Long Short Term Memory and Gradient Boosting Model for One Day Ahead Forecasting of ANTAM Gold Bar Prices

Annisa  Ashari; Zakarias Situmorang; Rika  Rosnelly

doi:10.52436/1.jutif.2026.7.2.5630

Authors

Annisa Ashari Computer Science, Universitas Potensi Utama, Indonesia
Zakarias Situmorang Computer Science, Universitas Potensi Utama, Indonesia
Rika Rosnelly Computer Science, Universitas Potensi Utama, Indonesia

DOI:

https://doi.org/10.52436/1.jutif.2026.7.2.5630

Keywords:

Criteria's Weighting, Integrated Primary Care Information System, Posyandu, Rank-Based Aggregation, Score-Based Aggregation

Abstract

This study develops and optimizes a hybrid LSTM-XGBoost forecasting model for daily ANTAM gold bar prices. The model utilizes historical time-series data of ANTAM gold prices, enriched with macroeconomic variables including the USD/IDR exchange rate and Brent oil prices, as well as derived features such as returns, lags, rolling statistics, and calendar effects. The LSTM component captures medium-term sequential patterns from the price series and macroeconomic variables, while the XGBoost component exploits a rich set of tabular features to model nonlinear relationships and volatility dynamics. Both models are trained and tuned separately, then combined through a weighted ensemble scheme in which the optimal weight is selected by minimizing Mean Absolute Percentage Error (MAPE) on the validation set. Experimental results on the test set show that the proposed hybrid model achieves Mean Squared Error (MSE) of 26,891,172.36, Root Mean Squared Error (RMSE) of 16,398.53, MAPE of 0.0058 (approximately 99.42% accuracy), and coefficient of determination \mathbit{R}^\mathbf{2} of 0.9971, outperforming a naïve baseline that assumes “tomorrow’s price equals today’s price”. The optimized LSTM-XGBoost hybrid model proves highly effective for short-term ANTAM gold price forecasting, providing reliable decision support for Indonesian gold market stakeholders.

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References

V. R. Prasetyo, S. Axel, J. T. Soebroto, D. Sugiarto, A. Winatan, and S. D. Njudang, “Prediksi Harga Emas Berdasarkan Data gold . org menggunakan Metode Long Short Term Memory Memory Method,” vol. 11, no. September, pp. 623–629, 2022.

I. M. Gananta, I. N. Purnama, and K. Q. Fredlina, “OPTIMASI PREDIKSI HARGA EMAS DENGAN METODE SUPPORT VECTOR REGRESSION ( SVR ) MENGGUNAKAN ALGORITMA GRID SEARCH,” vol. 7, no. 6, pp. 3160–3165, 2023.

Mutia Evi Kristhy, Sayu Oktalita, Sendy Yonathan, Tommy Susanto B.a, and Wiji Nurul Hikmah, “Keuntungan Investasi Emas Antam Di Masa Pandemi Covid-19,” J. Komunitas Yust., vol. 5, no. 1, pp. 388–399, 2022, doi: 10.23887/jatayu.v5i1.47128.

W. F. Hidayat, K. Handayani, Y. Malau, R. A. Purnama, and A. Setiadi, “Prediksi Harga Komoditi Emas Menggunakan Metode Long Short-Term Memory Dengan Penambahan Optimalisasi,” vol. 6, no. 2, 2024.

D. I. Purnama, “Peramalan Harga Emas Saat Pandemi Covid-19 Menggunakan Model Hybrid Autoregressive Integrated Moving Average - Support Vector Regression,” Jambura J. Math., vol. 3, no. 1, pp. 52–65, 2021, doi: 10.34312/jjom.v3i1.8430.

W. Budiharto, “Data science approach to stock prices forecasting in Indonesia during Covid-19 using Long Short-Term Memory (LSTM),” J. Big Data, vol. 8, no. 1, 2021, doi: 10.1186/s40537-021-00430-0.

N. K. Agusmawati1, F. Khoiriyah2, and A. Tholib3, “PREDIKSI HARGA EMAS MENGGUNAKAN METODE LSTM DAN GRU,” JiTEKH, vol. 8, no. 1, pp. 32–36, 2020, doi: 10.35447/jitekh.v8i1.194.

Y. R. Madhika and T. Hidayat, “Gold Price Prediction Using the ARIMA and LSTM Models,” vol. 7, no. 3, pp. 1255–1264, 2023.

Moch Farryz Rizkilloh and Sri Widiyanesti, “Prediksi Harga Cryptocurrency Menggunakan Algoritma Long Short Term Memory (LSTM),” J. RESTI (Rekayasa Sist. dan Teknol. Informasi), vol. 6, no. 1, pp. 25–31, 2022, doi: 10.29207/resti.v6i1.3630.

E. Fitri, “Analisis Perbandingan Metode Regresi Linier, Random Forest Regression dan Gradient Boosted Trees Regression Method untuk Prediksi Harga Rumah,” J. Appl. Comput. Sci. Technol., vol. 4, no. 1, pp. 58–64, 2023, doi: 10.52158/jacost.v4i1.491.

Putra Nasyuli Leriansyah, Lubis Imran, and Marwan Elhanafi Andi, “Penerapan Model Machine Learning Algoritma Gradient Boosting dan Linear Regression Melakukan Prediksi Harga Kendaraan Bekas,” J. Ilmu Komput. dan Sist. Informasi(JIRSI), vol. 2, no. 2, pp. 299–310, 2023, [Online]. Available: https://jurnal.unity-academy.sch.id/index.php/jirsi/index%0Ahttp://creativecommons.org/licenses/by-sa/4.0/

A. Aulia, B. Aprianti, Y. Supriyanto, and C. Rozikin, “Prediksi Harga Emas dengan Menggunakan Algoritma Support Vector Regression (Svr) dan Linear Regression,” J. Ilm. Wahana Pendidik., vol. 8, no. 5, pp. 84–88, 2022, doi: 10.5281/zenodo.6408864.

P. Simamora, S. A. Pasaribu, and V. Wijaya, “Peningkatan dan Optimalisasi Prediksi Harga Emas Menggunakan Metode Combine Machine Learning Random Forest dan Gradient Boosting,” vol. 01, pp. 42–51, 2024.

F. Achmad and R. Y. Kalbarini, “ANALISIS PROGRAM INVESTASI EMAS ANTAM DI BANK SYARIAH INDONESIA KANTOR CABANG KETAPANG,” pp. 89–107.

J. M. Ahn, J. Kim, and K. Kim, “Ensemble Machine Learning of Gradient Boosting (XGBoost, LightGBM, CatBoost) and Attention-Based CNN-LSTM for Harmful Algal Blooms Forecasting,” Toxins (Basel)., vol. 15, no. 10, 2023, doi: 10.3390/toxins15100608.

P. R. Mohapatra, A. K. Parida, S. K. Swain, and S. S. Basa, “Gradient Boosting and LSTM Based Hybrid Ensemble Learning for Two Step Prediction of Stock Market,” J. Adv. Inf. Technol., vol. 14, no. 6, pp. 1254–1260, 2023, doi: 10.12720/jait.14.6.1254-1260.

A. F. Shaikh, S. B. More, Y. L. Bhirud, V. V. Shelar, and K. M. Bagwan, “Comparative Analysis of Gradient Boosting Machines and Long Short-Term Memory Networks for Stream Flow Forecasting,” Larhyss J., vol. 2024, no. 60, pp. 171–187, 2024.

T. Handhayani, C. Tanudy, and J. Hendryli, “Prediksi Harga Emas di Indonesia Menggunakan Gated Recurrent Unit,” J. Fasilkom, vol. 13, no. 3, pp. 480–488, 2023, doi: 10.37859/jf.v13i3.6185.

M. G. Lusiana, M. Suhendra, S. L. Santoso, and E. Hartanto, “Minigold: Trend baru investasi emas di masa pandemi,” Pros. Semin. Nas. Ris. Pasar Modal, vol. 1, no. 1, 2021, [Online]. Available: http://conference.um.ac.id/index.php/rpm/article/view/2067

M. YURTSEVER, “Gold Price Forecasting Using LSTM, Bi-LSTM and GRU,” Eur. J. Sci. Technol., vol. 31, no. 31, pp. 341–347, 2021, doi: 10.31590/ejosat.959405.

D. Zhang, S. Chen, L. Liwen, and Q. Xia, “Forecasting Agricultural Commodity Prices Using Model Selection Framework with Time Series Features and Forecast Horizons,” IEEE Access, vol. 8, pp. 28197–28209, 2020, doi: 10.1109/ACCESS.2020.2971591.

W. Zhang, H. Li, L. Han, L. Chen, and L. Wang, “Slope stability prediction using ensemble learning techniques: A case study in Yunyang County, Chongqing, China,” J. Rock Mech. Geotech. Eng., vol. 14, no. 4, pp. 1089–1099, 2022, doi: 10.1016/j.jrmge.2021.12.011.

F. Matloob et al., “Software defect prediction using ensemble learning: A systematic literature review,” IEEE Access, vol. 9, pp. 98754–98771, 2021, doi: 10.1109/ACCESS.2021.3095559.

V. I. Kontopoulou, A. D. Panagopoulos, I. Kakkos, and G. K. Matsopoulos, “A Review of ARIMA vs. Machine Learning Approaches for Time Series Forecasting in Data Driven Networks,” Futur. Internet, vol. 15, no. 8, pp. 1–31, 2023, doi: 10.3390/fi15080255.

K. H. Suradiradja, “Algoritme Machine Learning Multi-Layer Perceptron dan Recurrent Neural Network untuk Prediksi Harga Cabai Merah Besar di Kota Tangerang,” Fakt. Exacta, vol. 14, no. 4, p. 194, 2022, doi: 10.30998/faktorexacta.v14i4.10376.

B. Lindemann, T. Müller, H. Vietz, N. Jazdi, and M. Weyrich, “A survey on long short-term memory networks for time series prediction,” Procedia CIRP, vol. 99, no. July 2020, pp. 650–655, 2021, doi: 10.1016/j.procir.2021.03.088.