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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Copyright (c) 2021 Journal of Computation Physics and Earth Science (JoCPES) https://creativecommons.org/licenses/by-sa/4.0
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 A content audit was conducted to reorganize fragmented navigation and outdated information.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
A Review: Information Technology-based Climate Data Dissemination
Changes in climate indicators can cause extreme weather and can trigger disasters, such as floods and droughts and even crop failure. it is difficult to predict because farmers and local governments do not understand the importance of climate information, the solution to the problem is to disseminate and disseminate information, but requires an information system that is also inseparable from software, IoT-based applications, and others. With the method used, namely by classifying the climate based on rainfall. In classifying the climate, the oldeman and schmidt-ferguson classifications are used. Then the dataset is formed to calculate the degree or probability of the rainfall category and the Data Normality test. The test results show that the classification of rainfall categories with light, normal, and heavy categories is 79.5%, 40.9%, and 86.4% respectively. While the precision is 96.4%, 42.6%, and 83.3% respectively. Therefore, in making applications as a medium for disseminating information, it is necessary to understand the process of seasonal occurrence, and how to turn these data into information that can be utilized by the wider community.
A Review: Prototype Gyro-Stabilizer for Buoys
With buoys, tsunami waves caused by underwater earthquakes can be detected. The buoy will monitor and record changes in the sea level in the ocean. Indonesia has already installed several tsunami buoys. Nine buoys were made by Indonesia, two buoys made by America, one buoy from Malaysia, and the other nine buoys donated by Germany. These buoys are placed at all points in the Indonesian seas, such as in the Sumatra, Java, Flores, Maluku, and Banda Seas, so they can assist the Meteorology, Climatology and Geophysics Agency (BMKG) in providing tsunami early warnings. But unfortunately, the tsunami buoy network was not functioning from 2012 to 2018, because it was damaged and lost. Therefore, a tool is needed to maintain the presence, function, and performance of the buoy system so that it can operate properly. A Gyro-Stabilizer device can maintain the stability of the buoy. The Gyro-Stabilizer prototype is made of a simple circuit: a DC motor with a speed of around 18000 rpm and a mechanical gyroscope. The power supply uses the same power supply used by the buoy itself. As long as the Gyro-Stabilizer keeps rotating it will maintain the orientation of the buoy and make it stable. With good buoy stability, the durability and function of the electronic components inside can be maintained because the impact force from waves or sea waves can be reduced by using this Gyro-Stabilizer.
Tsunami Buoy, Gyro-Stabilizer, Buoy Stability, Tsunami Early Warning System, Marine Stability Technology.
Time Series Forecasting for Average Temperature with the Long Short-Term Memory Network in Deli Serdang Geophysics Station
An understanding of trends analysis, and prediction of time series of average temperature as one of parameter weather and climate data for climate variables. It is the central process in assessing the state of the climate of a region and provides an overall estimate about the variations in the climate variables. Explore weather trends using normal and local yearly average temperatures, compare and make observations. In this study, we try to analyze local and normal average temperature data in Deli Serdang geophysc Station based on observation station in situ. The main goal of this study to compare the normal temperature to local station and to predict the average temperature data in BMKG Geophysics Station, Deli Serdang, North Sumatra using Long Short-Term Memory Model (LSTM). Based on the result of normal data science of exploring temperature with local temperature correlation, we got the display of training curve, residual plot and the scatter plot are shown using these codes. Based on the temperature series data from Geophysic station, the MSE value is 0.83 and the R2 value is 0.86.
Copyright (c) 2025 Journal of Computation Physics and Earth Science https://creativecommons.org/licenses/by/4.0
The Utilization of IoT in Real-time Temperature and Humidity Monitoring Using Microcontroller: A Literature Review
Temperature and humidity must be considered in various fields such as weather, agriculture, technology and industry. The Internet of Things (IoT) can be applied in temperature and humidity monitoring systems so that the data obtained is real time. The research uses a literature review method that has the main goal of searching journals through Google Scholar. This study analyzes the use of IoT for temperature and humidity monitoring using various sensors such as DHT22, DHT11, and DS18B20 in various fields. Based on several journals from 2017 to 2024, it was selected to provide an understanding of the effectiveness and accuracy of different types of sensors in environmental monitoring. The results of the study show that IOT makes it easier to monitor environmental conditions efficiently and accurately, helping to make decisions quickly and automatically. The DHT22 sensor is effective for monitoring due to its low cost and good quality, while DS18B20 excels in accuracy. This research is useful in the development of IoT in various environmental monitoring needs.
IoT, Temperature, Humidity, DHT22, Enviromental monitoring, Sensors.
Predictive Maintenance for Automatic Weather Station (AWS) Based on Anomaly Detection Using Autoencoder: A Literature Review
Automatic Weather Station or AWS is an instrument for measuring weather parameters automatically. The results of measuring weather parameters are very useful in the fields of meteorology and climatology, such as weather prediction, aviation and climate change. Especially in Indonesia, the Meteorology, Climatology and Geophysics Agency or BMKG has main tasks and functions in this field. Currently, data with accurate results is needed to produce accurate weather and climate predictions. However, sometimes there are anomalies in the data caused by AWS damage, resulting in inaccurate data. This will have an impact on modeling results in the fields of meteorology and climatology, where the modeling results are less precise. To overcome this problem, predictive maintenance is needed to avoid data errors in AWS operations. This research aims to build predictive maintenance at an Automatic Weather Station Based on Anomaly Detection using a Machine Learning Autoencoder. The anomaly data can be detected by machine learning autoencoders for monitoring AWS performance and conditions, that methodology applied in this study for build predictive maintenance in AWS. Finally, the expectation of this research is to make accurate predictive maintenance on AWS so perhaps that can reduce maintenance costs and increase the lifespan of the instrument before it breaks.
Predictive Maintenance, Automatic Weather Station (AWS), Anomaly Detection, Autoencoder.
Literature Review: Performance Analysis of CNN, LBP, and Haar Cascade using FER-2013 for Facial Emotion Recognition
The rapid progress in artificial intelligence is transforming how humans and computers interact, with facial expressions being key markers of human emotions. Since facial expressions change dynamically during communication, they offer insights into emotional states and have attracted significant research interest. However, detecting emotions through facial recognition is challenging due to individual differences in expressions, varied lighting conditions, and different facial orientations. These challenges highlight the need for models that can effectively address these issues to improve detection accuracy. This literature review explores several commonly used algorithms for emotion detection via facial recognition, including Convolutional Neural Networks (CNN), Haar Cascade, and Local Binary Pattern (LBP), with the FER2013 dataset serving as the basis for analysis.
Facial expression recognition, Local Binary Patterns, CNN, Haar Cascade, FER-2013.
Utilization of Himawari-9 and Radiosonde Weather Satellite Data in Heavy Rainfall Analysis(Case Study: Semarang, 14 March 2024)
The heavy rainfall event that hit Semarang City on 14 March 2024 caused flooding at several points. To understand the cause of this event, a comprehensive meteorological analysis is required. This research utilises Himawari-9 weather satellite data with RGB, day convective storm and 24-hour microphysics methods, as well as upper air observations using radiosonde at 12 UTC in the Semarang area. The use of this method is effective in knowing the atmospheric conditions in the Semarang area. The results of observations with the RGB method show cloud conditions that cause heavy rain with high intensity. Observations with the day convective storm method detect convective clouds that have the potential to cause heavy rain. The 24-hour microphysics method identifies High Cumulonimbus, Dense Cirrus, and Thick Cirrus cloud types that can cause heavy rain. Upper air observations with radiosonde at 12 UTC showed the early phase of severe weather in the Semarang area. The results of this study confirm the importance of satellite and radiosonde data integration in predicting and analysing heavy rain events for hydrometeorological disaster risk mitigation.
Heavy rain, Himawari-9, Radiosonde, Convective clouds, Flood.
Design of a Radiosonde System for Air Quality Monitoring in the Tangerang City Area
Tangerang City, located in Banten Province, Indonesia, experiences high rainfall levels that often lead to damaging floods. Climate change has exacerbated these disasters with increasingly unpredictable weather extremes. To address these challenges, upper-air observations using radiosondes are crucial for real-time atmospheric monitoring. Radiosondes effectively measure meteorological parameters such as air pressure, temperature, and humidity when flown into the atmosphere by balloons. This study developed a cost-effective and easily implementable radiosonde system to enhance meteorological data collection in Tangerang City. Thus, the system supports accurate weather analysis and improves disaster management and environmental decision-making in the region.
Tangerang City, Radiosonde, Upper-air observation, Meteorology, Disaster mitigation.
A Literature Review : Honeypot-Based Security Solutions for Safeguarding Critical Data at BMKG
The expanding dependence on advanced foundations by meteorological organizations like BMKG (Badan Meteorologi, Klimatologi, dan Geofisika) has increased the hazard of cyber attacks, which seem compromise basic climate and climate information frameworks. This paper investigates the execution of honeypot-based security arrangements as a proactive approach to defend BMKG's organize framework. Honeypots, outlined to draw potential aggressors, give important bits of knowledge into rising dangers and offer assistance to relieve dangers some time recently they reach center frameworks. By sending honeypots in BMKG's organize, this consider explores their viability in identifying and analyzing cyber-attacks focusing on meteorological information, which is basic for open security and national improvement arranging. The inquire about presents a comparative investigation of different honeypot arrangements and their capacity to distinguish modern dangers, such as zero-day misuses and Progressed Tireless Dangers (APTs), which posture critical dangers to BMKG's operations. Comes about illustrate that joining honeypots into BMKG's cybersecurity system upgrades risk discovery, diminishes reaction time, and reinforces in general information security. These discoveries highlight the potential for honeypot frameworks to play a key part in securing basic meteorological data, guaranteeing the unwavering quality and astuteness of climate information fundamental for calamity readiness and hazard administration.
Prediction Analysis of PM2.5 Concentration Based on Temperature Variables Using XGBoost Algorithm (Case Study: Kemayoran, Central Jakarta)
Improvement in air quality in urban areas like Central Jakarta is a big challenge due to high activities of transport, industry, and dense population. This study aims to predict PM2.5 concentrations by utilising the XGBoost algorithm based on temperature data as the main variable. The data was taken from Kemayoran, Central Jakarta, with an observation time span from 01 January 2017 to 12 February 2017. XGBoost was chosen due to the non-linear and complex nature of the data. Based on the results of the test, it shows that the model performance is far from improved, characterized by a high Mean Squared Error (MSE) value and a small R² score. These performance limitations are driven by the small amount of data and the absence of other supporting variables such as air humidity, wind speed, and rainfall. The high PM2.5 concentration was contributed by the research location in Kemayoran, one of the most densely populated areas with high industrial activity and fossil-fuelled transport. This study provides evidence to support the addition of supporting variables and the extension of the observation time span to enhance model accuracy. Therefore, the XGBoost algorithm can be used as a promising solution for air quality prediction in urban cities where air pollution has reached its peak.
Utilizing Machine Learning and Deep Learning Techniques for Forecasting Rainfall and Weather: A Review
Machine learning and deep learning are vital for achieving precise rainfall and weather forecasting, which is crucial for agricultural planning, managing water resources, and reducing disaster risks. This study reviews a range of literature on weather and rainfall forecasting, emphasizing deep learning techniques. Additionally, it examines the performance of various machine learning models, including Long Short-Term Memory (LSTM) networks and Support Vector Regression (SVR), in improving forecast accuracy. These methods show notable improvements in accuracy over traditional models. The study’s findings suggest that enhanced machine learning and deep learning models can significantly benefit weather forecasting, aiding in climate change adaptation efforts.
Machine Learning Regression Modeling Analysis for PM 2.5 Concentration Estimation in Jakarta: Approaches and Implications for Air Quality
Air pollution by fine particulate matter (PM2.5) significantly impacts public health and environmental stability. As an air pollutant, PM2.5 is influenced by climate factors such as temperature, humidity, and wind patterns, all of which fluctuate due to climate change. This literature review explores the application of machine learning (ML) in predicting and analyzing PM2.5 behavior, focusing on three primary methods: Support Vector Regression (SVR), Random Forest (RF), and Neural Networks (NN). Based on 20 studies, this review compares the strengths and limitations of each method, evaluating how ML techniques address the complexity and variability of climate data in the context of PM2.5.
Brilliant Muhammad Al Hadid Deva Sudarjo