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All Journal Format : Jurnal Imiah Teknik Informatika Pendas : Jurnah Ilmiah Pendidikan Dasar Jatilima : Jurnal Multimedia Dan Teknologi Informasi Jurnal AbdiMas Nusa Mandiri Jurnal Aplikasi Teknologi Informasi dan Manajemen (JATIM) Jurnal Abdimas Bina Bangsa Paradigma Sinergi: Jurnal Pengabdian Kepada Masyarakat NERO (Networking Engineering Research Operation) JI-Tech Journal of Community Research & Engagement (JCRE) Jurnal Ilmiah Universitas Satya Negara Indonesia
Chafid, Nurul
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Journal : paradigma

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Explainable Ensemble Machine Learning for Forecasting Gold Price Dynamics Using Financial Time-Series Data Tamam, Moh. Badri; Chafid, Nurul
Paradigma - Jurnal Komputer dan Informatika Vol. 28 No. 2 (2026): September 2026 Period
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/p.v28i2.12407

Abstract

Gold is widely regarded as a protective investment during periods of economic instability, yet its price movements are shaped by complex market forces, volatility regimes, and macroeconomic conditions. Forecasting gold prices therefore remains a challenging task in financial analytics. This study investigates gold price dynamics by applying ensemble machine learning models in combination with explainable artificial intelligence (XAI) techniques. The dataset comprises 2,511 daily observations from 2016–2026, enriched with engineered features including daily returns, short‑ to long‑term moving averages (MA20, MA50, MA200), rolling volatility, and trend distance indicators. A range of regression‑based models were evaluated, including Linear Regression, Decision Tree, Random Forest, HistGradientBoosting, XGBoost, LightGBM, Multilayer Perceptron (MLP), and Long Short‑Term Memory (LSTM). Performance was assessed using Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and R². Results indicate that Linear Regression achieved the lowest MAE (≈0.57), reflecting strong precision under linear assumptions, while ensemble models consistently demonstrated greater robustness and generalization in capturing non‑linear relationships. Deep learning models (MLP and LSTM) performed poorly under the chosen configuration. To enhance transparency, SHAP analysis identified closing price, short‑term volatility, and moving averages as dominant predictors, while LIME provided case‑specific explanations of individual forecasts. Overall, the integration of ensemble machine learning with XAI improved both predictive accuracy and interpretability, offering practical insights for financial forecasting, portfolio management, and risk assessment in commodity markets.
Co-Authors Aldi Setiawan, Aldi Anggriansyah, Muhammad Satya Auliana, Sigit Berliani, Intan Budi Santoso, Teguh Dewi, Susana Edi Siregar Eightian, Vieri Ginola Fanani, Khairul Gagah Dwiki Putra Aryono Hafidz Hanafiah hanifah hanifah Hendriawan, Mohamad Hernalom Sitorus Hozairi Kiki Kusumawati Kuzairi Moh. Badri Tamam Nova, Merlin Dwi Nurhayati Nurhayati Nursafira, Nursafira Ofan Sofian Panjaitan, Bosar Pramudi Harsono Prionggo Hendradi Rahmatina, Izazih Riama Sibarani Santoso, Teguh Budi Saputra, Apriyanto Sianturi, Heriston Silmi, Ai Siswahyudianto Siti Mudawanah Sukarno Bahat Nauli Sumadikarta, Istiqomah Wawan Kurniawan Yusriani Sapta Dewi