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All Journal Antivirus : Jurnal Ilmiah Teknik Informatika
Galih Adhi Putratama
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Computer Science & IT

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Evaluasi Komparatif Metode Machine Learning Untuk Memprediksi Perubahan Harga Saham Galih Adhi Putratama; Satya Maulana Fahreza; Yudhistira Rakha Ramandhani
Antivirus : Jurnal Ilmiah Teknik Informatika Vol 17 No 2 (2023): November 2023
Publisher : Universitas Islam Balitar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35457/antivirus.v17i2.2871

Abstract

Forecasting price patterns in the stock market poses a complicated and intricate task due to numerous uncertain factors and variables that influence market value. This study conducts a comparative evaluation of three popular computational learning approaches, namely Random Forest, K-Nearest Neighbors (KNN), and XGBoost, for predicting stock price changes. The research findings indicate that Random Forest achieves higher ROC scores, while XGBoost exhibits superior performance in relation to accuracy, recall, and precision. The Windowing method is also applied to the dataset to address overfitting issues. This study offers valuable knowledge for professionals and researchers in the domain of stock price prediction, enabling them to choose the optimal model based on preferred evaluation metrics.
Co-Authors Satya Maulana Fahreza Yudhistira Rakha Ramandhani