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All Journal Sinkron : Jurnal dan Penelitian Teknik Informatika Electronic Journal of Education, Social Economics and Technology
Budiarto, Indri
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Rainfall Monitoring Using Aloptama Automatic Rain Gauge And The Network Development Life Cycle Method Nugroho, Kristiawan; Afandi , Afandi; Rokhayadi, Wakhid; Budiarto, Indri; Hermawan, Taufan
Sinkron : jurnal dan penelitian teknik informatika Vol. 8 No. 4 (2024): Article Research Volume 8 Issue 4, October 2024
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i4.13908

Abstract

Examining the role of rainfall data management in monitoring and reducing natural disasters. Between the observation post and the coordinating office of the Central Java Meteorology, Climatology and Geophysics Agency, there are problems in managing rainfall data. To increase the accuracy and efficiency of rainfall monitoring, the Central Java BMKG Coordinator has used various platforms that are considered very good, such as Grafana, Node-RED, Xampp, and MQTT. Previous research has shown that the use of the Automatic Rain Gauge (ARG) and the Network Development Life Cycle (NDLC) method is very effective in creating an accurate and reliable rainfall monitoring system. This research uses the NDLC model, which consists of analysis, design, prototype simulation, implementation, monitoring and management stages. It is hoped that the research results will help improve visual monitoring of rainfall in local areas and increase understanding of rainfall patterns, flood prediction, water resource management and mitigation measures. This will serve as a reference for governments and institutions working together to make decisions to avoid catastrophic climate change.
Performance Comparison of Manual and Automatic Rain Gauge Using XGBoost and Random Forest Regression Budiarto, Indri; Supriyanto, Aji
Electronic Journal of Education, Social Economics and Technology Vol 6, No 1 (2025)
Publisher : SAINTIS Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33122/ejeset.v6i1.955

Abstract

This study aims to compare the performance of automatic and manual rain gauges in the Central Highlands of Central Java using a machine learning approach based on Extreme Gradient Boosting (XGBoost) and Random Forest Regression (RFR) algorithms. Daily rainfall data were collected from five regencies Banyumas, Banjarnegara, Wonosobo, Temanggung, and Pemalang between 2021 and 2024. Preprocessing involved merging data from two types of instruments (Automatic Rain Gauge/AWS and manual ombrometer), correcting anomalies, and standardizing date-time formats. The models were developed using feature engineering techniques, including multi-lag and moving averages, and evaluated using MAE, RMSE, and R-squared (R²) metrics. The results show that the XGBoost model with automatic data achieved the best performance, with a Mean Absolute Error (MAE) of 17.3632 mm, Root Mean Squared Error (RMSE) of 27.0282 mm, and R² of 0.5050. In comparison, the Random Forest model with automatically generated data produced an MAE of 16.6307 mm, an RMSE of 28.5286 mm, and an R² of 0.4485. Models with manual data showed lower performance, with R² values below 0.30. These findings indicate that automatic measurement data are more stable and effective for building predictive rainfall models using machine learning. This supports the use of automatic instruments as the primary data source in rainfall forecasting and hydrometeorological disaster mitigation systems.
Co-Authors Afandi , Afandi Dian Kristiawan Nugroho Hermawan, Taufan Rokhayadi, Wakhid