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Journal : Journal of Computer Science and Information Systems (JCoInS)

Rekayasa Fitur dan Gradient Boosting untuk Prediksi Harga Saham Pada Pasar Saham Indonesia Rambe, Bhakti Helvi; Munthe, Ibnu Rasyid; Hanum, Fauziah; Hutagaol, Anita Sri Rejeki
Journal of Computer Science and Information System(JCoInS) Vol 7, No 1: JCoInS | 2026
Publisher : Universitas Labuhanbatu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36987/jcoins.v7i1.8945

Abstract

This study aims to analyze the comparative performance of three machine learning models Neural Network, Random Forest, and XGBoost in predicting the stock price of Bank Rakyat Indonesia (BBRI.JK) based on feature engineering integration. The background of this study is based on the need to develop accurate and efficient predictive models to deal with stock market volatility. The Data used covers the period 2010-2025 with the application of technical indicators such as Moving Average (MA), Relative Strength Index (RSI), volatility, and price momentum as the main features. The research method uses a machine learning approach based on supervised learning with a five-fold cross validation process. Model evaluation was conducted using quantitative metrics including Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), coefficient of determination (R2), and Mean Absolute Percentage Error (MAPE). The results showed that XGBoost produced the Best Performance With R2 = 0.9451, MAE = 87.3129,and MSE = 10327.1187, followed by Random Forest (R2 = 0.9233) and Neural Network (R2 = 0.9120). The XGBoost Model proved to be the most stable and efficient in handling nonlinear data as well as extreme price fluctuations. The discussion confirms that the integration of engineering features improves the generalization capability of the model and lowers the prediction error rate significantly. Future research is recommended to include macroeconomic variables, sentiment data, and reinforcement learning approaches to broaden the scope and improve the model's adaptability to global financial market dynamics.
Co-Authors A.A. Ketut Agung Cahyawan W Amanda, Ade Trya Anita Sri Rejeki Hutagaol, Anita Sri Rejeki Ariyana Hikmanti Asmaul Husna Aulia Rahman Broto, Bayu Eko Budi Yulianto Chairunisya, Chairunisya Dicky, Dicky Yudhistira Pradana Djoko Susanto Eko Broto, Bayu Entas, Derinta FITRIANI LUBIS, FITRIANI Gulo, Nur Ainun Hantoro, Ramandha Rudwi Hendriyani, Chintia Hery Wibowo, Hery Islami, Muhammad Febrian Limbong, Christine Herawati Listianingsih, Lina Lumbantobing, Lyla Riani Mashuri Mashuri Ma’rifah, Atun Raudotul Muhammad Amar Adly, Muhammad Amar Munthe, Ibnu Rasyid Nanda, Calvin Tiara Nasution, Novrihan Leily Nazliah, Rahmi Neviami, Jeni Ni Made Dwiyana Rasuma Putri, Ni Made Dwiyana Rasuma Nur Kholizah Rambe, Selvi Pane, Maria Grace Panjaitan, Agape Anjumarito Pitriyani Pitriyani Prananda, Nadya Pratiwi, Anggaraini Putri, Diah Tri Utami Ramadhan, Taufiq Rambe , Fadil Azury Farega Rambe, Bhakti Helvi Rambe, Oki Gunawansah Richi Andrianto Rusdiana, Melania Diah S, Dwi Vita Lestari Sahmuddin, Sahmuddin Saputra, Julfan Sarwo Edi, Sarwo Simanjorang, Elida Florentina Sinaga Simbolon, Jose Andrian Siregar, Asdiah Siregar, Zulkifli M. Efendi Sitanggang, Jelita Yulianti Br Sitanggang, Kamsia Dorliana Soni Akhmad Nulhaqim Sri Haryati Sthefany, Gloria SURTININGSIH, SURTININGSIH Surya Maulana Sutiningsih Syahputra, Aldy Pratama Tria Wulandari Triana, Noor Yunida Vanessamae, Vanessamae Wandi Adiansah, Wandi Waruwu, Seti Nayanti Widiastiti, A. A. Istri Putra Wirakhmi, Ikit Netra Y, Heylen A Yanuarita, Heylen Amildha Zulkifli Musannip Efendi Siregar