ANALISIS PERBANDINGAN ALGORITMA LOGISTIC REGRESSION, RANDOM FOREST, XGBOOST, DAN LSTM DALAM PREDIKSI SAHAM APPLE DENGAN BACKTESTING DATA HISTORIS

Fachrizal Irfansyah El Zacky, Yusuf Sulistyo Nugroho

Abstract


Harga saham teknologi memiliki volatilitas yang tinggi sehingga diperlukan pendekatan analitis yang mampu menghasilkan prediksi akurat untuk mendukung pengambilan keputusan investasi. Penelitian ini bertujuan membandingkan kinerja empat algoritma, yaitu Logistic Regression, Random Forest, XGBoost, dan Long Short-Term Memory (LSTM) dalam memprediksi arah pergerakan saham teknologi. Data penelitian berupa harga saham Apple Inc. selama lima tahun, diperkaya indikator teknikal dan variabel makroekonomi. Keempat model dilatih untuk klasifikasi biner (naik/turun) dan dievaluasi menggunakan Accuracy, Precision, Recall, dan F1-Score. Hasil menunjukkan bahwa LSTM memberikan performa terbaik dengan Accuracy 77,42% dan F1-Score 0,811 karena mampu memanfaatkan dependensi temporal. Logistic Regression menghasilkan recall tinggi namun bias terhadap kelas dominan, sedangkan Random Forest dan XGBoost memberikan akurasi moderat dengan keterbatasan dalam menangkap dinamika deret waktu. Seluruh model diimplementasikan dalam aplikasi web berbasis Streamlit untuk mendukung analisis interaktif dan visualisasi proyeksi. Penelitian ini menyimpulkan bahwa pendekatan deep learning, khususnya LSTM, lebih sesuai untuk data saham yang volatil, serta menegaskan pentingnya pemilihan algoritma yang selaras dengan karakteristik data dan penerapan backtesting untuk memastikan keandalan prediksi pada kondisi pasar nyata.

Keywords


Predikasi Saham; Machine Learning; Saham Teknologi; Klasifikasi; Backtesting

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DOI: https://doi.org/10.29100/jipi.v11i3.9936

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