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All Journal Journal of Information Systems and Informatics Journal of Intelligent Software Systems
Agung Wilis Nurcahyo
Universitas Teknologi Digital Indonesia
Computer Science & IT

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Spatio-Temporal Graph Neural Network for Solar Irradiance Prediction: A Case Study in Nganjuk, Indonesia Agung Wilis Nurcahyo; Bambang Purnomosidi Dwi Putranto
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.
LACM-Tree: Exact Similarity Search via Learning-Enhanced Clustered Metric Trees with Distance-Table Compression Techniques Ali Impron; Linda Sutriani; Adi Kusjani; Agung Wilis Nurcahyo; Fadhlih Girindra Putra; Muhammad Haries
Journal of Intelligent Software Systems Vol 5, No 1 (2026): July 2026
Publisher : LPPM UTDI (d.h STMIK AKAKOM) Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26798/jiss.v5i1.2816

Abstract

Unmanned aerial vehicle (UAV)-based agricultural and forestry monitoring generates large volumes of imagery, creating a need for efficient similarity search over the high-dimensional embeddings produced by deep learning models. Classical metric indexing methods such as the M-tree, Slim-tree, and the Clustered Metric Tree (CM-tree) provide exact search with support for dynamic operations, but their performance degrades in high dimensions because the quadratic size of the pairwise distance table erodes the effective node capacity. This paper identifies distance-table compression as the key enabler for extending the CM-tree to high-dimensional embeddings. The core contribution is the Compressed Distance Table (CDT): 8-bit quantization of the pairwise distance table combined with an error-margin pruning rule that provably preserves result completeness (Theorem 1). Under an iso-memory configuration in which the per-node table budget is held constant, CDT halves the total distance-table footprint and reduces node accesses by up to 53% at n = 20,000 (and 61% on skewed data) while maintaining recall of exactly 1.000, verified by an auditing protocol that found zero unsafe prunings across more than 24,000 quantized pruning decisions. Three lighter-weight extensions—cost-oriented pivot selection (LPS), density-adaptive splitting (DAS), and multi-pivot bound tightening (MBT)—are evaluated in a controlled component-wise ablation that serves as a diagnostic study of learning-augmented metric trees. The ablation shows that these components are not additive: DAS in particular degrades performance through split-induced fragmentation, and the mechanism of this negative interaction is analyzed in detail. All results are obtained from a complete open prototype with directly measured distance computations and node accesses, on datasets up to 20,000 objects and 256 dimensions. The findings position distance-table compression—rather than learned heuristics—as the most robust path toward exact, dynamic, memory-efficient metric indexing for embedding workloads.
Co-Authors Ali Impron Bambang Purnomosidi Dwi Putranto Fadhlih Girindra Putra Linda Sutriani Muhammad Haries