Aplikasi Prediksi Harga Saham Telkom Indonesia Berbasis Streamlit Menggunakan Machine Learning

Abstract

Fluctuations in the share price of PT Telkom Indonesia (TLKM) are a major obstacle for individual investors, who generally rely on non-numerical analysis. This study aims to develop an easy prediction tool by testing three machine learning methods—linear regression, support vector machine (SVM), and neural network (NN)—and then using them in a user-friendly web application built with the Streamlit framework. Historical TLKM stock data was prepared through a feature engineering process before performance testing. According to the comparison results, the Support Vector Machine (SVM) model proved to have the best predictive ability compared to Linear Regression and NN. The SVM model is better because it has the lowest RMSE (Root Mean Squared Error) and the highest R² (Coefficient of Determination) score, meaning it can better understand the complex and non-linear trends in the TLKM market. The selected model was then integrated into the Streamlit application, which provides real-time prediction results and comparison visualizations. This research successfully bridges the gap between the accuracy of advanced machine learning models and the real needs of investors by providing an informative and easy-to-use decision-making tool.