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All Journal Jurnal Teknik Informatika UNIKA Santo Thomas Jurnal Teknik Informatika Unika Santo Thomas (JTIUST) Journal of Computation Physics and Earth Science
Trihadi, Edward
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Physics

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A Web-Based Machine Learning Approach for Standardized Precipitation Index Prediction Hadi, Ahmad Fauzi Faishal; Sinambela, Marzuki; Rachmawardani, Agustina; Trihadi, Edward
Jurnal Teknik Informatika UNIKA Santo Thomas Vol 10 No. 1 : Tahun 2025
Publisher : LPPM UNIKA Santo Thomas

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Accurate and user-friendly drought forecasting tools are crucial for mitigating the impact of meteorological droughts, particularly in vulnerable areas such as South Sumatra, Indonesia. This study introduces an interactive web-based application built to anticipate drought conditions by forecasting the Standardized Precipitation Index (SPI). The system relies on deep learning techniques trained using three decades of rainfall data collected from the Climatological Station in South Sumatra. In evaluating model performance, both Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) architectures were assessed. While both models delivered comparable short-term predictions, the LSTM experienced a significant decline in accuracy over extended forecasting periods (specifically at SPI-6), primarily due to overfitting. In contrast, the RNN displayed more stable and reliable results, making it the preferable model for this geographical context. Specifically, the RNN achieved a lower Mean Absolute Error (MAE) of 0.4007, a reduced Root Mean Squared Error (RMSE) of 0.4684, and a higher coefficient of determination (R²) of 0.7338. These metrics outperformed those of the LSTM, which recorded a MAE of 0.4115, an RMSE of 0.4840, and an R² of 0.7036. Such results confirm that the RNN offers a more precise and dependable fit for the station’s dataset. The web platform also effectively visualizes the model outputs, providing a dynamic and interactive 24-month forecast view that supports early warning efforts and informed decision-making for regional authorities and stakeholders.
A Web-Based Machine Learning Approach for Standardized Precipitation Index Prediction Hadi, Ahmad Fauzi Faishal; Sinambela, Marzuki; Rachmawardani, Agustina; Trihadi, Edward
Jurnal Teknik Informatika UNIKA Santo Thomas Vol 10 No. 1 : Tahun 2025
Publisher : LPPM UNIKA Santo Thomas

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Accurate and user-friendly drought forecasting tools are crucial for mitigating the impact of meteorological droughts, particularly in vulnerable areas such as South Sumatra, Indonesia. This study introduces an interactive web-based application built to anticipate drought conditions by forecasting the Standardized Precipitation Index (SPI). The system relies on deep learning techniques trained using three decades of rainfall data collected from the Climatological Station in South Sumatra. In evaluating model performance, both Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) architectures were assessed. While both models delivered comparable short-term predictions, the LSTM experienced a significant decline in accuracy over extended forecasting periods (specifically at SPI-6), primarily due to overfitting. In contrast, the RNN displayed more stable and reliable results, making it the preferable model for this geographical context. Specifically, the RNN achieved a lower Mean Absolute Error (MAE) of 0.4007, a reduced Root Mean Squared Error (RMSE) of 0.4684, and a higher coefficient of determination (R²) of 0.7338. These metrics outperformed those of the LSTM, which recorded a MAE of 0.4115, an RMSE of 0.4840, and an R² of 0.7036. Such results confirm that the RNN offers a more precise and dependable fit for the station’s dataset. The web platform also effectively visualizes the model outputs, providing a dynamic and interactive 24-month forecast view that supports early warning efforts and informed decision-making for regional authorities and stakeholders.
Desain Ulang Antarmuka Pengguna dan Pengalaman dengan Peningkatan Identitas Merek untuk Situs Web STMKG melalui Implementasi WordPress Aji, Tonny Wahyu; Yasir, Ahmad Meijlan; Nardi; Sorfian; Rachmawardani, Agustina; Jehadun, Marianus Carol; Wastumirad, Adi Widiatmoko; Trihadi, Edward
Journal of Computation Physics and Earth Science (JoCPES) Vol 5 No 1 (2025): Journal of Computation Physics and Earth Science
Publisher : Yayasan Kita Menulis - JoCPES

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63581/JoCPES.v5i1.15

Abstract

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.
Co-Authors Aji, Tonny Wahyu Hadi, Ahmad Fauzi Faishal Jehadun, Marianus Carol Marzuki, Sinambela Nardi Rachmawardani, Agustina Sorfian Wastumirad, Adi Widiatmoko Yasir, Ahmad Meijlan