Forecasting Data Time Series Menggunakan MLP dan LSTM untuk Memprediksi Jumlah Produksi Bir
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Muhammad Ibnu Choldun Rachmatullah
*
Universitas Telkom, Bandung,
Indonesia
(*) Corresponding Author
Abstract
Time series data forecasting is an important approach in various sectors such as finance, energy, and healthcare. As technology advances, deep learning methods such as Multi-Layer Perceptron (MLP) and Long Short-Term Memory (LSTM) are increasingly being used to improve prediction accuracy. This study compares the performance of these two methods in forecasting a time series dataset of monthly beer production in Australia. The model was trained and tested using a 70% training and 30% testing data split. Performance evaluation was based on the Root Mean Square Error (RMSE) value after 10 experimental repetitions. The results show that MLP has a lower RMSE value and a smaller standard deviation than LSTM, both on the training and testing data. This indicates that MLP is more stable and efficient in handling datasets with simple patterns and low complexity, while LSTM tends to require more intensive tuning and has a higher risk of overfitting. Therefore, MLP is recommended as a lighter and more consistent alternative forecasting method for similar data scenarios.
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References
Bandara, K., Bergmeir, C., & Smyl, S. (2020). Forecasting across time series databases using recurrent neural networks on groups of similar series: A clustering approach. Expert Systems with Applications, 140. https://doi.org/10.1016/j.eswa.2019.112896
Casolaro, A., Capone, V., Iannuzzo, G., & Camastra, F. (2023). Deep Learning for Time Series Forecasting: Advances and Open Problems. Information (Switzerland), 14(11). https://doi.org/10.3390/info14110598
Chomiak, T., & Hu, B. (2024). Time-series forecasting through recurrent topology. Communications Engineering, 3(1), 1–10. https://doi.org/10.1038/s44172-023-00142-8
Damayanti, S., Yosmar, S., & Afandi, N. (2023). Implementation of Fuzzy Time Series Chen for Forecasting Indonesian Oil and Gas Imports Value. BAREKENG: Jurnal Ilmu Matematika Dan Terapan, 17(2), 0685–0694. https://doi.org/10.30598/barekengvol17iss2pp0685-0694
Egrioglu, E., & Bas, E. (2024). A new deep neural network for forecasting: Deep dendritic artificial neural network. Artificial Intelligence Review, 57(7), 1–25. https://doi.org/10.1007/s10462-024-10790-7
Hariyanto, S., Sumanto, Y. D., Khabibah, S., & Zaenurrohman. (2023). Average-Based Fuzzy Time Series Markov Chain Based on Frequency Density Partitioning. Journal of Applied Mathematics, 2023. https://doi.org/10.1155/2023/9319883
Kim, J., Kim, H., Kim, H., Lee, D., & Yoon, S. (2024). A Comprehensive Survey of Time Series Forecasting: Architectural Diversity and Open Challenges. http://arxiv.org/abs/2411.05793
Kong, X., Chen, Z., Liu, W., Ning, K., Zhang, L., Muhammad Marier, S., Liu, Y., Chen, Y., & Xia, F. (2025). Deep learning for time series forecasting: a survey. In International Journal of Machine Learning and Cybernetics (Issue 0123456789). Springer Berlin Heidelberg. https://doi.org/10.1007/s13042-025-02560-w
Li, W., & Law, K. L. E. (2024). Deep Learning Models for Time Series Forecasting: A Review. IEEE Access, 12(May), 92306–92327. https://doi.org/10.1109/ACCESS.2024.3422528
Lim, B., & Zohren, S. (2021). Time-series forecasting with deep learning: A survey. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 379(2194). https://doi.org/10.1098/rsta.2020.0209
Lin, L., Li, Z., Li, R., Li, X., & Gao, J. (2024). Diffusion models for time-series applications: a survey. Frontiers of Information Technology and Electronic Engineering, 25(1), 19–41. https://doi.org/10.1631/FITEE.2300310
Masini, R. P., Medeiros, M. C., & Mendes, E. F. (2023). Machine learning advances for time series forecasting. Journal of Economic Surveys, 37(1), 76–111. https://doi.org/10.1111/joes.12429
Mojtahedi, F. F., Yousefpour, N., Chow, S. H., & Cassidy, M. (2025). Deep Learning for Time Series Forecasting: Review and Applications in Geotechnics and Geosciences. Archives of Computational Methods in Engineering. https://doi.org/10.1007/s11831-025-10244-5
Montet, F., Pasquier, B., Wolf, B., & Hennebert, J. (2024). Enabling Diffusion Model for Conditioned Time Series Generation †. Engineering Proceedings, 68(1). https://doi.org/10.3390/engproc2024068025
Okia Dinda Kelana, Atus Amadi Putra, Nonong Amalita, & Admi Salma. (2023). Comparison of the Chen and Sinsgh’s Fuzzy Time Series Methods in Forecasting Farmer Exchange Rates in Indonesia. UNP Journal of Statistics and Data Science, 1(4), 264–270. https://doi.org/10.24036/ujsds/vol1-iss4/36
Oliveira, J. M., & Ramos, P. (2024). Evaluating the Effectiveness of Time Series Transformers for Demand Forecasting in Retail. Mathematics, 12(17), 1–28. https://doi.org/10.3390/math12172728
Ping, W., Hu, Y., & Luo, L. (2024). Price Forecast of Treasury Bond Market Yield: Optimize Method Based on Deep Learning Model. IEEE Access, 12(November), 194521–194539. https://doi.org/10.1109/ACCESS.2024.3519438
Rahmad Revi Fadillah, Dony Permana, Yenni Kurniawati, & Admi Salma. (2023). Application of the Fuzzy Time Series-markov Chain Method to the Rupiah Exchange Rate Against the US Dollar (USD). UNP Journal of Statistics and Data Science, 1(4), 369–376. https://doi.org/10.24036/ujsds/vol1-iss4/91
Sina, L. B., Secco, C. A., Blazevic, M., & Nazemi, K. (2023). Hybrid Forecasting Methods—A Systematic Review. Electronics (Switzerland), 12(9). https://doi.org/10.3390/electronics12092019
Sineglazov, V., & Horbatiuk, V. (2025). Time Series Forecasting Using Recurrent Neural Networks Based on Recurrent Sigmoid Piecewise Linear Neurons. Applied Artificial Intelligence, 39(1). https://doi.org/10.1080/08839514.2025.2490057
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