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All Journal Jurnal Teknik Informatika (JUTIF) STATISTIKA
Kamisan, Nur Arina Bazilah
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Computer Science & IT Decision Sciences, Operations Research & Management Mathematics

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Optimizing Train-Test Splits for LSTM and MLP Models in Bitcoin Price Forecasting Accuracy Kamisan, Nur Arina Bazilah; Lee, Muhammad Hisyam; Sulandari, Winita
Statistika Vol. 25 No. 2 (2025): Statistika
Publisher : Department of Statistics, Faculty of Mathematics and Natural Sciences, Universitas Islam Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29313/statistika.v25i2.6989

Abstract

Abstract. This study investigates the application and efficiency of two machine learning models, Long-Short Term Memory (LSTM) and Multilayer Perceptron (MLP), for cryptocurrency price forecasting, using Bitcoin as a case study. MLP is a feedforward neural network that learns patterns from independent data, while LSTM is a recurrent network that remembers past information to handle sequential or time-series data. The rapid growth and volatility of cryptocurrencies underscore the need for accurate price predictions to support investor’s and trader’s decision-making. The study aims to identify the optimal train-test splitting ratio for each machine learning model and to forecast Bitcoin prices over a 120 days. The daily Bitcoin price data is obtained from the Bitcoin website recorded from January 2018 until March 2021. Model performance was evaluated using Akaike Information Criterion (AIC), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE). Experimental results demonstrate that both models exhibit strong predictive capabilities; the LSTM model consistently outperforms MLP in accuracy and reliability, achieving lower MAE, MAPE, and AIC values. These findings highlight LSTM’s effectiveness for forecasting volatile financial data and provide insights into selecting appropriate data-splitting ratios to improved model performance.
Prophet with Google Trends for Forecasting Train Passengers in Java Ferawati, Kiki; Sulandari, Winita; Kamisan, Nur Arina Bazilah
Jurnal Teknik Informatika (Jutif) Vol. 7 No. 2 (2026): JUTIF Volume 7, Number 2, April 2026
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2026.7.2.5306

Abstract

As a popular transportation method for long-distance travel, trains were also a preferred choice during the homecoming period before Eid Al-Fitr, one of the major religious holidays in Indonesia. During this period, known locally as ‘mudik,’ millions of people travel from the urban cities back to their hometowns to celebrate with their families, creating a significant surge in transportation demand. However, since the holiday follows the Islamic calendar, which changes slightly every year, forecasting train passengers becomes tricky, thus requiring a different approach to achieve accurate predictions. This study utilizes the Prophet method to forecast train passengers in Java (excluding the Jabodetabek area) using the data from 2006 to 2024. We also incorporated the COVID-19 period as a fixed external regressor, along with external regressors from Google Trends data using the keywords ‘kereta api’, ‘mudik’, and ‘lebaran’, which are commonly searched by the public in relation to train travel and the Eid homecoming period. The results on the test set, 2024 data, showed that the word ‘mudik’ was the most effective in improving forecast accuracy, with a MAPE of 9.12 and RMSE of 797.76, a decrease of 11.57% and 9.34% compared to the updated baseline. This indicates that public search behavior around the term ‘mudik’ closely aligns with actual travel demand patterns. The findings of this study suggest that Prophet with external regressors are capable of forecasting train passengers and Google Trends can be a valuable addition for capturing data patterns related to specific phenomenon.
Co-Authors Kiki Ferawati Lee, Muhammad Hisyam Winita Sulandari