Implementasi Algoritma LSTM untuk Prediksi Kebutuhan Bahan Baku Restoran di Bale Raos Kraton Yogyakarta

Authors

Abdul Rahman Wahid Universitas Mercu Buana Yogyakarta
Arita Witanti Universitas Mercu Buana Yogyakarta

DOI:

Abstract

Bale Raos Kraton Yogyakarta menghadapi tantangan dalam manajemen persediaan bahan baku akibat fluktuasi permintaan. Penelitian ini mengembangkan model Long Short-Term Memory (LSTM) untuk memprediksi kebutuhan 36 bahan baku berbasis data historis penjualan dan kalender musiman (Januari 2023–Februari 2025). Tahap preprocessing mencakup interpolasi temporal, pembentukan fitur lagging (1-hari dan 7-hari), one-hot encoding, dan normalisasi MinMax. Arsitektur LSTM berlapis (256/128 unit) dibangun, kemudian dievaluasi dengan pembagian data: pelatihan (Januari 2023–Oktober 2024), validasi (November–Desember 2024), dan pengujian (Januari–Februari 2025). Hasil menunjukkan kinerja optimal dengan MSE 0.0108 dan MAE 0.0735. Simulasi prediksi 31 hari (29 Januari–28 Februari 2025) mencapai Overall Aggregated Accuracy (Makro) 90,27%, membuktikan efektivitas model dalam meminimalkan risiko overstock dan stockout secara operasional.

References

Nassibi, N., Fasihuddin, H., & Hsairi, L. (2023). Demand Forecasting Models for Food Industry by Utilizing Machine Learning Approaches. International Journal of Food Science & Technology, 14(3), 892–898. doi: 10.14569/IJACSA.2023.01403101

Panda, S. K., & Mohanty, S. N. (2023). Time Series Forecasting and Modeling of Food Demand Supply Chain Based on Regressors Analysis. Journal of Food Engineering, 11, 42679–42700. doi: 10.1109/ACCESS.2023.3266275

Schmidt, A., Kabir, M. W. U., & Hoque, M. T. (2022). Machine Learning Based Restaurant Sales Forecasting. Machine Learning and Knowledge Extraction, 4, 105–130. doi: 10.3390/make4010006

Haque, M. S. (2023). Retail Demand Forecasting Using Neural Networks and Macroeconomic Variables. Journal of Mathematics and Statistics Studies, 4(3), 1–6. doi: 10.32996/jmss.2023.4.3.1

Rodrigues, M., Migueis, V., Freitas, S., & Machado, T. (2023). Machine Learning Models for Short-Term Demand Forecasting in Food Catering Services: A Solution to Reduce Food Waste. Journal of Cleaner Production, 435, 1–16. doi: 10.1016/j.jclepro.2023.140265

Bengio, Y., Simard, P., & Frasconi, P. (1994). Learning Long-Term Dependencies with Gradient Descent is Difficult. IEEE Transactions on Neural Networks, 5(2), 157–166. doi: 10.1109/72.279181

Hochreiter, S., & Schmidhuber, J. (1997). Long Short-Term Memory. Neural Computation, 9(8), 1735–1780. doi: 10.1162/neco.1997.9.8.1735

Shihua, C., Xin, M., & Wen, L. (2024). Analysis and Optimization Strategies for Demand Forecasting Issues at Mixue. International Journal on Economics, Finance and Sustainable Development (IJEFSD), 7, 742–755. doi: 10.31149/ijefsd.v6i12.5346

Niako, N., Melgarejo, J. D., Maestre, G. E., & Vatcheva, K.P. (2024). Effects of Missing Data Imputation Methods on Univariate Blood Pressure Time Series Data Analysis and Forecasting with ARIMA and LSTM. BMC Medical Research Methodology, 24, 1–32. doi: 10.1186/s12874-024-02448-3

Hastie, T., Tibshirani, R., & Friedman, J. (2009). The Elements of Statistical Learning: Data Mining, Inference, and Prediction (2nd ed.). New York, NY, USA: Springer.

Janiesch, C., Zschech, P., & Heinrich, K. (2021). Machine Learning and Deep Learning. Electronic Markets, 31, 685–695. doi: 10.1007/s12525-021-00475-2

Surakhi, O., Zaidan, M. A., Fung, P. L., Motlagh, N. H., Serhan, S., AlKhanafseh, M., Ghoniem, R .M., & Hussein, T. (2021). Time-Lag Selection for Time-Series Forecasting Using Neural Network and Heuristic Algorithm. Electronics, 10(20). doi: 10.3390/electronics10202518

Albeladi, K., Zafar, B., & Mueen, A. (2023). Time Series Forecasting using LSTM and ARIMA. International Journal of Advanced Computer Science and Applications (IJACSA), 14(1), 313–320. doi: 10.14569/ijacsa.2023.0140133

Liu, F., Chen, L., Zheng, Y., & Feng, Y. (2022). A Prediction Method with Data Leakage Suppression for Time Series. Electronics, 11(22). doi: 10.3390/electronics11223701

Pranolo, A., Setyaputri, F.U., Paramarta, A. K. I., Triono, A. P. P., Fadhilla, A. F., Akbari, A. K. G., Utama, A. B. P., Wibawa, A. P., & Uriu, W. (2024). Enhanced Multivariate Time Series Analysis Using LSTM: A Comparative Study of Min-Max and Z-Score Normalization Techniques. ILKOM Jurnal Ilmiah, 16(2), 210–220. doi: 10.33096/ilkom.v1612.2333.210-220

Abdullah, E., Wee, M. I. H. M. A., Mohamad, R., & Hidayat, N. M. (2025). Enhancing Solar Irradiance Forecasting Using LSTM and Meteorological Data. Journal of Information Systems Engineering and Management, 10, 161–166. doi: 10.52783/jisem.v10i37s.6388

Guo, F., Mo, H., Wu, J., Pan, L., Zhou, H., Zhang, Z., Li, L., & Huang, F. (2024). A Hybrid Stacking Model for Enhanced Short-Term Load Forecasting. Electronics, 13. doi: 10.3390/electronics13142719

Siami-Namini, S., Tavakoli, N., & Namin, A.S. (2019). The Performance of LSTM and BiLSTM in Forecasting Time Series. 2019 IEEE International Conference on Big Data (Big Data), 1394–1401. doi: 10.1109/ICMLA.2018.00228

Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., & Salakhutdinov, R. (2014). Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research, 15, 1929–1958. doi: 10.5555/2627435.2670313

Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. Cambridge, MA, USA: MIT Press.