Journal of Computation Physics and Earth Science (JoCPES)
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.
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Design and Implementation of a Web-Based RFID-Enabled Library Visitor Attendance and Management System: A Case Study at STMKG
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.
Copyright (c) 2026 Journal of Computation Physics and Earth Science (JoCPES) https://creativecommons.org/licenses/by-sa/4.0
Optimization of Electronic Control for a Monohull Leisure Boat Using Meteorological Data to Enhance Stability and Safety in Coastal Environments
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.
Design and Implementation of a React.js-Based Monitoring Dashboard for Tangerang Weather Radar and Server Room Air Conditioning Control
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.
Design and Implementation of an IoT-Based 3-Phase Electricity Usage Monitoring System: Case Study at Geophysics Station Class I Sleman, Yogyakarta
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.
Meteorological Analysis of Strong Wind-Producing Clouds: A Case Study of Klaten, 18 November 2024
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.
Estimating Aftershock Termination Time Using Statistical Decay Models: A Case Study of the March 22, 2024 Bawean Earthquake
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.
Revealing the Subsurface Geometry of the 2023 Sumedang Earthquake Sequence Using Double-Difference Relocation and Cross-Section Analysis
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.
Fault Mapping of the Central Aceh Segment of the Sumatran Fault as an Extension of the Great Sumatran Fault (GSF) Using Gravity Analysis
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.
Gravity First Horizontal Derivative (FHD) Second Vertical Derivative (SVD) Aceh Tengah Segment
Gravity analysis, Fault mapping, Sumatran Fault, Central Aceh Segment, Tectonics, Geophysics
Estimation of Jayapura 2023 Aftershock Decay Time Using Python-Based Secant Algorithm
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.
Subsurface Structural Identification and Seismicity Correlation in West Java Using EMAG2 Geomagnetic Data
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.
Subsurface Structure of Palu-Koro Fault Zone Using TOPEX Satellite Gravity Data and Regional-Residual Anomaly Separation
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.
Deterministic Seismic Hazard Analysis (DSHA) and Peak Ground Acceleration Mapping in West Java (2018–2024)
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.
Solar Radiation Computation from Satellite Weather Data in Batam Using Linear Regression, Random Forest, and Decision Tree
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.
Solar Radiation, Satellite Weather Data, Machine Learning, Linear Regression, Random Forest, Decision Tree
Design and Development of an IoT-Based UV Meter and Dual-Axis Solar Tracker for Real-Time UV Index Monitoring Optimization
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.
Copyright (c) 2025 Journal of Computation Physics and Earth Science (JoCPES) https://creativecommons.org/licenses/by-sa/4.0
Redesign of User Interface and Experience with Brand Identity Enhancement for the STMKG Website through WordPress Implementation
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.
Low Cost Sensor Technology Innovation in Air Quality Monitoring: A Systematic Literature Review on Opportunities for Public Budget Efficiency
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.
Impact of Urban Heat Island on Shifting Weather Patterns in Sidoarjo and Malang: BMKG Daily Data-Based Analysis
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.
Design and build an IoT system for temperature and humidity observation using DHT 22 with ThingSpeak
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.
Implementation of Web-based Realtime Monitoring System Using YOLOv8 for Green Box Detection and Automatic Capture in Navigation Missions at the Indonesian Boat Contest
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.
Flood Early Warning Monitoring System Using KNN Methods In Bangkalan District
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.
Comparison of Random Forest and LSTM Methods for Temperature Prediction
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.
Residual Network Architecture Model for Image 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.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.
Rainfall Trend Analysis in Tangerang City Using Linear Regression and Random Forest
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.
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
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.
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
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.
PM2.5 Concentration Prediction Model in Jakarta Area Using Random Forest Algorithm
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.
Evaluation of the XGBoost Model for Rainfall Prediction and Classification Using BMKG Data and OpenWeather API
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.
Comparative Analysis of Linear Regression Models and XGBoost to Assess the Impact of ENSO on Rainfall in Ternate City in 2023
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.
Prediction of Aquaculture Quality Using Long Short-Term Memory in the Sunda Strait Region
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.
Machine Learning for Oceanographic Prediction
Sea Surface Temperature (SST) & Salinity
Copyright (c) 2022 Journal of Computation Physics and Earth Science https://creativecommons.org/licenses/by/4.0
Analysis of the Use of Telegram Bot for Earthquake Information Dissemination Systems and Weather Forecasting
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.
Weather forecast, Earthquake, Dissemination, Mobile application, Telegram bot.
Copyright (c) 2021 Journal of Computation Physics and Earth Science https://creativecommons.org/licenses/by/4.0