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All Journal SINTECH (Science and Information Technology) Journal JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Jusikom: Jurnal Sistem Informasi Ilmu Komputer Journal of Information Systems and Informatics Cyber Security dan Forensik Digital (CSFD) Journal of Dinda : Data Science, Information Technology, and Data Analytics International Journal Software Engineering and Computer Science (IJSECS) Journal of Intelligent Software Systems Journal Of Artificial Intelligence And Software Engineering
Bambang Purnomosidi Dwi Putranto
Universitas Teknologi Digital Indonesia
Computer Science & IT Decision Sciences, Operations Research & Management Education

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Journal : Journal of Information Systems and Informatics

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Spatio-Temporal Graph Neural Network for Solar Irradiance Prediction: A Case Study in Nganjuk, Indonesia Nurcahyo, Agung Wilis; Putranto, Bambang Purnomosidi Dwi
Journal of Information System and Informatics Vol 8 No 1 (2026): February
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v8i1.1424

Abstract

Solar energy utilization in tropical regions is strongly influenced by the accuracy of solar irradiance estimation, which is affected by temporal variability and spatial atmospheric interactions. Conventional forecasting approaches commonly rely on single-station time-series models, limiting their ability to capture regional dependencies. This study proposes a spatio-temporal modeling framework based on Graph Neural Networks (GNN) to estimate solar irradiance by explicitly incorporating spatial relationships among observation sites. The study focuses on Sawahan Subdistrict, Nganjuk Regency, Indonesia, using solar irradiance data collected from five Automatic Weather Stations (AWS) operated by the Indonesian Agency for Meteorology, Climatology, and Geophysics (BMKG) during 2024. Each station is represented as a graph node, with spatial connections constructed based on geographical distance, while temporal dependencies are modeled using Long Short-Term Memory (LSTM). Experimental results show that the proposed model achieves a Mean Absolute Error (MAE) of 102.64 W/m², a Root Mean Squared Error (RMSE) of 166.76 W/m², and an R² value of 0.6446 for the target location. These findings demonstrate that GNN-based spatial aggregation improves estimation stability and accuracy, providing practical support for localized solar energy assessment in tropical regions.
Co-Authors Ahmad Bilal Almagribi Almagribi, Ahmad Bilal Andre Argisitawan Andriyani, Widyastuti Anwarudin Anwarudin Arma Fauzi Bagas Triaji Cucut Hariz Pratomo Danny Kriestanto Dommy Kristomo Domy Kristomo Faizal Makhrus Faizal Makhrus Femi Dwi Astuti Hamdani Hamdani Hendra Maryanto Heri Muhrial I Nyoman Oka Semadi Kuindra Iriyanta Moh. Abdul Kholik Muhammad Guntara Muhammad Najib Nenen Isnaeni Nugroho, Muhammad Agung Nurcahyo, Agung Wilis Rikie Kartadie Robertus Saptoto Robertus Saptoto Siti Khomsah, Siti Sri Redjeki Widyastuti Andriyani