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All Journal HOAQ (High Education of Organization Archive Quality) : Jurnal Teknologi Informasi
Winardi, Kevin
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Computer Science & IT

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PENERAPAN ALGORITMA RANDOM FOREST REGRESSION DALAM PREDIKSI HARGA SAHAM BBRI: IMPLEMENTATION OF THE RANDOM FOREST REGRESSION ALGORITHM FOR PREDICTING BBRI STOCK PRICES Winardi, Kevin; Nugroho, Yulianus Febry Tri; Johannes; Herdiatmoko, Hendrik Fery
HOAQ (High Education of Organization Archive Quality) : Jurnal Teknologi Informasi Vol. 17 No. 1 (2026): Jurnal HOAQ - Teknologi Informasi
Publisher : STIKOM Uyelindo Kupang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52972/hoaq.vol17no1.p9-18

Abstract

Pergerakan harga saham dipengaruhi oleh berbagai faktor dan bersifat fluktuatif, sehingga diperlukan metode prediksi yang mampu menangkap pola data yang kompleks. Penelitian ini bertujuan untuk memprediksi harga saham menggunakan metode Random Forest Regression. Data yang digunakan dibagi menjadi data pelatihan dan data pengujian untuk mengevaluasi kinerja model. Kinerja model dievaluasi menggunakan beberapa metrik, yaitu Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), dan koefisien determinasi (R²). Hasil penelitian menunjukkan bahwa model Random Forest Regression setelah optimasi menghasilkan nilai MAE sebesar 64,02,  RMSE sebesar 84,51, dan R² sebesar 0,8484. Nilai-nilai tersebut mengindikasikan bahwa model memiliki tingkat kesalahan prediksi yang rendah dan mampu menjelaskan 84,84% variasi pada data harga saham. Berdasarkan hasil tersebut, dapat disimpulkan bahwa Random Forest memiliki kinerja yang baik dan cukup andal dalam memprediksi harga saham.   Stock price movements are influenced by various factors and exhibit high volatility, making accurate prediction a challenging task. This study aims to predict stock prices using the Random Forest Regression method. The dataset is divided into training and testing sets to evaluate the model’s performance. The performance of the model is assessed using Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and the coefficient of determination (R²). The results show that the Random Forest Regression model after optimization achieves an MAE of 64,02, an RMSE of 84,51, and an R² value of 0.8484. These results indicate a low prediction error and demonstrate that the model is able to explain 84.84% of the variance in stock price data. Therefore, it can be concluded that Random Forest is an effective and reliable method for stock price prediction.
Co-Authors Herdiatmoko, Hendrik Fery Johannes Nugroho, Yulianus Febry Tri