Komparasi dan Implementasi Algoritma Regresi Machine Learning untuk Prediksi Indeks Harga Saham Gabungan

Dwi Eko Waluyo, Hayu Wikan Kinasih, Cinantya Paramita, Dewi Pergiwati, Rajendra Nohan, Fauzi Adi Rafrastara

Abstract


Indeks Harga Saham Gabungan (IHSG) or Indonesia Composite Index (ICI) is part of the macro indicators of a country that describes the economic condition of a country. ICI is an interesting study to research since its existence will be able to show market sentiment regarding an event that occurred in a country. This research tries to predict the ICI in the future based on historical data. The dataset used in this research is publicly available in Yahoo Finance. The experiment is conducted by implementing some regression machine learning algorithms, such as Decision Tree, Random Forest, k-Nearest Neighbor (kNN), and Linear Regression. As a result, Decision Tree has the lowest MSE value compared to other methods: 1268.242. In this research, a website-based application prototype was also developed that can be used to view IHSG graphs and make future predictions, using the 4 (four) tested algorithms.

Keywords


IHSG, prediksi, machine learning, decision tree, random forest, knn, regresi linear

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DOI: https://doi.org/10.30591/jpit.v9i1.6105

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