC BY-SA 4.0). By submitting and publishing your work in this journal, you agree to the following terms: Attribution: You must give appropriate credit to the original author(s) and provide a link to the license. You may do so in any reasonable manner, but not in any way that suggests the licensor(s) endorse you or your use. ShareAlike: If you remix, transform, or build upon the material, you must distribute your contributions under the same license as the original. This means any derivative works must be shared alike under CC BY-SA 4.0. Non-Exclusive Distribution: Authors retain copyright of their work, and publishing in this journal does not transfer ownership. Authors grant the journal a non-exclusive, worldwide, and royalty-free license to publish, share, and distribute the work under the CC BY-SA 4.0 license. Commercial and Non-Commercial Use: The work can be used for both commercial and non-commercial purposes, provided that the original author(s) are properly credited, and derivative works are distributed under the same terms. No Warranty: The work is provided "as is", and the authors or the journal make no warranties regarding the accuracy, completeness, or reliability of the content. Derivative Works: You may create derivative works of the material, but such works must be licensed under the same CC BY-SA 4.0 license and must attribute the original author(s) appropriately. No Additional Restrictions: You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits. For more information, please refer to the full terms of the Creative Commons Attribution-ShareAlike 4.0 International License.
items: Design and Implementation of a Web-Based RFID-Enabled Library Visitor Attendance and Management System: A Case Study at STMKG
items: Optimization of Electronic Control for a Monohull Leisure Boat Using Meteorological Data to Enhance Stability and Safety in Coastal Environments
items: Design and Implementation of a React.js-Based Monitoring Dashboard for Tangerang Weather Radar and Server Room Air Conditioning Control
items: Design and Implementation of an IoT-Based 3-Phase Electricity Usage Monitoring System: Case Study at Geophysics Station Class I Sleman, Yogyakarta
items: Meteorological Analysis of Strong Wind-Producing Clouds: A Case Study of Klaten, 18 November 2024
items: Estimating Aftershock Termination Time Using Statistical Decay Models: A Case Study of the March 22, 2024 Bawean Earthquake
items: Revealing the Subsurface Geometry of the 2023 Sumedang Earthquake Sequence Using Double-Difference Relocation and Cross-Section Analysis
items: Fault Mapping of the Central Aceh Segment of the Sumatran Fault as an Extension of the Great Sumatran Fault (GSF) Using Gravity Analysis
items: Estimation of Jayapura 2023 Aftershock Decay Time Using Python-Based Secant Algorithm
items: Subsurface Structural Identification and Seismicity Correlation in West Java Using EMAG2 Geomagnetic Data
items: Subsurface Structure of Palu-Koro Fault Zone Using TOPEX Satellite Gravity Data and Regional-Residual Anomaly Separation
items: Deterministic Seismic Hazard Analysis (DSHA) and Peak Ground Acceleration Mapping in West Java (2018–2024)
items: Solar Radiation Computation from Satellite Weather Data in Batam Using Linear Regression, Random Forest, and Decision Tree
items: Design and Development of an IoT-Based UV Meter and Dual-Axis Solar Tracker for Real-Time UV Index Monitoring Optimization
items: Redesign of User Interface and Experience with Brand Identity Enhancement for the STMKG Website through WordPress Implementation
items: Low Cost Sensor Technology Innovation in Air Quality Monitoring: A Systematic Literature Review on Opportunities for Public Budget Efficiency
items: Impact of Urban Heat Island on Shifting Weather Patterns in Sidoarjo and Malang: BMKG Daily Data-Based Analysis
items: Design and build an IoT system for temperature and humidity observation using DHT 22 with ThingSpeak
items: Implementation of Web-based Realtime Monitoring System Using YOLOv8 for Green Box Detection and Automatic Capture in Navigation Missions at the Indonesian Boat Contest
items: Flood Early Warning Monitoring System Using KNN Methods In Bangkalan District
items: Comparison of Random Forest and LSTM Methods for Temperature Prediction
items: Residual Network Architecture Model for Image Weather Classification
items: Rainfall Trend Analysis in Tangerang City Using Linear Regression and Random Forest
items: Machine Learning Approaches for Classifying Indian Ocean Dipole (IOD) Using Random Forest and Decision Tree Models with SST, MSLP, And Total Precipitation Data from the Waters Off West Sumatra
items: Automated Analysis of Radiosonde Temperature Data Using Python: A Study on Data Homogenization and Climate Trends Observed at Sta. Met. Kelas I Sultan Iskandar Muda - Banda Aceh
items: PM2.5 Concentration Prediction Model in Jakarta Area Using Random Forest Algorithm
items: Evaluation of the XGBoost Model for Rainfall Prediction and Classification Using BMKG Data and OpenWeather API
items: Comparative Analysis of Linear Regression Models and XGBoost to Assess the Impact of ENSO on Rainfall in Ternate City in 2023
items: Prediction of Aquaculture Quality Using Long Short-Term Memory in the Sunda Strait Region
items: Analysis of the Use of Telegram Bot for Earthquake Information Dissemination Systems and Weather Forecasting

Design and Implementation of a Web-Based RFID-Enabled Library Visitor Attendance and Management System: A Case Study at STMKG
type: item
description: enabled library visitor attendance and management system as a single-site case study at STMKG. Academic libraries require structured visit data to support service evaluation and institutional reporting, yet manual logbooks often produce inconsistent records and limit timely analysis. To address this issue, the proposed system digitalizes attendance capture through three integrated workflows within one platform: RFID-based identification for cadets, controlled selection for employees, and a structured manual form for public visitors. The system also provides administrative capabilities for monitoring and reporting, including visit statistics dashboards, time-filtered logs with search, and spreadsheet export for routine reporting needs. The implementation adopts a cloud-backed architecture using a modern web application frontend and a managed backend with database persistence and authentication services. Baseline security controls were incorporated to protect administrative functions and reduce automated abuse at public-facing entry points, including access verification, bot protection, and database-level access boundaries. Functional verification through end-to-end scenario testing confirms that the core attendance workflows, reporting features, and security mechanisms operate as intended within the defined scope. The resulting artifact is deployable in STMKG and can be adapted for similar higher-education libraries seeking practical visitor attendance digitalization.
subject: Articles
  topic: section
subject: Library Management System
  topic: keywords
subject: RFID
  topic: keywords
subject: Information System
  topic: keywords
subject: Digitalization
  topic: keywords
subject: STMKG
  topic: keywords
subject: Library Management System
  topic: subjects
subject: RFID
  topic: subjects
subject: Information System
  topic: subjects
subject: Digitalization
  topic: subjects
subject: STMKG
  topic: subjects
creator: Ahmad Meijlan Yasir
creator: Tonny Wahyu Aji
rights: Copyright (c) 2026 Journal of Computation Physics and Earth Science (JoCPES) https://creativecommons.org/licenses/by-sa/4.0
date: 2026-03-19
publication date: 2026-03-19
doi: 10.63581/JoCPES.v5i2.01

Optimization of Electronic Control for a Monohull Leisure Boat Using Meteorological Data to Enhance Stability and Safety in Coastal Environments
type: item
description: Indonesia’s maritime geography offers significant potential for marine tourism but requires transportation systems capable of operating safely under variable coastal environmental conditions influenced by wind and wave variability. This study presents the design and evaluation of a compact monohull leisure boat integrated with an Electronic Remote Control (ERC) system to improve maneuverability and operational safety. The research applies an engineering design approach involving regression-based estimation of principal dimensions, digital hull modeling using Maxsurf, static stability evaluation through righting arm (GZ) analysis, and spatial optimization using a General Arrangement (GA) plan. Five alternative hull geometries were developed and analyzed to identify the most suitable configuration for marine tourism operations. The stability analysis indicates that all hull models exhibit positive righting arm values within the evaluated heel angle range, confirming adequate static stability under typical coastal conditions. Among the evaluated configurations, Model 4 demonstrates the most favorable stability characteristics. The integration of the ERC system further enhances vessel maneuverability and reliability during low-speed navigation and docking, supporting safer marine tourism transportation in coastal waters.
subject: Articles
  topic: section
subject: monohull leisure boat
  topic: keywords
subject: unmanned surface vehicles
  topic: keywords
subject: remote control system
  topic: keywords
subject: marine tourism
  topic: keywords
subject: static stability analysis
  topic: keywords
subject: unmanned surface vehicle
  topic: subjects
subject: remote control system
  topic: subjects
subject: monohull leisure boat
  topic: subjects
subject: marine robotics
  topic: subjects
creator: Muhammad Ramdhani Setyo Nugroho
creator: Thalia Salsabila
creator: Yobel Eliezer Mahardika
creator: Rizky Ramadhani
creator: Anton Widodo
rights: Copyright (c) 2026 Journal of Computation Physics and Earth Science (JoCPES) https://creativecommons.org/licenses/by-sa/4.0
date: 2026-03-19
publication date: 2026-03-19
doi: 10.63581/JoCPES.v5i2.04

Design and Implementation of a React.js-Based Monitoring Dashboard for Tangerang Weather Radar and Server Room Air Conditioning Control
type: item
description: The Tangerang Weather Radar Station plays a critical role in aviation safety, yet its maintenance workflow is currently fragmented. Technicians must monitor radar telemetry and control server room cooling systems using separate, non-integrated platforms. This study addresses this inefficiency by developing a centralized web dashboard based on the React.js and Node.js framework. The system integrates real-time BITE (Built-In Test Equipment) data from the radar and provides remote control for the air conditioning units via an existing IoT infrastructure. Testing results demonstrate that the dashboard accurately renders telemetry parameters (High Voltage Power Supply, Transmitter Power, and VSWR) and executes AC control commands with minimal latency within the local network environment. The responsive design ensures accessibility across desktop and mobile devices, providing a unified "Single Pane of Glass" solution for maintaining optimal radar operation and server environmental conditions.
subject: Articles
  topic: section
subject: Weather Radar
  topic: keywords
subject: Server Room Monitoring
  topic: keywords
subject: React.js
  topic: keywords
subject: Node.js
  topic: keywords
subject: System Integration
  topic: keywords
subject: Weather Radar
  topic: subjects
subject: Server Room Monitoring
  topic: subjects
subject: React.js
  topic: subjects
subject: Node.js
  topic: subjects
subject: System Integration
  topic: subjects
creator: Muhammad Fadil Pradana Andi Alief
creator: Nadila R Muhiddin
creator: Bintoro Puspo Adi
creator: Kadek Ari Sudama
rights: Copyright (c) 2026 Journal of Computation Physics and Earth Science (JoCPES) https://creativecommons.org/licenses/by-sa/4.0
date: 2026-03-19
publication date: 2026-03-19
doi: 10.63581/JoCPES.v5i2.03

Design and Implementation of an IoT-Based 3-Phase Electricity Usage Monitoring System: Case Study at Geophysics Station Class I Sleman, Yogyakarta
type: item
description: Efficient electrical energy management is increasingly important in supporting the sustainability of institutional operations and national budget efficiency policies. This study presents the design and implementation of an Internet of Things (IoT)-based 3-phase electricity usage monitoring system implemented at the Sleman Class I Geophysical Station, Yogyakarta. This system integrates a PZEM-004T energy sensor, an ESP32 microcontroller, a 16x2 Liquid Crystal Display, and a W5500 Ethernet module to acquire, process, and transmit real-time electrical parameters, including voltage, current, and kWh. A local network topology is implemented to ensure stable data transmission to a MySQL-based database server, which allows storing data according to a predefined table structure. A web-based user interface is developed to visualize real-time measurements, historical graphs, and electricity cost estimates. Several features are also developed. System evaluation is conducted by comparing the developed instrument with the official PLN kWh meter. Statistical analysis shows an average difference of 0.021 kWh with a standard deviation of 0.02132 kWh. The linear regression results show a slope of 1.000349, an intercept of −3.589572, and a coefficient of determination (R²) of 0.9999988, indicating a near-perfect linear correlation. Error metrics including MAE (0.01934 kWh), RMSE (0.02696 kWh), and MAPE (0.0001885%) confirm the high accuracy and precision of the system. Therefore, the proposed system is reliable for real-time internal energy monitoring and supports data-driven electricity usage optimization.
subject: Articles
  topic: section
subject: IoT
  topic: keywords
subject: Electrical
  topic: keywords
subject: Monitoring System
  topic: keywords
subject: ESP32
  topic: keywords
subject: PZEM-004T
  topic: keywords
subject: IoT
  topic: subjects
subject: Electrical
  topic: subjects
subject: Monitoring System
  topic: subjects
subject: ESP32
  topic: subjects
subject: PZEM-004T
  topic: subjects
creator: Arfany Dhimas Muftareza
creator: Bagas Pamungkas Saputra
creator: Dimas Aditya Wiranata
creator: Fahar Rafif Arganto
creator: Hilldegard Virgil Richard May
creator: Kholid Amirudin
creator: Muhammad Arya Bintang Pratama
creator: Muhammad Ramdhani Setyo Nugroho
creator: Arif Kurniawan
rights: Copyright (c) 2026 Journal of Computation Physics and Earth Science (JoCPES) https://creativecommons.org/licenses/by-sa/4.0
date: 2026-03-19
publication date: 2026-03-19
doi: 10.63581/JoCPES.v5i2.02

Meteorological Analysis of Strong Wind-Producing Clouds: A Case Study of Klaten, 18 November 2024
type: item
description: Klaten Regency, Central Java, has frequently experienced extreme convective weather in the form of strong surface winds that have caused damage to residential areas and public facilities. This study analyzed a strong wind event that occurred on 18 November 2024 between 15:30 and 16:30 Local Time, which had significant impacts across several sub-districts in Klaten. The objective of this research was to evaluate the atmospheric dynamics and cloud characteristics responsible for the event. A descriptive–analytical approach was applied using secondary data, including the Dipole Mode Index, the Madden–Julian Oscillation Diagram, streamline map, and atmospheric stability indices to assess atmospheric dynamics. In addition, dual-polarization weather radar products were used to examine convective cloud structure, vertical development, wind convergence and divergence patterns, and indications of vortex formation. The results showed that the event was dominated by a rapidly developing Cumulonimbus cloud system, with maximum reflectivity values reaching approximately 69 dBZ and cloud-top heights of about 10 km. Low-level wind convergence and horizontal wind shear played a critical role in triggering deep convection. Atmospheric stability indices indicated sufficiently unstable conditions, although instability was not extreme. Doppler radar analysis revealed strong updraft–downdraft interactions, intense convergence–divergence patterns, and vortex formation during the mature stage of the storm.
subject: Articles
  topic: section
subject: Strong winds
  topic: keywords
subject: Atmospheric dinamics
  topic: keywords
subject: Cloud characteristics
  topic: keywords
subject: Cumulonimbus cloud
  topic: keywords
subject: Klaten
  topic: keywords
subject: Strong winds
  topic: subjects
subject: Atmospheric dynamics
  topic: subjects
subject: Cloud characteristics
  topic: subjects
subject: Cumulonimbus cloud
  topic: subjects
subject: Klaten
  topic: subjects
creator: Febby Debora Abigael
creator: Ahmad Hanif Al'aziz
creator: Arya Zaki Ramadhan
creator: Achmad Zakir
creator: Aditya Mulya
rights: Copyright (c) 2026 Journal of Computation Physics and Earth Science (JoCPES) https://creativecommons.org/licenses/by-sa/4.0
date: 2026-03-19
publication date: 2026-03-19
doi: 10.63581/JoCPES.v5i2.05

Estimating Aftershock Termination Time Using Statistical Decay Models: A Case Study of the March 22, 2024 Bawean Earthquake
type: item
description: An earthquake struck Bawean Island on March 22, 2024 with a magnitude of 6.5 followed by a series of aftershocks. In this study, the decay of aftershocks in the Bawean region was analyzed from March 22 to March 31, 2024 using the Omori Method, Mogi I Method, Mogi II Method, and Utsu Method. The purpose of this research is to determine the duration of aftershock decay in Bawean and to identify the most suitable statistical method for predicting aftershock decay time in the region. The analyzed data were obtained from the BMKG earthquake repository. By applying the four methods, results were obtained in the form of aftershock decay duration and correlation coefficients for each method. The analysis shows that the most appropriate statistical methods for calculating aftershock decay time in Bawean are the Mogi I Method and the Utsu Method both producing a correlation coefficient of approximately -0.806 and an aftershock decay duration of 24 days consistent with the BMKG earthquake catalog data.
subject: Articles
  topic: section
subject: Bawean Earthquake
  topic: keywords
subject: Aftershoocks
  topic: keywords
subject: Omori
  topic: keywords
subject: Mogi
  topic: keywords
subject: Utsu
  topic: keywords
subject: Bawean Earthquake
  topic: subjects
subject: Aftershocks
  topic: subjects
subject: Omori
  topic: subjects
subject: Mogi
  topic: subjects
subject: Utsu
  topic: subjects
creator: Martha Rajagukguk
creator: Rayhan Irfan Hielmy
rights: Copyright (c) 2026 Journal of Computation Physics and Earth Science (JoCPES) https://creativecommons.org/licenses/by-sa/4.0
date: 2026-03-19
publication date: 2026-03-19
doi: 10.63581/JoCPES.v5i2.10

Revealing the Subsurface Geometry of the 2023 Sumedang Earthquake Sequence Using Double-Difference Relocation and Cross-Section Analysis
type: item
description: The Sumedang Regency experienced a significant tectonic earthquake sequence beginning on December 31, 2023, which raised concerns regarding active fault structures in the region. Identifying the precise causative fault geometry was essential for seismic hazard mitigation but remained challenging due to the complex local geology and potential location errors in preliminary data. This study investigated the source mechanism and subsurface geometry of the aftershocks recorded between December 31, 2023, and January 8, 2024. The Double-Difference (HypoDD) method was applied to relocate earthquake hypocenters by minimizing travel-time residuals, utilizing a 1-D velocity model with a variable Vp/Vs ratio. Subsequently, vertical cross-section analysis was conducted to interpret the dip patterns and fault orientation perpendicular to the seismicity trend. The results revealed a significant transformation in the spatial distribution pattern; while initial data exhibited a linear north-south trend, the relocated hypocenters formed a distinct curved cluster extending from the south toward the west. The seismic activity was concentrated at shallow depths ranging from 3 km to 17 km. Cross-section interpretations suggested two potential fault geometries: a near-vertical alignment indicating a strike-slip mechanism potentially associated with the Subang Segment of the Baribis Fault, or an inclined, slab-like structure indicative of a thrust fault system. These findings provided critical constraints for future moment tensor inversion studies.
subject: Articles
  topic: section
subject: Sumedang Earthquake
  topic: keywords
subject: Hypocenter Relocation
  topic: keywords
subject: Double-Difference
  topic: keywords
subject: Cross-Section Analysis
  topic: keywords
subject: Baribis Fault
  topic: keywords
subject: Sumedang Earthquake
  topic: subjects
subject: Hypocenter Relocation
  topic: subjects
subject: Double-Difference
  topic: subjects
subject: Cross-Section Analysis
  topic: subjects
subject: Baribis Fault
  topic: subjects
creator: Rayhan Irfan Hielmy
rights: Copyright (c) 2026 Journal of Computation Physics and Earth Science (JoCPES) https://creativecommons.org/licenses/by-sa/4.0
date: 2026-03-19
publication date: 2026-03-19
doi: 10.63581/JoCPES.v5i2.06

Fault Mapping of the Central Aceh Segment of the Sumatran Fault as an Extension of the Great Sumatran Fault (GSF) Using Gravity Analysis
type: item
description: Aceh is known as one of the most tectonically active regions in Indonesia with a relatively high level of seismicity. One of the active zones is the Central Aceh Segment, which forms part of the Sumatra Fault as an extension of the Great Sumatran Fault. This study aims to identify the distribution and characteristics of fault zones and types in the Central Aceh Segment using gravity data from GGMplus. The analysis was carried out by applying the First Horizontal Derivative (FHD) and Second Vertical Derivative (SVD) methods. The results indicate significant lateral density contrasts that delineate the presence of a major fault and several secondary fault structures within the study area. Interpretation of SVD values along several profiles suggests that many of the inferred structures exhibit reverse fault characteristics, with some indications of normal faults. These features are interpreted as secondary deformation structures that may develop within a strike-slip fault under localized transpressional or transtensional stress regimes. Subsurface geological modelling along profiles AA′–BB′, BB′–CC′, and EE′–FF′ shows variations in rock density that highlight zones of high-density distribution.
subject: Articles
  topic: section
subject: Gravity First Horizontal Derivative (FHD) Second Vertical Derivative (SVD) Aceh Tengah Segment
  topic: keywords
subject: Gravity analysis, Fault mapping, Sumatran Fault, Central Aceh Segment, Tectonics, Geophysics
  topic: subjects
creator: Qisthi Mursyida
creator: Muhamad Syirojudin
rights: Copyright (c) 2026 Journal of Computation Physics and Earth Science (JoCPES) https://creativecommons.org/licenses/by-sa/4.0
date: 2026-03-19
publication date: 2026-03-19
doi: 10.63581/JoCPES.v5i2.08

Estimation of Jayapura 2023 Aftershock Decay Time Using Python-Based Secant Algorithm
type: item
description: Accurately determining the termination time of aftershocks is crucial for disaster mitigation and establishing safe periods for community recovery. This study aimed to estimate the decay time of the January 2023 Jayapura aftershock sequence (M 5.4) using a numerical computational approach. The Mogi II decay model was selected due to its high compatibility with local seismicity. To resolve its complex non-linear exponential equations without analytical derivatives, the Secant Method was implemented using Python. The algorithm was initialized with starting guess values of x0=0 and x1=1, and an error tolerance of 0.0001. To validate algorithmic robustness and efficiency, a sensitivity test was conducted, and the method was benchmarked against the Bisection method. Results demonstrated that the Secant algorithm achieved superior computational efficiency, converging in exactly 10 iterations (~0.000115 seconds) compared to Bisection's 18 iterations, while remaining highly stable under arbitrary extreme initial guesses. The numerical solution predicted the decay termination at day 12.765, subsequently rounded to 13 days following the mainshock. This finding showed exact agreement with manual observational data, successfully extrapolating the decay trajectory beyond the 10-day BMKG recording window. The study concluded that the Python-based Secant algorithm is effective, rapid, robust, and precise in solving the Mogi II equation, demonstrating significant potential as an automated analytical tool to enhance disaster mitigation decision-making.
subject: Articles
  topic: section
subject: Jayapura Earthquake
  topic: keywords
subject: Mogi II Model
  topic: keywords
subject: Non-linear Equation
  topic: keywords
subject: disaster mitigation
  topic: keywords
subject: Computational Seismology
  topic: keywords
subject: Jayapura Earthquake
  topic: subjects
subject: Mogi II Model
  topic: subjects
subject: Non-linear Equation
  topic: subjects
subject: Disaster Mitigation
  topic: subjects
subject: Computational Seismology
  topic: subjects
creator: Rayhan Irfan Hielmy
creator: Sandy Tri Gustono
rights: Copyright (c) 2026 Journal of Computation Physics and Earth Science (JoCPES) https://creativecommons.org/licenses/by-sa/4.0
date: 2026-03-19
publication date: 2026-03-19
doi: 10.63581/JoCPES.v5i2.07

Subsurface Structural Identification and Seismicity Correlation in West Java Using EMAG2 Geomagnetic Data
type: item
description: West Java represents one of Indonesia's most seismically active regions, characterized by complex interactions between the Indo-Australian subduction zone and active onshore fault systems. This study investigated subsurface geological configurations and their relationship to earthquake occurrences using Earth Magnetic Anomaly Grid (EMAG2) data with a 2-arc-minute resolution. The research methodology utilized Reduction to the Pole (RTP) to eliminate dipolar effects caused by the equatorial location, followed by spectral analysis to separate regional and residual anomalies. To precisely map structural lineaments, First Horizontal Derivative (FHD) and Second Vertical Derivative (SVD) techniques were applied to the residual data. The processed maps revealed distinct anomaly contrasts, with high magnetic intensities (up to 300 nT) associated with volcanic intrusions and low anomalies (approximately −100 nT) indicating sedimentary basins or hydrothermally altered zones. A critical analysis of the December 15, 2017, Tasikmalaya earthquake (Mw 6.5) identified a strong correlation between the epicenter and a significant low magnetic anomaly zone of −150 nT. This specific signature was interpreted as a fracture zone characterized by rock demagnetization resulting from tectonic stress accumulation. These findings confirm that integrating EMAG2 data with derivative filtering is a robust approach for delineating active tectonic structures, thereby contributing essential data for regional seismic hazard mitigation.
subject: Articles
  topic: section
subject: Geomagnetic Anomaly
  topic: keywords
subject: EMAG2
  topic: keywords
subject: Derivative Analysis
  topic: keywords
subject: Fault System
  topic: keywords
subject: Tasikmalaya Earthquake
  topic: keywords
subject: Geomagnetic Anomaly
  topic: subjects
subject: EMAG2
  topic: subjects
subject: Derivative Analysis
  topic: subjects
subject: Fault System
  topic: subjects
subject: Tasikmalaya Earthquake
  topic: subjects
creator: Rayhan Irfan Hielmy
rights: Copyright (c) 2026 Journal of Computation Physics and Earth Science (JoCPES) https://creativecommons.org/licenses/by-sa/4.0
date: 2026-03-19
publication date: 2026-03-19
doi: 10.63581/JoCPES.v5i2.09

Subsurface Structure of Palu-Koro Fault Zone Using TOPEX Satellite Gravity Data and Regional-Residual Anomaly Separation
type: item
description: The Palu-Koro Fault in Central Sulawesi represents a highly active tectonic feature with significant seismic hazard potential, yet detailed subsurface mapping remains challenging due to the difficult terrain in the region. This study analyzed the subsurface structure of the fault zone by utilizing TOPEX satellite gravity data to overcome accessibility issues. The primary objective was to delineate the fault geometry and characterize subsurface lithological boundaries through density contrasts. The data processing stage initially determined the average surface rock density using the Parasnis method, which yielded a precise value of 2.45 grams per cubic centimeter. This density was subsequently applied to generate the Simple Bouguer Anomaly map. To distinguish between deep-seated regional trends and shallow local structures, the study employed two filtering approaches: the second-order polynomial method and the Moving Average method. The results demonstrated that both filtering techniques yielded consistent residual anomaly patterns. The main trace of the Palu-Koro Fault was clearly identified as a continuous low-anomaly zone, interpreted as a fracture system filled with low-density sedimentary deposits. Conversely, significant high-amplitude positive anomalies were detected adjacent to the fault trace, suggesting the existence of shallow high-density bodies such as igneous intrusions or uplifted basement blocks. This research concluded that the integration of satellite gravity data with regional-residual anomaly separation successfully mapped the structural complexity of the area, providing critical baseline data for updating seismic hazard models and enhancing disaster mitigation strategies in Sulawesi.
subject: Articles
  topic: section
subject: Gravity Anomaly
  topic: keywords
subject: Palu-Koro Fault
  topic: keywords
subject: TOPEX Satellite
  topic: keywords
subject: Parasnis Method
  topic: keywords
subject: Derivative Filter
  topic: keywords
subject: Gravity Anomaly
  topic: subjects
subject: Palu-Koro Fault
  topic: subjects
subject: TOPEX Satellite
  topic: subjects
subject: Parasnis Method
  topic: subjects
subject: Derivative Filter
  topic: subjects
creator: Rayhan Irfan Hielmy
rights: Copyright (c) 2026 Journal of Computation Physics and Earth Science (JoCPES) https://creativecommons.org/licenses/by-sa/4.0
date: 2026-03-19
publication date: 2026-03-19
doi: 10.63581/JoCPES.v5i2.11

Deterministic Seismic Hazard Analysis (DSHA) and Peak Ground Acceleration Mapping in West Java (2018–2024)
type: item
description: West Java is a region with one of the most complex tectonic settings in Indonesia, influenced by the Sunda Megathrust subduction zone and an active inland fault system. This study aimed to estimate the Peak Ground Acceleration (PGA) values in the West Java region using a Deterministic Seismic Hazard Analysis (DSHA) approach with the Maximum of Maxima criteria. The data used is sourced from the BMKG earthquake catalog for the 2018–2024 period, which includes the most recent epicenter, hypocenter depth, and magnitude parameters. PGA calculations were performed using the Milne attenuation model for each grid point in the target area, followed by spatial interpolation using PyGMT software. The analysis results reveal a significant bimodal seismic hazard characteristic. An absolute maximum PGA value of 0.46 g was identified, triggered by a local shallow crustal earthquake with a magnitude of M 5.8 at a shallow depth within the inland region. Meanwhile, the southern coastal zone is dominated by the influence of a large Megathrust earthquake (M 8.7), although its shaking impact attenuates as it moves inland toward northern population centers. These findings confirm that while subduction sources possess greater energy potential, local active inland faults pose a more direct and destructive shaking threat to densely populated inland areas. The hazard map generated in this study is expected to serve as a crucial technical reference for resilient infrastructure planning and the updating of earthquake mitigation strategies in West Java Province.
subject: Articles
  topic: section
subject: DSHA
  topic: keywords
subject: PGA
  topic: keywords
subject: West Java
  topic: keywords
subject: BMKG Catalog
  topic: keywords
subject: Megathrust
  topic: keywords
subject: DSHA
  topic: subjects
subject: PGA
  topic: subjects
subject: West Java
  topic: subjects
subject: BMKG Catalog
  topic: subjects
subject: Megathrust
  topic: subjects
creator: Rayhan Irfan Hielmy
rights: Copyright (c) 2026 Journal of Computation Physics and Earth Science (JoCPES) https://creativecommons.org/licenses/by-sa/4.0
date: 2026-03-19
publication date: 2026-03-19
doi: 10.63581/JoCPES.v5i2.12

Solar Radiation Computation from Satellite Weather Data in Batam Using Linear Regression, Random Forest, and Decision Tree
type: item
description: This study addresses the necessity of evaluating solar radiation as a renewable energy source in tropical regions, specifically focusing on the challenges of estimation in Batam. The objective is to model daily solar radiation levels using satellite-derived weather data to overcome the lack of surface observation stations. Daily meteorological variables, including air temperature, relative humidity, rainfall, surface pressure, and wind speed, were sourced from the NASA POWER platform for the period January 1, 2020, to July 2, 2025. To ensure robust model generalization and prevent data leakage, the dataset was partitioned chronologically, utilizing data from 2020–2024 for training and the year 2025 for independent testing. Three computational models Linear Regression (LR), Random Forest (RF), and Decision Tree (DT) were applied to the processed data. The evaluation results indicate that the Random Forest model achieved the highest relative performance among the tested algorithms, recording a Mean Squared Error (MSE) of 19.61, a Mean Absolute Error (MAE) of 3.42, and a coefficient of determination R² of 0.20. In comparison, the Linear Regression model produced an R² of 0.19, while the Decision Tree showed significantly lower predictive accuracy. Despite being the most viable model, an R² of 0.20 reveals that the current predictors explain only 20% of the variance in solar radiation, highlighting the inherent complexity of tropical atmospheric dynamics. These findings suggest that while machine learning offers a promising framework for energy planning in Batam, further research incorporating additional explanatory features, such as cloud cover or aerosol indices, is required to improve model reliability.
subject: Articles
  topic: section
subject: Solar Radiation
  topic: keywords
subject: Weather Forecast
  topic: keywords
subject: Regression Model
  topic: keywords
subject: Machine Learning
  topic: keywords
subject: Linear Regression
  topic: keywords
subject: Random Forest
  topic: keywords
subject: Decision Tree
  topic: keywords
subject: Satellite Data
  topic: keywords
subject: Renewable Energy
  topic: keywords
subject: Batam City
  topic: keywords
subject: Solar Radiation, Satellite Weather Data, Machine Learning, Linear Regression, Random Forest, Decision Tree
  topic: subjects
creator: Prayoga Pandapotan Simanjuntak
creator: Marzuki Sinambela
rights: Copyright (c) 2026 Journal of Computation Physics and Earth Science (JoCPES) https://creativecommons.org/licenses/by-sa/4.0
date: 2026-03-19
publication date: 2026-03-19
doi: 10.63581/JoCPES.v5i2.13

Design and Development of an IoT-Based UV Meter and Dual-Axis Solar Tracker for Real-Time UV Index Monitoring Optimization
type: item
description: The ultraviolet (UV) radiation emitted by the sun has both positive and negative impacts on human life. Excessive exposure to UV rays can lead to various health issues, such as skin cancer and cataracts. Therefore, UV radiation monitoring becomes crucial, especially in the face of climate change, which may increase the intensity of UV radiation due to the depletion of the ozone layer. This study aims to design an Internet of Things (IoT)-based UV index monitoring system, equipped with a dual- axis solar tracker to optimize UV index measurements. The system utilizes the ESP32 microcontroller as the main processing unit, the UVM-30A sensor to detect UV radiation, and the DS3231 Real Time Clock (RTC) module for time synchronization. UV index data is displayed in real-time through a Liquid Crystal Display (LCD) screen and the Blynk platform for easy remote access. Test results show that the system performs well, with a low relative error compared to UV index data from the reference site uvindex.app. This system provides an innovative solution for efficient and real-time UV index monitoring, which can increase public awareness about the dangers of UV radiation.
subject: Articles
  topic: section
subject: UV Index Monitoring
  topic: keywords
subject: Internet of Things (IoT)
  topic: keywords
subject: Dual-Axis Solar Tracker
  topic: keywords
subject: UV Index Monitoring
  topic: subjects
subject: Internet of Things (IoT)
  topic: subjects
subject: Dual-Axis Solar Tracker
  topic: subjects
creator: Dwi Agustian
creator: Ilham Muthahhari
creator: Valiant Yuvi Syahreza
creator: Anton Widodo
creator: Muchamad Rizqy Nugraha
creator: Edward Trihadi
rights: Copyright (c) 2025 Journal of Computation Physics and Earth Science (JoCPES) https://creativecommons.org/licenses/by-sa/4.0
date: 2025-03-08
publication date: 2025-03-08
doi: 10.63581/JoCPES.v5i1.01

Redesign of User Interface and Experience with Brand Identity Enhancement for the STMKG Website through WordPress Implementation
type: item
description: This paper presents the redesign, rebuild, and rebranding of the official website of Sekolah Tinggi Meteorologi Klimatologi dan Geofisika (STMKG) using the WordPress content management system. The project aimed to modernize the institution’s digital presence by enhancing layout consistency, mobile responsiveness, and brand identity. A content audit was conducted to reorganize fragmented navigation and outdated information. The entire development was executed directly within WordPress using Elementor, enabling rapid prototyping without external wireframing tools. Key improvements include structured program sections, a modern news layout, and a standardized footer, all designed in line with STMKG’s visual identity. Performance optimization—though not the primary focus—involved basic caching, compression, and lazy loading, with assessments via GTmetrix indicating areas for future improvement. The project, completed by a sixth-semester cadet, highlights the feasibility of student-led web transformation initiatives within academic institutions. Positive stakeholder feedback confirmed improvements in usability, clarity, and institutional credibility. This work demonstrates the practical application of accessible web technologies to deliver scalable, branded, and user-centered digital solutions in educational settings.
subject: Articles
  topic: section
subject: WordPress
  topic: keywords
subject: Website Redesign
  topic: keywords
subject: Institutional Branding
  topic: keywords
subject: UI/UX
  topic: keywords
subject: Elementor
  topic: keywords
subject: Digital Transformation
  topic: keywords
subject: Academic Web Development
  topic: keywords
subject: WordPress
  topic: subjects
subject: Website Redesign
  topic: subjects
subject: Institutional Branding
  topic: subjects
subject: UI/UX
  topic: subjects
subject: Elementor
  topic: subjects
subject: Digital Transformation
  topic: subjects
subject: Academic Web Development
  topic: subjects
creator: Tonny Wahyu Aji
creator: Ahmad Meijlan Yasir
creator: Nardi
creator: Sorfian
creator: Agustina Rachmawardani
creator: Marianus Carol Jehadun
creator: Adi Widiatmoko Wastumirad
creator: Edward Trihadi
rights: Copyright (c) 2025 Journal of Computation Physics and Earth Science (JoCPES) https://creativecommons.org/licenses/by-sa/4.0
date: 2025-03-27
publication date: 2025-03-27
doi: 10.63581/JoCPES.v5i1.15

Low Cost Sensor Technology Innovation in Air Quality Monitoring: A Systematic Literature Review on Opportunities for Public Budget Efficiency
type: item
description: Air quality monitoring is critical in supporting environmental and public health policies, but conventional station-based methods still have limitations in cost and area coverage. Alternatively, low-cost sensor technologies based on the Internet of Things (IoT) and Artificial Intelligence (AI) offer solutions that are more economical, flexible, and capable of real-time monitoring. However, low-cost sensors face the challenge of lower accuracy compared to conventional sensors and require regular calibration for reliable measurement results. This paper analyzes the development of low-cost sensor technology, its effectiveness compared to conventional technology, and its impact on public budget efficiency. The results show that the integration of AI and IoT can improve the accuracy of low-cost sensors, while the citizen science model has the potential to expand monitoring coverage by involving the public in data collection. With the right strategy, low-cost sensors can be an inclusive and sustainable solution to support more cost-effective and data-driven environmental policies.
subject: Articles
  topic: section
subject: Low-cost Sensors
  topic: keywords
subject: Air Quality
  topic: keywords
subject: IoT
  topic: keywords
subject: Artificial Intelligence
  topic: keywords
subject: Budget Efficience
  topic: keywords
subject: Environmental Policy
  topic: keywords
subject: Citizen Science
  topic: keywords
subject: Low-cost Sensors
  topic: subjects
subject: Air Quality
  topic: subjects
subject: IoT
  topic: subjects
subject: Artificial Intelligence
  topic: subjects
subject: Budget Efficiency
  topic: subjects
subject: Environmental Policy
  topic: subjects
subject: Citizen Science
  topic: subjects
creator: Thalia Salsabila
creator: Diah Putri Aravena Mokoginta
creator: George Jonathan Imanuel Bonde
creator: Ahmad Aqila Nubala
creator: Galung Wisnu Pambela
creator: Zulfitrah Mokoginta
creator: Syahrajad Al Syarqiyah
rights: Copyright (c) 2025 Journal of Computation Physics and Earth Science (JoCPES) https://creativecommons.org/licenses/by-sa/4.0
date: 2025-03-25
publication date: 2025-03-25
doi: 10.63581/JoCPES.v5i1.14

Impact of Urban Heat Island on Shifting Weather Patterns in Sidoarjo and Malang: BMKG Daily Data-Based Analysis
type: item
description: The Urban Heat Island (UHI) phenomenon is an increase in surface temperature in urban areas compared to the surrounding, more rural areas. This phenomenon can affect shifts in weather patterns, including an increase in average temperature, changes in rainfall patterns, and an increase in extreme weather events. This study aims to analyse the impact of UHI on shifting weather patterns in Indonesia using BMKG daily climate data. The method used includes a quantitative approach withtime series analysis to understand the trend of changes in air temperature and rainfall in Sidoarjo and Malang. The results showed a correlation between UHI intensity and changes in weather patterns,where areas with high UHI intensity experienced a significant increase in temperature and changes in rainfall patterns. This research is expected to provide insights for more sustainable urban planning in the face of UHI impacts in Indonesia.
subject: Articles
  topic: section
subject: Urban Heat Island
  topic: keywords
subject: Temperature
  topic: keywords
subject: Weather
  topic: keywords
subject: Sustainable Urban
  topic: keywords
subject: Urban Heat Island
  topic: subjects
subject: Temperature
  topic: subjects
subject: Weather
  topic: subjects
subject: Sustainable Urban
  topic: subjects
creator: Assajdah Abdullah
creator: Muhammad Zaki Albukhori
creator: Krisna Dewi
creator: Wyra Steven Hutagalung
creator: Muhammad Yazid Ilmany
creator: Syahrajad Al Syarqiyah
creator: Zulfitrah Mokoginta
rights: Copyright (c) 2025 Journal of Computation Physics and Earth Science (JoCPES) https://creativecommons.org/licenses/by-sa/4.0
date: 2025-03-23
publication date: 2025-03-23
doi: 10.63581/JoCPES.v5i1.13

Design and build an IoT system for temperature and humidity observation using DHT 22 with ThingSpeak
type: item
description: In a study conducted in recent years, it was found that atmospheric conditions such as temperature and humidity values greatly affect the climatic conditions of a region. This real-time temperature and humidity monitoring system is very useful and can be used in various regions. By using IoT, monitoring can be done in real-time. Temperature and humidity can use DHT 22 linked to ESP 32 and the data is stored and displayed in ThingSpeak. The trial of the monitoring tool was carried out for more than 31 hours and obtained 6920 data. At the time of the experiment the monitoring system worked well. At the time of observation for temperature the maximum value is 32.4 ̊C, the minimum value is 28.1 ̊C, the mean is 29.75621387283237 ̊C, and the standard deviation is 3.587911994653761 and for humidity the maximum value is 81.4%, the minimum value is 63.6%, the mean is 72.6355404624 and the standard deviation is 3.5879112. From the experiment, it was also found that the temperature value was inversely proportional to the humidity and when it was going to rain the temperature value went up while the humidity went down and when it rained the temperature went up while the temperature went down.
subject: Articles
  topic: section
subject: Temperature
  topic: keywords
subject: Humidity
  topic: keywords
subject: IoT
  topic: keywords
subject: DHT22
  topic: keywords
subject: Temperature
  topic: subjects
subject: Humidity
  topic: subjects
subject: IoT
  topic: subjects
subject: DHT22
  topic: subjects
creator: Fachrul Rizky Syaputra
rights: Copyright (c) 2025 Journal of Computation Physics and Earth Science (JoCPES) https://creativecommons.org/licenses/by-sa/4.0
date: 2025-03-22
publication date: 2025-03-22
doi: 10.63581/JoCPES.v5i1.12

Implementation of Web-based Realtime Monitoring System Using YOLOv8 for Green Box Detection and Automatic Capture in Navigation Missions at the Indonesian Boat Contest
type: item
description: This research presents the design and development of a real-time object detection and monitoring system specifically aimed at identifying green box objects using the YOLOv8 model. The system integrates OpenCV for frame-by-frame video processing, MySQL for image storage as Binary Large Objects (BLOBs), and a Flask-based web interface for real-time visualization. Green box objects detected with a confidence score above 0.7 are cropped and stored in the database. A dynamic web interface, updated every 2 seconds using AJAX, enables real-time monitoring and allows users to download the latest detected image for further analysis. Experimental results demonstrate that the YOLOv8 model achieves high detection accuracy, as measured by precision, recall, and mean average precision (mAP). The proposed system effectively combines object detection, data storage, and web-based visualization to provide a robust and scalable solution for real-time monitoring. Tests conducted under real-world conditions confirm the system's efficiency and reliability. Future work may involve hardware acceleration via edge computing, support for multi-object detection, and integration of advanced tracking algorithms to broaden its applicability in autonomous systems and industrial automation.
subject: Articles
  topic: section
subject: Real-time Monitoring
  topic: keywords
subject: Web-based Visualization
  topic: keywords
subject: YOLOv8
  topic: keywords
subject: Flask
  topic: keywords
subject: MySQL
  topic: keywords
subject: Object Detection
  topic: keywords
subject: OpenCV
  topic: keywords
subject: Real-time Monitoring
  topic: subjects
subject: Web-based Visualization
  topic: subjects
subject: YOLOv8
  topic: subjects
subject: Flask
  topic: subjects
subject: MySQL
  topic: subjects
subject: Object Detection
  topic: subjects
subject: OpenCV
  topic: subjects
creator: Yobel Eliezer Mahardika
creator: Tonny Wahyu Aji
creator: Dimas Aditya Wiranata
creator: Antonius Kukuh Prasetyaji
rights: Copyright (c) 2025 Journal of Computation Physics and Earth Science (JoCPES) https://creativecommons.org/licenses/by-sa/4.0
date: 2025-03-21
publication date: 2025-03-21
doi: 10.63581/JoCPES.v5i1.11

Flood Early Warning Monitoring System Using KNN Methods In Bangkalan District
type: item
description: Bangkalan Regency faces serious challenges due to flood disasters that periodically threaten the safety and welfare of the community. The flood phenomenon in this area is caused by a complex combination of geographic and climate factors, including increased extreme rainfall and the dynamics of sea level rise. Annual floods not only cause infrastructure damage but also threaten the livelihoods and lives of residents living around the river flow. This study aims to develop an innovative flood early warning system using the K-Nearest Neighbors (KNN) method to predict potential disasters before floods occur. By using water flow data analysis and machine learning algorithms, this system is designed to provide accurate and timely early estimates. The main advantage of this study is its ability to proactively mitigate disaster risks using modern computer technology. The study produced a prototype of a flood detection and simulation system that can help local governments, related agencies, and the Bangkalan community in taking preventive and mitigation actions at an earlier stage. Therefore, this system is expected to make a significant contribution to reducing the impact of disasters and protecting the lives of Bangkalan Regency residents.
subject: Articles
  topic: section
subject: Flood
  topic: keywords
subject: Early Warning System
  topic: keywords
subject: Prediction
  topic: keywords
subject: KKN
  topic: keywords
subject: Algorithm
  topic: keywords
subject: Flood
  topic: subjects
subject: Early Warning System
  topic: subjects
subject: Prediction
  topic: subjects
subject: Algorithm
  topic: subjects
creator: Iqbal Fariansyah Ridwan
rights: Copyright (c) 2025 Journal of Computation Physics and Earth Science (JoCPES) https://creativecommons.org/licenses/by-sa/4.0
date: 2025-03-19
publication date: 2025-03-19
doi: 10.63581/JoCPES.v5i1.10

Comparison of Random Forest and LSTM Methods for Temperature Prediction
type: item
description: This study compares the performance of Long Short-Term Memory (LSTM) and Random Forest (RF) models in predicting temperature data from Tanjung Priok, Indonesia, using evaluation metrics such as RMSE, MAE, and R² Score. The LSTM model demonstrated its ability to capture temporal dependencies and temperature trends, achieving an R² score of 0.4493 and an MAE of 0.5863. In contrast, the RF model performed better in minimizing prediction errors, with a lower RMSE of 0.6498 and an R² score of 0.4066. While the LSTM model excelled in explaining variance in the temperature data, the RF model was more effective in stable periods, exhibiting lower prediction errors. The results highlight that both models have distinct advantages, with LSTM better suited for capturing long-term temperature trends and RF performing well during periods of stability. Future research could explore hybrid models or further optimization of these techniques to improve prediction accuracy, particularly for dynamic and extreme temperature fluctuations.
subject: Articles
  topic: section
subject: Long Short-Term Memory (LSTM)
  topic: keywords
subject: Random Forest (RF)
  topic: keywords
subject: Temperature Prediction
  topic: keywords
subject: RMSE
  topic: keywords
subject: MAE
  topic: keywords
subject: R2 Score
  topic: keywords
subject: Machine Learning
  topic: keywords
subject: Time Series Forecasting
  topic: keywords
subject: Temperature Trends
  topic: keywords
subject: Model Evaluation
  topic: keywords
subject: Long Short-Term Memory (LSTM)
  topic: subjects
subject: Random Forest (RF)
  topic: subjects
subject: Temperature Prediction
  topic: subjects
subject: RMSE
  topic: subjects
subject: MAE
  topic: subjects
subject: R2 Score
  topic: subjects
subject: Machine Learning
  topic: subjects
subject: Time Series Forecasting
  topic: subjects
subject: Temperature Trends
  topic: subjects
subject: Model Evaluation
  topic: subjects
creator: Muhammad Asyril Ar Rosyad
creator: Aviv Maghridlo
rights: Copyright (c) 2025 Journal of Computation Physics and Earth Science (JoCPES) https://creativecommons.org/licenses/by-sa/4.0
date: 2025-03-18
publication date: 2025-03-18
doi: 10.63581/JoCPES.v5i1.09

Residual Network Architecture Model for Image Weather Classification
type: item
description: Weather classification plays an important role in many fields, including agriculture, transportation, and meteorology. Traditional methods for weather recognition are usually based on human observation or sensor networks, which are prone to errors and quite costly. To overcome the limitation, this research implements the Convolutional Neural Network method with a Residual Network model architecture for image-based weather classification. Using a dataset of 1,500 images categorized into five weather conditions cloudy, foggy, rainy, sunny and sunrise. The model training accuracy reached a level of 92%, while the validation accuracy reached a level of 94% and resulted in a testing accuracy of 86.7%. The model training accuracy was high for sunny and sunrise conditions. Accuracy was lower in rainy and foggy weather conditions. This research shows that the ResNet model architecture can provide a low-cost, efficient, and high-accuracy solution for weather classification.Weather classification plays an important role in many fields, including agriculture, transportation, and meteorology. Traditional methods for weather recognition are usually based on human observation or sensor networks, which are prone to errors and quite costly. To overcome the limitation, this research implements the Convolutional Neural Network method with a Residual Network model architecture for image-based weather classification. Using a dataset of 1,500 images categorized into five weather conditions cloudy, foggy, rainy, sunny and sunrise. The model training accuracy reached a level of 92%, while the validation accuracy reached a level of 94% and resulted in a testing accuracy of 86.7%. The model training accuracy was high for sunny and sunrise conditions. Accuracy was lower in rainy and foggy weather conditions. This research shows that the ResNet model architecture can provide a low-cost, efficient, and high-accuracy solution for weather classification.
subject: Articles
  topic: section
subject: Convolutional Neural Network
  topic: keywords
subject: Residual Network
  topic: keywords
subject: Weater Classification
  topic: keywords
subject: Convolutional Neural Network
  topic: subjects
subject: Residual Network
  topic: subjects
subject: Weather Classification
  topic: subjects
creator: Fauzi Hasbi Saputra
rights: Copyright (c) 2025 Journal of Computation Physics and Earth Science (JoCPES) https://creativecommons.org/licenses/by-sa/4.0
date: 2025-03-16
publication date: 2025-03-16
doi: 10.63581/JoCPES.v5i1.08

Rainfall Trend Analysis in Tangerang City Using Linear Regression and Random Forest
type: item
description: Rainfall is an important element in the hydrological cycle that has a significant impact on the environment and human life, especially in tropical areas such as Tangerang City. This study aims to analyze annual and monthly rainfall trends and compare the performance of Linear Regression and Random Forest methods in predicting daily rainfall. Daily rainfall data from the Soekarno-Hatta Meteorological Station during the period 2019–2024 are used as model input. The results show that Random Forest has superior performance in capturing complex and extreme rainfall fluctuation patterns, with lower Mean Squared Error (MSE) and higher R-squared (R²) compared to Linear Regression. Linear Regression is only able to predict linear trends simply but is less accurate in handling non-linear variations. This study provides practical contributions to flood risk mitigation, water resource management, and urban infrastructure planning. The development of more accurate prediction models, such as Random Forest, is an important step in supporting climate change adaptation and environmental management in urban areas. Further research is recommended to include additional atmospheric variables and more complex validation techniques to improve prediction accuracy.
subject: Articles
  topic: section
subject: Rainfall
  topic: keywords
subject: Linear Regression
  topic: keywords
subject: Random Forest
  topic: keywords
subject: Machine Learning
  topic: keywords
subject: Rainfall
  topic: subjects
subject: Linear Regression
  topic: subjects
subject: Random Frest
  topic: subjects
subject: Machine Learning
  topic: subjects
creator: Rizaldi Wisnu Wardana
rights: Copyright (c) 2025 Journal of Computation Physics and Earth Science (JoCPES) https://creativecommons.org/licenses/by-sa/4.0
date: 2025-03-13
publication date: 2025-03-13
doi: 10.63581/JoCPES.v5i1.07

Machine Learning Approaches for Classifying Indian Ocean Dipole (IOD) Using Random Forest and Decision Tree Models with SST, MSLP, And Total Precipitation Data from the Waters Off West Sumatra
type: item
description: This research investigates the utilization of machine learning methodologies, particularly Random Forest and Decision Tree algorithms, to categorize Indian Ocean Dipole (IOD) occurrences by employing Sea Surface Temperature (SST), Mean Sea Level Pressure (MSLP), and total precipitation datasets derived from the maritime region adjacent to West Sumatra. The study leverages data amassed from 2020 to 2024, concentrating on diverse climatic scenarios linked to IOD. The efficacy of both algorithms is assessed using evaluative criteria such as accuracy, precision, and recall. The findings reveal that the Random Forest algorithm surpasses the Decision Tree algorithm, attaining an accuracy rate exceeding 85%, with SST recognized as the predominant predictor. These results underscore the promise of machine learning techniques in advancing the comprehension of IOD and its ramifications on regional meteorological trends, thereby facilitating enhanced climate forecasting models and guiding decision-making frameworks for climate adaptation.
subject: Articles
  topic: section
subject: Indian Ocean Dipole
  topic: keywords
subject: Machine Learning
  topic: keywords
subject: Random Forest
  topic: keywords
subject: Decision Tree
  topic: keywords
subject: Sea Surface Temperature
  topic: keywords
subject: Mean Sea Level Pressure
  topic: keywords
subject: Precipitation Data
  topic: keywords
subject: Indian Ocean Dipole
  topic: subjects
subject: Machine Learning
  topic: subjects
subject: Random Forest
  topic: subjects
subject: Decision Tree
  topic: subjects
subject: Sea Surface Temperature
  topic: subjects
subject: Mean Sea Level Pressure
  topic: subjects
subject: Precipitation Data
  topic: subjects
creator: Muhammad Arya Bintang Pratama
rights: Copyright (c) 2025 Journal of Computation Physics and Earth Science (JoCPES) https://creativecommons.org/licenses/by-sa/4.0
date: 2025-03-12
publication date: 2025-03-12
doi: 10.63581/JoCPES.v5i1.06

Automated Analysis of Radiosonde Temperature Data Using Python: A Study on Data Homogenization and Climate Trends Observed at Sta. Met. Kelas I Sultan Iskandar Muda - Banda Aceh
type: item
description: Radiosonde temperature data serve as a cornerstone for understanding atmospheric dynamics and investigating long-term climate trends. Despite their significance, these datasets are often hindered by challenges such as instrumental biases, shifts in observational protocols, and limited vertical resolution, which can obscure critical atmospheric patterns. This study introduces a Python-based automated framework designed to streamline radiosonde data analysis, emphasizing homogenization, vertical resolution enhancement, and advanced visualization techniques. By utilizing robust libraries such as pandas, matplotlib, and seaborn, the framework effectively mitigates inconsistencies and promotes reproducibility. The findings highlight significant improvements in data quality, allowing for more accurate identification of temperature trends across the troposphere and stratosphere. Additionally, this approach reduces analytical biases and enhances the resolution of key atmospheric processes. The proposed framework contributes a valuable methodology for climate researchers, offering new opportunities to advance studies on atmospheric behavior and climate change dynamics.
subject: Articles
  topic: section
subject: Radiosonde
  topic: keywords
subject: Atmospheric Dynamics
  topic: keywords
subject: Climate Trends
  topic: keywords
subject: Python Framework
  topic: keywords
subject: Data Homogenization
  topic: keywords
subject: Vertical Resolution
  topic: keywords
subject: Troposphere
  topic: keywords
subject: Stratosphere
  topic: keywords
subject: Data Analysis
  topic: keywords
subject: Climate Scince
  topic: keywords
subject: Radiosonde
  topic: subjects
subject: Atmospheric Dynamics
  topic: subjects
subject: Climate Trends
  topic: subjects
subject: Python Framework
  topic: subjects
subject: Data Homogenization
  topic: subjects
subject: Vertical Resolution
  topic: subjects
subject: Troposphere
  topic: subjects
subject: Stratosphere
  topic: subjects
subject: Data Analysis
  topic: subjects
subject: Climate Science
  topic: subjects
creator: Muhammad Faiz
creator: Hafifuddin Hasfa Marpaung
rights: Copyright (c) 2025 Journal of Computation Physics and Earth Science (JoCPES) https://creativecommons.org/licenses/by-sa/4.0
date: 2025-03-11
publication date: 2025-03-11
doi: 10.63581/JoCPES.v5i1.05

PM2.5 Concentration Prediction Model in Jakarta Area Using Random Forest Algorithm
type: item
description: This study predicts PM2.5 concentrations in Jakarta using the Random Forest algorithm with historical air quality data from 2015 to 2024. Hyperparameter tuning was performed to optimize model performance, focusing on parameters such as n_estimators, max_depth, and min_samples_split. The model achieved a Mean Absolute Error (MAE) of 14.44, a Root Mean Square Error (RMSE) of 18.75, and an R² Score of 0.61. While the model captured general PM2.5 fluctuation patterns, deviations at certain points indicate room for improvement. Descriptive analysis showed an average PM2.5 concentration of 94.46 µg/m³, with peaks up to 209 µg/m³, exceeding healthy air quality thresholds. The model can be integrated into real-time monitoring systems and support data-driven policies. Future work could incorporate meteorological variables and evaluate longer-term trends to enhance accuracy.
subject: Articles
  topic: section
subject: PM2.5 Prediction
  topic: keywords
subject: Random Forest
  topic: keywords
subject: Air Quality
  topic: keywords
subject: Machine Learning
  topic: keywords
subject: Jakarta
  topic: keywords
subject: Hyperparameter Tuning
  topic: keywords
subject: Environmental Tuning
  topic: keywords
subject: Environmental Monitoring
  topic: keywords
subject: Data-Driven Policy
  topic: keywords
subject: PM2.5 Prediction
  topic: subjects
subject: Random Forest
  topic: subjects
subject: Air Quality
  topic: subjects
subject: Machine Learning
  topic: subjects
subject: Jakarta
  topic: subjects
subject: Hyperparameter Tuning
  topic: subjects
subject: Data-Driven Policy
  topic: subjects
creator: Muhammad Naufal Afif Al Arsy
creator: Ahmad Meijlan Yasir
rights: Copyright (c) 2025 Journal of Computation Physics and Earth Science (JoCPES) https://creativecommons.org/licenses/by-sa/4.0
date: 2025-03-10
publication date: 2025-03-10
doi: 10.63581/JoCPES.v5i1.04

Evaluation of the XGBoost Model for Rainfall Prediction and Classification Using BMKG Data and OpenWeather API
type: item
description: Indonesia, situated between two continents and two oceans, experiences significant climate variability, with rainfall patterns shaped by geographical and topographical factors, as well as phenomena like the El Niño Southern Oscillation (ENSO). Accurate rainfall forecasting plays a critical role in disaster mitigation, agricultural planning, and water resource management. This study focuses on developing a rainfall prediction and classification model using the Extreme Gradient Boosting (XGBoost) algorithm. The model leverages historical rainfall data from the Indonesian Meteorological, Climatological, and Geophysical Agency (BMKG) and real-time data from the OpenWeather API. The output includes rainfall trend graphs and classification of rainfall intensity into categories such as light, moderate, or heavy. Model performance is assessed through metrics like accuracy, precision, RMS (Root Mean Square), and RMSE (Root Mean Square Error). This research highlights the integration of historical and real-time data for weather forecasting and demonstrates the application of advanced machine learning techniques like XGBoost to build robust and precise prediction models. The findings are expected to offer practical insights for disaster risk reduction, agricultural strategy planning, and effective water resource management.
subject: Articles
  topic: section
subject: Rainfall Prediction
  topic: keywords
subject: XGBoost
  topic: keywords
subject: Rainfall Classification
  topic: keywords
subject: OpenWeather API
  topic: keywords
subject: BMKG
  topic: keywords
subject: Rainfall Prediction
  topic: subjects
subject: XGBoost
  topic: subjects
subject: Rainfall Classification
  topic: subjects
subject: OpenWeather API
  topic: subjects
subject: BMKG
  topic: subjects
creator: Muhammad Saori Isjayan Syah
creator: Nardi
creator: Agustina Rachmawardani
rights: Copyright (c) 2025 Journal of Computation Physics and Earth Science (JoCPES) https://creativecommons.org/licenses/by-sa/4.0
date: 2025-03-09
publication date: 2025-03-09
doi: 10.63581/JoCPES.v5i1.03

Comparative Analysis of Linear Regression Models and XGBoost to Assess the Impact of ENSO on Rainfall in Ternate City in 2023
type: item
description: The purpose of this study is to evaluate how well two prediction models—linear regression and XGBoost—perform in assessing how ENSO (El Niño-Southern Oscillation) affects rainfall in Ternate City in 2023. The Meteorology, Climatology, and Geophysics Agency (BMKG) provided monthly rainfall data, while the Bureau of Meteorology (BOM) in Australia provided ENSO index data. Performance indicators such Pearson correlation analysis, the coefficient of determination (R-squared), and mean squared error (MSE) were used in the evaluation. According to the findings, the two models perform differently when it comes to capturing the pattern of the link between rainfall and ENSO; XGBoost is more adaptable but has a tendency to overfit on small amounts of data, whereas linear regression obtains a better R-squared value.
subject: Articles
  topic: section
subject: ENSO
  topic: keywords
subject: Rainfall
  topic: keywords
subject: Linear Regression
  topic: keywords
subject: XGBoost
  topic: keywords
subject: Machine Learning
  topic: keywords
subject: ENSO
  topic: subjects
subject: Rainfall
  topic: subjects
subject: Linear Regression
  topic: subjects
subject: XGBoost
  topic: subjects
subject: Machine Learning
  topic: subjects
creator: Firman Almaliky Gapri Amra
creator: Anton Widodo
creator: Muchamad Rizqy Nugraha
rights: Copyright (c) 2025 Journal of Computation Physics and Earth Science (JoCPES) https://creativecommons.org/licenses/by-sa/4.0
date: 2025-03-08
publication date: 2025-03-08
doi: 10.63581/JoCPES.v5i1.02

Prediction of Aquaculture Quality Using Long Short-Term Memory in the Sunda Strait Region
type: item
description: Aquaculture plays a vital role in addressing global seafood demands and ensuring food security, particularly in tropical regions like the Sunda Strait, Indonesia. However, aquaculture success depends on key environmental parameters, including sea surface temperature (SST), salinity, ocean heat content, and thermocline depth, which exhibit complex spatiotemporal variability. This study applies a Long Short-Term Memory (LSTM) model to predict aquaculture suitability by analyzing five critical oceanographic parameters: depth of the 26°C isotherm (so26chgt), ocean heat content (sohtc300), mixed layer depth (somxl010), sea surface salinity (sosaline), and sea surface temperature (sosstsst). Using the ORAS5 dataset spanning January 2015 to March 2025, the model achieved high accuracy, with R² scores exceeding 0.89 for all parameters. Spatial prediction maps for November 2024 to March 2025 were generated, highlighting regions with optimal environmental conditions for aquaculture. Results indicate that SST and salinity are the most influential factors affecting aquaculture quality, with favorable conditions predominantly observed from December to May. The findings underscore the potential of deep learning models in supporting sustainable aquaculture management through accurate environmental forecasting.
subject: Articles
  topic: section
subject: Aquaculture Suitability
  topic: keywords
subject: Long Short-Term Memory (LSTM)
  topic: keywords
subject: Environmental Parameters
  topic: keywords
subject: Sunda Strait
  topic: keywords
subject: Salinity
  topic: keywords
subject: Ocean Climate Heat
  topic: keywords
subject: Climate Data Store (CDS)
  topic: keywords
subject: Machine Learning
  topic: keywords
subject: Sustainable Aquaculture
  topic: keywords
subject: Aquaculture Suitability
  topic: subjects
subject: Long Short-Term Memory (LSTM)
  topic: subjects
subject: Environmental Parameters
  topic: subjects
subject: Machine Learning for Oceanographic Prediction
  topic: subjects
subject: Sea Surface Temperature (SST) & Salinity
  topic: subjects
creator: John Pieter Sirfefa Aes
rights: Copyright (c) 2022 Journal of Computation Physics and Earth Science https://creativecommons.org/licenses/by/4.0
date: 2022-03-26
publication date: 2022-03-26
doi: 10.53842/fpfhej39

Analysis of the Use of Telegram Bot for Earthquake Information Dissemination Systems and Weather Forecasting
type: item
description: As one method deployment information forecast weather, BMKG has have mobile application based on Android and iOS with the name @infoBMKG since in 2016. Felt urgent for designing system with a deployment model information-based request to stay awake along with growth user application service message instant. Webhook method for Telegram Bot selected because especially effective _ for Bot creators in need guide for finish Settings base Telegram bot creation. Because of the server hosted and should use https then used Webhooks method. So, Telegram bots can respond message with fast. The success rate of Telegram Bot aimed at spreading information varies depending on the menu. A 100% success rate and an average response time of 2.54 seconds were recorded for the menu forecast weather, 100 % and an average response time of 2.76 seconds were recorded for the airport weather information menu, and 100 % and an average response time of 7.28 seconds were recorded for the image information menu satellite.
subject: Articles
  topic: section
subject: Weather forecast, Earthquake, Dissemination, Mobile application, Telegram bot.
  topic: keywords
subject: -
  topic: subjects
creator: Muhammad Bayu Putra Primary
creator: Nardi
rights: Copyright (c) 2021 Journal of Computation Physics and Earth Science https://creativecommons.org/licenses/by/4.0
date: 2021-10-01
publication date: 2021-10-01
doi: 10.53842/jgznre15