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All Journal Jurnal Pendidikan Vokasi Indonesian Journal of Educational Studies Journal of Educational Science and Technology Jurnal Kependidikan Media Jurnal Teknik Informatika (JUTIF) PENGABDI: Jurnal Hasil Pengabdian Masyarakat Seminar Nasional Pengabdian Kepada Masyarakat INOVASI: Jurnal Hasil Pengabdian Masyarakat IPTEK: Jurnal Hasil Pengabdian kepada Masyarakat Teknovokasi : Jurnal Pengabdian Masyarakat Seminar Nasional Hasil Penelitian LP2M UNM Jurnal Pengabdian Masyarakat Journal of Embedded Systems, Security and Intelligent Systems Jurnal MediaTIK Journal of Computers, Informatics, and Vocational Education
Ridwan Daud Mahande
Universitas Negeri Makassar
Religion Agriculture, Biological Sciences & Forestry Arts Humanities Chemistry Computer Science & IT Dentistry Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mathematics Medicine & Pharmacology Nursing Public Health Social Sciences Other

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Journal : Journal of Embedded Systems, Security and Intelligent Systems

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LSTM Based Stock Price Forecasting Using RSI and MACD: A Case Study On BBRI Abdal, Nurul Mukhlisah; Mahande, Ridwan Daud; Wahyuni, Maya Sari; Siddik, Andi Muhammad Amil
Journal of Embedded Systems, Security and Intelligent Systems Vol 6, No 2 (2025): June 2025
Publisher : Program Studi Teknik Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/jessi.v6i2.8518

Abstract

The high volatility and non-linearity of stock market data present significant challenges in forecasting price movements. This study aims to develop an accurate predictive model for the daily closing price of PT Bank Rakyat Indonesia (Persero) Tbk (BBRI) using a Long Short-Term Memory (LSTM) neural network. The primary objective is to enhance predictive accuracy by incorporating technical indicators—Relative Strength Index (RSI) and Moving Average Convergence Divergence (MACD)—into the model architecture. Historical stock data from 2010 to 2024 were collected from Yahoo Finance. The dataset was preprocessed through data cleaning, feature engineering, normalization, and time-based splitting into training and testing sets. The LSTM model was trained using the Mean Squared Error (MSE) loss function and optimized with the Adam optimizer. Model performance was evaluated based on key metrics including MSE, Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE). The results indicate that the model performs effectively, with training RMSE of 73.61 and testing RMSE of 92.47. These findings demonstrate that the LSTM model, enriched with RSI and MACD indicators, is capable of capturing temporal patterns in stock prices and generating reliable forecasts. The study contributes to the growing body of literature on deep learning applications in financial forecasting, and offers practical insights for investors and analysts in understanding market behavior and supporting data-driven decision-making.
Co-Authors A. Bangki Ade Putri Hummaerah Abdal, Nurul Mukhlisah Aksa, Reski Auliah Alamsyah - Ambo Dalle Aminuddin Bakry Andi Akram Nur Risal Andi Baso Kaswar Andi Baso Kaswar Ashari, Hilda Asiani Abu BAKHRANI RAUF Bakri, Hasrul Burhamzah, Muftihaturrahmah Dwi Rezky Anandari Sulaiman Dwi Wahyuni Aprianti Fathahillah Fathahillah Fitrah Asma Darmawan Fitrah Asma Darmawan Herdiani, Rizki Husnirrahman J Irwansyah Suwahyu Jasmin, Jasmin Jasruddin Daud Malago Jasruddin Daud Malago Johar Amir Mappalotten, Abd. Muis Mappangara, Surianto Muhammad Asriadi Muhammad Ayat Hidayat Muhammad Fadel Adha Muhammad Haristo Rahman Muhammad Nawir Nasir Nasir Nasrah Natsir Ninik Rahayu Ashadi Nita Marselina Noor Fadilah Romadhani Nurhijrah, Nurhijrah Nurul Mukhlisa Abdal Rivai, Andi Tenri Ola Samad, Muh Risal Samad, Muh Rizal Sanatang Serly Setialaksana, Wirawan - Shabrina Syntha Dewi Siddik, Andi Muhammad Amil Syahrul Syahrul Wahyu Hidayat Wahyuni, Maya Sari Yahya, Muh.