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All Journal IJCCS (Indonesian Journal of Computing and Cybernetics Systems) JSI: Jurnal Sistem Informasi (E-Journal)
Istifa Shania Putri
Telkom University
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Library & Information Science Other

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Target Representation as a Key Factor for Generalization of Physics-Based Electricity Price Correction Models Istifa Shania Putri
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 20, No 4 (2026): October
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.123700

Abstract

Physics-based electricity price forecasting models, such as Linear Optimal Power Flow (LOPF), provide strong theoretical accuracy but often fail to capture real market prices. To reduce this gap, machine learning-based correction models are commonly applied; however, their reliability under significant market regime shifts remains unclear. This study investigates the robustness boundaries of price correction models using German electricity market data from 2019 and 2024, consisting of 17,544 hourly observations. Three key findings are identified. First, model selection and hyperparameter optimization do not improve cross-regime robustness: all 16 tested configurations fail when models trained in high-price regimes are applied to low-price regimes, increasing forecasting errors by 2.2–4.0 times. Second, the main source of failure is the target formulation. For the best-performing configuration, replacing the conventional price-difference target (EUR/MWh) with a ratio-based target turns all four transfer scenarios positive, achieving up to a 34.1% improvement in backward transfer. Third, model degradation is primarily driven by the distance between fuel-cost market regimes rather than temporal distance. Evaluation across 30 regime transitions shows that conventional correction approaches deteriorate as market condition differences increase. These findings reveal the limitations of current AI-based electricity price correction frameworks and highlight target redesign and regime-aware modeling as critical strategies for improving robustness under evolving market conditions.This study tests model resilience during the 2022 extreme market volatility (peak of the European gas crisis, RMSE: 159.06 EUR/MWh). The results show that the ratio target outperforms the difference target by +8% to +11% when predicting crisis periods. However, the model fails when trained on crisis data but tested under normal conditions, showing an explicit regime limit.
Agentic AI dalam Pertanian: Tinjauan Literatur Terstruktur atas Perkembangan, Arsitektur, dan Arah Riset Indonesia Atika Istiqomah; Istifa Shania Putri
JSI: Jurnal Sistem Informasi (E-Journal) Vol 18 No 2 (2026): JSI: Jurnal Sistem Informasi (E-Journal)
Publisher : Jurusan Sistem Informasi Fakultas Ilmu Komputer Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18495/jsi.v18i2.601

Abstract

Agentic AI menjadi salah satu arah penting dalam evolusi kecerdasan buatan karena memperluas fungsi model dari sekadar prediksi menuju perencanaan, penggunaan alat, memori, dan koordinasi antaragen. Di sektor pertanian, perkembangan ini relevan karena keputusan budidaya bergantung pada integrasi data spasial, sensor, iklim, risiko usaha tani, dan pengetahuan agronomi yang heterogen. Artikel ini menyajikan tinjauan literatur terstruktur atas publikasi terbuka periode 2021-2026 yang membahas decision support systems, land suitability, crop recommendation, large language models, foundation models, multimodal models, explainable AI, dan framework agentic untuk pertanian. Metode telaah disusun secara purposive-structured melalui lima tahap, yaitu identifikasi tujuan penelitian, perumusan pertanyaan telaah, penelusuran kata kunci, seleksi dan ekstraksi data, serta sintesis interpretatif. Korpus akhir terdiri atas 25 artikel domain dan 1 referensi metodologis. Hasil sintesis menunjukkan empat temuan utama. Pertama, agentic AI penting bagi pertanian karena mampu menjembatani kebutuhan akan keputusan yang explainable, adaptif, dan berbasis data lapangan. Kedua, literatur bergerak dari predictive AI dan DSS yang berfokus pada akurasi menuju sistem generatif, tool-augmented, dan multi-agent. Ketiga, arsitektur yang paling relevan adalah arsitektur berlapis yang memadukan grounding data, domain modeling, tool-use and planning, multi-agent coordination, serta human oversight. Keempat, agenda riset Indonesia perlu menekankan corpus agronomi lokal, benchmark tugas, evaluasi lintas agen, lokalisasi bahasa, dan governance by design. Kontribusi utama artikel ini terletak pada penyusunan sistematika telaah yang lebih operasional, taksonomi lima lapis agentic AI untuk pertanian, serta rumusan agenda riset yang dapat digunakan sebagai dasar pengembangan sistem pertanian cerdas yang lebih terintegrasi.
Co-Authors Atika Istiqomah