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Abstract
This study discusses the Machine Learning algorithm with technical indicator features in predicting the movement of Indonesian banking sector stocks. Many people seek profit in Indonesian banking stocks because most of them have good fundamentals but have high volatility. The strategy that can be used is the Support Vector Machine (SVM) algorithm with the Moving Averages Convergence Divergence (MACD) technical indicator feature. The SVM algorithm is used because it can process stock price movement data and technical indicators which tend to be complex. This research was conducted with the aim of contributing to the development of a machine learning-based stock prediction model that can be used by academics and financial practitioners. The research stages are collecting historical data on Infobank15 stock movements, data cleaning, training and testing the SVM model, then backtesting. The research methodology includes data processing using Python, training and testing the SVM model, then trading simulation with an initial capital of IDR 100 million. The kernels tested include RBF, Polynomial, and Sigmoid. Model performance is evaluated based on return, win rate, profit ratio, Sharpe ratio, maximum drawdown, risk-reward ratio, and accuracy rate. Historical data of Infobank15 stock is used in this study where the dataset is historical data from 2019-2024 for training and testing the model and historical data from 2024 for backtesting. Based on the experimental results obtained, it can be concluded that the combination of the SVM model and the MACD indicator yields favourable outcomes. The kernel that provides the best overall results is the Polynomial kernel.
Penelitian ini membahas penerapan Machine Learning dengan fitur indikator teknikal dalam memprediksi pergerakan saham sektor perbankan Indonesia. Banyak masyarakat mencari keuntungan di saham perbankan Indonesia karena sebagian besar memiliki fundamental yang baik, namun memiliki volatilitas yang tinggi. Strategi yang dapat digunakan yaitu algoritma Support Vector Machine (SVM) dengan fitur indikator teknikal Moving Averages Convergence Divergence (MACD). Algoritma SVM dipakai karena dapat mengolah data-data pergerakan harga saham dan indikator teknikal yang di mana cenderung kompleks. Riset ini dilakukan dengan tujuan berkontribusi pada pengembangan model prediksi saham berbasis Machine Learning yang dapat digunakan oleh akademisi dan praktisi keuangan. Tahapan risetnya yaitu pengumpulan data historis pergerakan saham Infobank15, pembersihan data, pelatihan dan pengujian model SVM, kemudian backtesting. Metodologi risetnya meliputi pengolahan data menggunakan Python, pelatihan dan pengujian model SVM, kemudian simulasi trading. Kernel yang diuji antara lain Radial Basis Function, Polynomial, dan Sigmoid. Kinerja model dievaluasi berdasarkan return, win-rate, profit ratio, sharpe ratio, maximum drawdown, risk-reward ratio, dan accuracy rate. Data historis saham Infobank15 digunakan dalam penelitian ini adalah tahun 2019-2024 untuk pelatihan dan pengujian model serta data historis 2024 untuk backtesting. Dari hasil percobaan yang telah dilakukan, dapat disimpulkan bahwa kombinasi model SVM dan indikator MACD memberikan hasil yang baik. Kernel yang memberikan hasil terbaik secara keseluruhan adalah Polynomial.
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References
-
[1] N. Kasnaningrum, “: Model Volatilitas Return Indeks Saham Syariah di Indonesia dan Malaysia,” Uinjkt.ac.id, 2022.
[2] H. Yasin, A. Prahutama, and T. W. Utami, “PREDIKSI HARGA SAHAM MENGGUNAKAN SUPPORT VECTOR REGRESSION DENGAN ALGORITMA GRID SEARCH,” MEDIA STATISTIKA, vol. 7, no. 1, Jun. 2014.
[3] V. G. Utomo, N. Wakhidah, & A. N. Putri, “PREDIKSI HARGA SAHAM DENGAN SVM (SUPPORT VECTOR MACHINE) DAN PEMILIHAN FITUR F-SCORE,” Jurnal Informatika Upgris, vol. 6, no. 1, Jul. 2020.
[4] H. Hamzah & S. Winardi, “Effective Stock Prediction Model Using MACD Method,” International Journal of Informatics and Computation, vol. 4, no. 2, p. 1, Dec. 2022.
[5] A. Antonio Agudelo Aguirre, R. Alfredo Rojas Medina, dan N. Darío Duque Méndez, “Machine learning applied in the stock market through the Moving Average Convergence Divergence (MACD) indicator,” Investment Management and Financial Innovations, vol. 17, no. 4, pp. 44–60, Nov. 2020.
[6] V. R. Yulianti, & Y. B. Kusuma, “Analisis Teknikal Saham BBCA Menggunakan Indikator MACD dan RSI Dalam Mengambil Keputusan Investasi,” Economics And Business Management Journal, vol. 3, no. 2, pp. 409–413, Jun. 2024.
[7] W. Huang, Y. Nakamori, & S.-Y. Wang, “Forecasting stock market movement direction with support vector machine,” Computers & Operations Research, vol. 32, no. 10, pp. 2513–2522, Oct. 2005.
[8] Taufik Hidayatulloh, “KAJIAN KOMPARASI PENERAPAN ALGORITMA SUPPORT VECTOR MACHINE (SVM) DAN MULTILAYER PERCEPTRON (MLP) DALAM PREDIKSI INDEKS SAHAM SEKTOR PERBANKAN: STUDI KASUS SAHAM LQ45 IDX BANK BCA,” vol. 1, no. 1, pp. 262–272, May 2014.
[9] A. R. Fonseca et al., “Testing the Application of Support Vector Machine (SVM) to Technical Trading Rules,” IEEE Xplore, Apr. 01, 2021.
[10] E. A. Nida, “Analisis Kinerja Algoritma Support Vector Machine (SVM) Guna Pengambilan Keputusan Beli/Jual Pada Saham PT Elnusa Tbk. (ELSA),” Jurnal Transformatika, vol. 17, no. 2, pp. 160–170, Jan. 2020.
[11] Gunawan, G., Putri, A. R., & Kurniawan, R. D, “Komparasi Penerapan Algoritma SVM dan Neural Network dalam Memprediksi IHSG,” Jurnal BATIRSI, 8(1), 1-12. Jul. 2024.
[12] W. R. U. Fadilah, D. Agfiannisa, and Y. Azhar, “Analisis Prediksi Harga Saham PT. Telekomunikasi Indonesia Menggunakan Metode Support Vector Machine,” Fountain of Informatics Journal, vol. 5, no. 2, p. 45, Sep. 2020.
[13] Muhamad Jihad Jauhari, “Perbandingan Tingkat Return Saham Berdasarkan Indikator Analisis Teknikal Moving Average Convergence Divergence (Macd), Stochastic, Relative Strength Index (Rsi), Dan Bollinger Bands Selama Masa Pandemi Covid-19,” Uinjkt.ac.id, 2022.
[14] A. Riyanto and S. Astuti, “Perbandingan Tingkat Akurasi Metode Analisis Teknikal Moving Average Convergence Divergence, Moving Average, Relative Strength Index Saham Infobank15,” Jurnal Ilmiah Mahasiswa Manajemen, Bisnis dan Akuntansi (JIMMBA), vol. 6, no. 2, pp. 228–240, Apr. 2024.
[15] Bruzz, “Indeks Saham Infobank15 Periode 1 Oktober s.d. 31 Desember 2024,” IdxStock, Feb. 10, 2025. https://idxstock.com/infobank15/ (accessed Apr. 10, 2025).