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All Journal International Journal Of Computer, Network Security and Information System (IJCONSIST) Sains Data Jurnal Studi Matematika dan Teknologi
Masti Fatchiyah Maharani
Universitas Pembangunan Nasional “Veteran” Jawa Timur
Computer Science & IT Electrical & Electronics Engineering Engineering Mathematics Physics

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Stock Price Prediction Using Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) Methods Riza Akhsani Setyo Prayoga; Fery Almas Ariansyah; Muhammad Falikhuddin Daffa; Laqma Dica Fitrani; Masti Fatchiyah Maharani; Angga Lisdiyanto; Steven Angkawidjaja
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.
A Comparative Study of Holt-Winters Exponential Smoothing Models for Forecasting Palm Oil Production at PT XYZ Difta Alzena Sakhi; Karina Auralia; Muhammad Nasrudin; Masti Fatchiyah Maharani
Sains Data Jurnal Studi Matematika dan Teknologi Vol 4, No 2: July-December 2026
Publisher : Institut Nurul Islam Mojokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52620/sainsdata.v4i2.420

Abstract

Monthly palm oil production fluctuates due to seasonal patterns and long-term trends, making accurate forecasting essential for operational planning in plantation companies. This study aims to compare seven Holt–Winters Exponential Smoothing models with different trend and seasonal component configurations to forecast the monthly palm oil production of PT XYZ using a univariate approach. The analysis is based on 135 observations covering the period from January 2015 to March 2026. The performance of each model configuration was evaluated using the last 12 months as the test set and assessed based on the Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE). The results indicate that the Holt–Winters model with a multiplicative trend and additive seasonality consistently achieved the best forecasting performance, with a MAPE of 5.71%, an MAE of 284.89 tons, and an RMSE of 331.83 tons, substantially outperforming the other six model configurations. The selected model was subsequently used to generate production forecasts for the next 12 months, providing a basis for managerial decision-making in harvest planning, mill capacity management, workforce allocation, and sales strategy.
Co-Authors Angga Lisdiyanto Difta Alzena Sakhi Fery Almas Ariansyah Karina Auralia Laqma Dica Fitrani Muhammad Falikhuddin Daffa Muhammad Nasrudin Riza Akhsani Setyo Prayoga Steven Angkawidjaja