MENGOPTIMALKAN ALGORITMA MACHINE LEARNING UNTUK PREDIKSI HARGA SAHAM BERKELANJUTAN: STUDI KASUS PADA SEKTOR ENERGI TERBARUKAN DI INDONESIA

Abstrak

This study aims to develop a stock price prediction model using machine learning algorithms applicable to the renewable energy sector in Indonesia. With the increasing awareness of sustainable investment, the renewable energy sector has become a primary focus for investors looking to contribute to sustainable development. This research uses historical stock price data from companies in the renewable energy sector listed on the Indonesia Stock Exchange (IDX). The algorithms used include Random Forest, Support Vector Machine (SVM), and Long Short-Term Memory (LSTM). The results show that the LSTM model provides the highest prediction accuracy with an average error of 2.5%. These findings are expected to serve as a reference for investors in making more accurate and sustainable investment decisions.