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p-Index From 2021 - 2026
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All Journal Indonesian Journal of Applied Mathematics and Statistics International Journal of Computer Science and Humanitarian AI
Lucky, Henry
Unknown Affiliation
Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Public Health

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Web Application for IHSG Prediction Using Machine Learning Algorithms Wijaya, Andryan Kalmer; Lucky, Henry; Arifin, Samsul
Indonesian Journal of Applied Mathematics and Statistics Vol. 2 No. 1 (2025): Indonesian Journal of Applied Mathematics and Statistics (IdJAMS)
Publisher : Lembaga Penelitian dan Pengembangan Matematika dan Statistika Terapan Indonesia, PT Anugrah Teknologi Kecerdasan Buatan PT Anugrah Teknologi Kecerdasan Buatan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.71385/idjams.v2i1.21

Abstract

This study investigates the effectiveness of the Long Short-Term Memory (LSTM) method in predicting the stock price of the Composite Stock Price Index (CSPI). LSTM, a variant of Recurrent Neural Networks, is designed to overcome challenges such as the vanishing gradient problem and long-term dependencies in time-series data. Given the dynamic and volatile nature of financial markets, accurate stock price prediction is crucial for investors and analysts. The data set used in this study consists of daily CSPI prices from January 2000 to December 2023, which serve as both training and testing data for model development. The LSTM model is trained to forecast the next day’s stock price, and its performance is compared with traditional statistical models, particularly the Autoregressive Integrated Moving Average (ARIMA) model and linear regression. Performance evaluation is based on the Mean Absolute Percentage Error (MAPE), a widely used metric for assessing predictive accuracy. The results indicate that while the ARIMA model achieves a lower MAPE of 0.7%, demonstrating slightly superior accuracy, the LSTM model also performs well, with a MAPE of approximately 1%. These findings suggest that while statistical models like ARIMA remain highly effective for stock price forecasting, deep learning approaches such as LSTM still offer promising predictive capabilities, especially when handling large and complex datasets. The ability of LSTM to capture non-linear patterns and temporal dependencies makes it a viable alternative for financial forecasting, potentially benefiting traders and market analysts seeking data-driven decision-making tools.
Analyzing Public Sentiment Toward the Makan Bergizi Gratis (MBG) Program on TikTok Using SVM and IndoBERT Winston, Alfredo; Darren, Nicholas; Lucky, Henry; Pradana, Rilo; Sagala, Noviyanti
International Journal of Computer Science and Humanitarian AI Vol. 3 No. 1 (2026): IJCSHAI (In Press)
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/ijcshai.v3i1.15184

Abstract

Social media has become a major platform for the public to express opinions toward government programs. This study analyzes public sentiment toward Indonesia’s Makan Bergizi Gratis (MBG) program using a text mining approach. A total of 11,730 TikTok comments related to the MBG program were collected and classified into positive, negative, and neutral sentiments. Two classification models were compared: a traditional Support Vector Machine (SVM) using TF-IDF features and a transformer-based model, IndoBERT. Experimental results show that IndoBERT outperforms the tuned SVM model, achieving an accuracy of 0.78 and a weighted F1-score of 0.78, compared to 0.73 accuracy and 0.73 F1-score obtained by the SVM. IndoBERT demonstrates better performance in handling neutral and context-dependent sentiments, indicating its effectiveness for analyzing Indonesian social media data related to public policy evaluation. This study contributes to the growing body of research on Indonesian sentiment analysis by providing an empirical comparison between classical machine learning and transformer-based models for analyzing public responses to government policies using social media data
Co-Authors Darren, Nicholas Pradana, Rilo Sagala, Noviyanti Samsul Arifin Wijaya, Andryan Kalmer Winston, Alfredo