Stock Price Prediction of Bank Rakyat Indonesia Using an Ensemble Stacking Model of K-Nearest Neighbors (KNN) and Support Vector Machine (SVM)

Muhammad, Tamami and Sefto, Pratama and Zaenuddin, . and Haldi, Budiman and Erfan, Karyadiputra and Desy Ika, Puspitasari (2025) Stock Price Prediction of Bank Rakyat Indonesia Using an Ensemble Stacking Model of K-Nearest Neighbors (KNN) and Support Vector Machine (SVM). INTI JOURNAL, 2025 (28). pp. 1-5. ISSN e2600-7320

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Abstract

Need for future price forecasts by investors faces difficulties in achieving accurate predictions because market changes exist. Standard single models do not accurately model stock market behaviors because of their complex nature. The problem solution implemented by the study involves combining K-Nearest Neighbors (KNN) and Support Vector Machine (SVM) to create ensemble stacking. Research personnel collected Bank Rakyat Indonesia's (BRI) historical stock price data using KNN and SVM models. Studio performance delivers superior predictive results with lower error rates than KNN and SVM models that operate individually. Study results demonstrate stacking technology produces the most desirable results for stock market price prediction.

Item Type:	Article
Uncontrolled Keywords:	Stock Prediction, Machine Learning, Ensemble Stacking, KNN, SVM
Subjects:	H Social Sciences > HG Finance Q Science > QA Mathematics > QA76 Computer software T Technology > T Technology (General)
Depositing User:	Unnamed user with email masilah.mansor@newinti.edu.my
Date Deposited:	20 Sep 2025 07:07
Last Modified:	20 Sep 2025 07:07
URI:	http://eprints.intimal.edu.my/id/eprint/2177

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