Perbandingan Kinerja Lstm, Random Forest, Dan Xgboost Dalam Memprediksi Harga Penutupan Indeks Harga Saham Gabungan (Ihsg) Berbasis Data Historis

Abstract

This study compares the performance of three machine learning algorithms, Long Short-Term Memory (LSTM), Random Forest, and Extreme Gradient Boosting (XGBoost), in predicting the next-day closing price of Indonesia's Composite Stock Price Index (IHSG) as a baseline before further feature engineering is applied in a broader ongoing study. Daily historical price data (Open, High, Low, Close, Volume) covering January 2015 to early 2026 were collected from Yahoo Finance. Two feature representations were compared: the raw 5-dimensional OHLCV attributes, and a 64-dimensional temporal representation extracted from the same OHLCV data using a Bidirectional LSTM (Bi-LSTM) encoder. Each representation was evaluated using LSTM, Random Forest, and XGBoost, with performance measured by Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE) on a chronological 80:20 train-test split. The results show that the raw OHLCV representation combined with LSTM achieved the best performance (RMSE = 196.11, MAE = 164.14, MAPE = 2.18%), outperforming Random Forest and XGBoost on the same representation (MAPE 3.78% and 3.80%). Encoding the OHLCV data into a 64-dimensional Bi-LSTM representation without any external signal reduced accuracy across all three algorithms, most notably for LSTM (MAPE rising to 7.13%), indicating that unsupervised temporal encoding discards useful absolute price information when no additional predictive feature is introduced. These findings establish a validated baseline configuration and evaluation pipeline for IHSG closing-price prediction, intended as the foundation for a subsequent study that integrates external textual features into the same experimental framework.