Application of the Random Forest Algorithm for Stock Purchase Recommendation in Indonesian State-Owned Banks
Isi Artikel Utama
Abstrak
The stock market of Indonesian state-owned enterprises (SOEs) presents significant investment potential but is characterized by high volatility and complex influencing factors, making it difficult for investors—especially beginners—to make informed decisions. This research proposes a comprehensive stock recommendation system based on the Random Forest algorithm, designed to analyze and predict SOE stock performance through the integration of historical price data, financial report indicators, and market sentiment analysis. The system aims to identify optimal stock purchase recommendations by capturing both quantitative and qualitative market dynamics. Model performance was assessed using the Mean Absolute Error (MAE) metric, demonstrating that the Random Forest-based approach effectively enhances prediction accuracy and reliability. The findings suggest that this model can serve as a valuable decision-support tool for investors, improving market insight, minimizing financial risk, and supporting more data-driven investment strategies.
Rincian Artikel
Artikel ini berlisensi Creative Commons Attribution 4.0 International License.
Referensi
A. Runis Makkulau and I. Yuana, “YUME : Journal of Management Penerapan Analisa Fundamental dan Technical Analysis Sebagai Upaya Dalam Meningkatkan Keinginan Investasi Mahasiswa di Pasar Modal Syariah,” YUME J. Manag., vol. 4, no. 3, pp. 165–180, 2021, doi: 10.37531/yume.vxix.432.
Z. Sun, “The Approaches to Predict Stock Prices by Random Forest,” Appl. Comput. Eng., vol. 96, no. 1, pp. 162–168, Nov. 2024, doi: 10.54254/2755-2721/96/20241333.
A. Pratomo, R. Febrian Umbara, and A. A. Rohmawati, “Prediksi Pergerakan Harga Saham dengan Metode Random Forest Menggunakan Trend Deterministic Data Preparation ( Studi Kasus Saham Perusahaan PT Astra International Tbk, PT Garuda Indonesia Tbk, dan PT Indosat Tbk ).” [Online]. Available: https://finance.yahoo.com/
M. E. Bastian, B. Rahayudi, and D. E. Ratnawati, “Prediksi Trend Harga Saham Jangka Pendek berdasarkan Fitur Technical Analysis dengan menggunakan Algoritma Random Forest,” J. Pengemb. Teknol. Inf. dan Ilmu Komput., vol. 5, no. 10, pp. 4536–4542, 2021, [Online]. Available: http://j-ptiik.ub.ac.id
M. Iqbal Baihaqi, A. Syaripudin, and F. Agung Nugroho, “Implementation Of The Random Forest Algorithm In Stock Price Predictions Based On Historical Data Implementasi Algoritma Random Forest Pada Prediksi Harga Saham Berdasarkan Data Historis.”
I. Of, Y. Finance, M. L. In, and E. O. F. Model, “Implementasi Sistem Prediksi Saham Real-Time dengan Integrasi Yahoo Finance API dan Machine Learning di Google Colab: Analisis dan Evaluasi Performa Model,” vol. 15, no. 01, pp. 25–31, 2025.
A. M. Priyatno, L. Ningsih, and M. Noor, “Harnessing Machine Learning for Stock Price Prediction with Random Forest and Simple Moving Average Techniques,” J. Eng. Sci. Appl., vol. 1, no. 1, pp. 1–8, 2024, doi: 10.69693/jesa.v1i1.1.
K. Valiant, Y. Lukito, and R. G. Santosa, “Sistem Prediksi Harga Saham LQ45 Dengan Random Forest Classifier,” no. 2,
, doi: 10.21460/jutei.2019.32.187.
V. M. Tanto and T. A. Kurniawan, “Pengembangan sistem rekomendasi investasi saham berbasis web (studi kasus: reliance sekuritas malang),” J. Pengemb. Teknol. Inf. dan Ilmu Komput., vol. 6, no. 6, pp. 2813–2821, 2022, [Online]. Available: http://j-ptiik.ub.ac.id
Y. Kaligis, “Analisis Tingkat Kesehatan Bank Dengan Menggunakan Metode Camel Pada Industri Perbankan Bumn Yang Terdaftar Di Bursa Efek Indonesia,” J. Ris. Ekon. Manajemen, Bisnis dan Akunt., vol. 1, no. 3, pp. 263–272, 2013, doi: 10.35794/emba.v1i3.2015.