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All Journal Jurnal Penelitian Teknologi Informasi dan Sains
Angga Sukma Budi Darmawan
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Computer Science & IT

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TOWR Stock Forecasting From 2021-2025 Using Machine Learning Andy Hermawan; Angga Sukma Budi Darmawan; Muhammad Iqbal; Mochammad Rivan Akhsa; Nila Rusiardi Jayanti; Zidan Amukti Rajendra
Jurnal Penelitian Teknologi Informasi dan Sains Vol. 3 No. 1 (2025): JURNAL PENELITIAN TEKNOLOGI INFORMASI DAN SAINS
Publisher : Institut Teknologi dan Bisnis (ITB) Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54066/jptis.v3i1.3136

Abstract

Accurate stock price forecasting is a crucial yet challenging task due to the complex and dynamic nature of financial markets. This study employs the Prophet model to predict the stock prices of PT Sarana Menara Nusantara Tbk (TOWR) from 2021 to 2025. The research leverages historical stock data, incorporating dividend distribution dates and Annual General Meeting (AGM) events as external regressors to enhance predictive accuracy. The model was developed using machine learning-based time series forecasting, with hyperparameter tuning applied to optimize performance. The evaluation metrics indicate a Mean Absolute Error (MAE) of Rp49.92 and a Mean Absolute Percentage Error (MAPE) of 6.47%, demonstrating the model’s robustness in capturing long-term stock price trends. The findings suggest that stock prices exhibit significant movements around dividend announcement periods and AGM events, highlighting the impact of corporate actions on market behavior. This study reinforces the importance of incorporating fundamental financial indicators into forecasting models to improve decision-making for investors and financial analysts. The results offer practical implications for investment strategy formulation, risk management, and market trend analysis.
Co-Authors Andy Hermawan Mochammad Rivan Akhsa Muhammad Iqbal Nila Rusiardi Jayanti Zidan Amukti Rajendra