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All Journal Jurnal Teknologi Informatika dan Komputer International Journal Of Computer, Network Security and Information System (IJCONSIST)
Masti Fatchiyah Maharani
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Computer Science & IT Electrical & Electronics Engineering

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Stock Price Prediction Using Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) Methods Riza Akhsani Setyo Prayoga; Ariansyah, Fery Almas; Daffa, Muhammad Falikhuddin; Laqma Dica Fitrani; Masti Fatchiyah Maharani; Angga Lisdiyanto; Angkawidjaja , Steven
IJCONSIST JOURNALS Vol 7 No 1 (2025): September
Publisher : International Journal of Computer, Network Security and Information System

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33005/ijconsist.v7i1.158

Abstract

This research aims to improve the accuracy of stock price prediction through the application of Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) methods, focusing on stocks from the Composite Stock Price Index (CSPI) referred to as the IDX Composite. The research process includes comprehensive steps, including data collection and preprocessing, dataset creation with emphasis on stock closing prices, and division of the dataset into training and test data. The LSTM and GRU models were designed with a recurrent layer and a Dense layer and then trained for 100 epochs with a batch size of 32. Model evaluation was performed by comparing key metrics such as Root Mean Squared Error (RMSE), Mean Squared Error (MSE), and Mean Absolute Error (MAE) on the test set. The EPOCH-RMSE graph provides an overview of the changes in the RMSE value during training. The best result of the LSTM model was achieved at the 96th epoch with RMSE 40.36, MSE 1385.97, and MAE 30.09, while GRU achieved peak performance at the 92nd epoch with RMSE 37.33, MSE 908.29, and MAE 25.42. In conclusion, GRU can be considered as a more effective option in predicting JCI stock prices based on performance evaluation using various metrics such as RMSE, MSE, and MAE.
Digital Business Model Development through the Implementation of a Smart Tuition Payment System Fitrani, Laqma; Angga Lisdiyanto; Masti Fatchiyah Maharani; Yerezqy Bagus; Dina Zatusiva Haq
Jurnal Teknologi Informatika dan Komputer Vol. 12 No. 1 (2026): Jurnal Teknologi Informatika dan Komputer
Publisher : Universitas Mohammad Husni Thamrin

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37012/jtik.v12i1.3282

Abstract

  The tuition payment system is an essential component of school financial administration that supports educational operations. However, many schools still rely on manual or semi-digital payment processes, which often result in delayed transaction recording, data entry errors, and limited transparency in financial reporting. This study aims to develop a web-based online tuition payment application to improve the efficiency, accuracy, and transparency of school financial management. The research employed a qualitative descriptive approach with data collected through observation, interviews, and literature review. System development was conducted using the Agile method, allowing the application to be refined iteratively according to user needs. The system was implemented using PHP and MySQL and includes features such as student data management, tuition billing generation, payment recording, digital receipt generation, and real-time financial reporting. The results indicate that the developed system enhances administrative efficiency, reduces recording errors, and improves the timeliness and transparency of financial reports. Furthermore, the implementation of this system supports the achievement of Sustainable Development Goal (SDG) 4: Quality Education by strengthening governance and sustainability in educational services.
Co-Authors Angga Lisdiyanto Angkawidjaja , Steven Ariansyah, Fery Almas Daffa, Muhammad Falikhuddin Dina Zatusiva Haq Fitrani, Laqma Laqma Dica Fitrani Riza Akhsani Setyo Prayoga Yerezqy Bagus