Forecasting Penjualan Sembako berbasis Model Prophet: Strategi Efisiensi Stok pada pada Ritel Tradisional

Haikal Tirta Albanna; Diana Diana

doi:10.29408/edumatic.v9i3.32251

Authors

Haikal Tirta Albanna Program Studi Teknik Informatika, Universitas Bina Darma https://orcid.org/0009-0005-5979-6730
Diana Diana Program Studi Teknik Informatika, Universitas Bina Darma https://orcid.org/0000-0002-5798-6654

DOI:

https://doi.org/10.29408/edumatic.v9i3.32251

Keywords:

forecasting, inventory optimization, prophet, seasonal demand, small-scale retail

Abstract

Maintaining a stable supply of staple commodities remains a major challenge for retail stores, as inventory management often relies on intuition rather than data-driven insights, leading to potential losses from overstocking or missed sales when supplies run out. This study implements the Prophet model to forecast staple-food sales and to support data-driven inventory optimization. In contrast to previous research that has predominantly focused on large-scale industries or e-commerce, this study examines small-scale retail settings characterized by pronounced seasonal demand fluctuations. Monthly sales data from August 2023 to May 2025 were analyzed using the Prophet model, with performance evaluated through MAE, RMSE, and MAPE metrics. The Prophet model was selected for its capability to capture nonlinear trends and seasonal effects without the complex parameterization required by models such as ARIMA. The results show an average forecasting accuracy of 94.09%, demonstrating the model’s adaptability in identifying sales fluctuations during seasonal periods such as Ramadan. Practically, the findings assist store owners in optimizing stock levels and promotional strategies, while academically the study extends the forecasting literature within small-scale retail contexts with limited data. The novelty of this research lies in applying the Prophet model to enhance data-driven inventory-management decisions in micro and small enterprises exhibiting irregular seasonal patterns.

References

Armansyah, A., & Ramli, R. K. (2022). Model prediksi kelulusan mahasiswa tepat waktu dengan metode Naïve Bayes. Edumatic: Jurnal Pendidikan Informatika, 6(1), 1-10. https://doi.org/10.29408/edumatic.v6i1.4789

Atamimi, F. M. H., Wintanti, W., & Abdillah, G. (2025). Enhanching Prophet Time Series Forecasting on Sparse Data via Hyperparameter Optimizattion: A Case Study in Retail. Sinkron, 9(2), 1000–1007. https://doi.org/10.33395/sinkron.v9i2.14804

Azzahra, A., Ramdhan, W., & Kifti, W. M. (2022). Single Exponential Smoothing: Metode Peramalan Kebutuhan Vaksin Campak. Edumatic: Jurnal Pendidikan Informatika, 6(2), 215–223. https://doi.org/10.29408/edumatic.v6i2.6299

Bisma, M. H., Hakim, M. A., Arifin, R., & Susena, E. (2025). Sistem Informasi Manajemen Stok Toko Kelontong Berbasis Web untuk Pencatatan Stok di Toko Dandung. Switch: Jurnal Sains Dan Teknologi Informasi, 3(1), 19–34. https://doi.org/10.62951/switch.v3i4.507

Damaliana, A. T., Hindrayani, K. M., & Fahrudin, T. M. (2025). Hybrid Holt Winter-Prophet method to forecast the num-ber of foreign tourist arrivals through Bali’s Ngurah Rai Airport. International Journal of Data Science Engineering, and Analytics, 3(2), 1–11. https://doi.org/10.33005/ijdasea.v3i2.8

Haryanto, H. (2024). Implementasi Sistem Pengendalian Inventory pada UMKM Warung Awi Makanan Khas Selatpanjang. Pemberdayaan Masyarakat : Jurnal Aksi Sosial, 1(4), 115–124. https://doi.org/10.62383/aksisosial.v1i4.942

Hyndman, R. J. (2024). Forecasting short course. In R. J Hyndman (Ed.), Proceedings of the Forecasting Workshop, University of Western Australia (1st ed., pp. 1–138). University of Western Australia.

Kenyi, M. G. S., & Yamamoto, K. (2024). A hybrid SARIMA-Prophet model for predicting historical streamflow time-series of the Sobat River in South Sudan. Discover Applied Sciences, 6(9), 457. https://doi.org/10.1007/s42452-024-06083-x

Kristianto, B. A., Licantik, L., Sari, N. N. K., & Widiatry, W. (2025). Penerapan Business Forecasting dengan Metode Arima (Autoregressive Integrated Moving Average) dalam Meramalkan Penjualan Produk di Cafe The Garrison. JOINTECOMS (Journal of Information Technology and Computer Science), 5(1), 90-99.

Meliani, S., Siagian, Y., & Ananda, R. (2024). Sistem Forecasting Permintaan Tempe menggunakan Metode Weighted Moving Average. Edumatic: Jurnal Pendidikan Informatika, 8(1), 133–142. https://doi.org/10.29408/edumatic.v8i1.25632

Menculini, L., Marini, A., Proietti, M., Garinei, A., Bozza, A., Moretti, C., & Marconi, M. (2021). Comparing Prophet and Deep Learning to ARIMA in Forecasting Wholesale Food Prices. Forecasting, 3(3), 644–662. https://doi.org/10.3390/forecast3030040

Muttaqin, W. M. I., Ramdhan, W., & Kifti, W. M. (2022). Sistem Peramalan Permintaan Darah dengan Metode Simple Moving Average. Edumatic: Jurnal Pendidikan Informatika, 6(2), 242–251. https://doi.org/10.29408/edumatic.v6i2.6326

Omotoye, E., & Rotimi, B. (2025). Stationarity in Prophet Model Forecast: Performance Evaluation Approach. American Journal of Theoretical and Applied Statistics, 14(3), 109–117. https://doi.org/10.11648/j.ajtas.20251403.12

Pradita, S. P. (2024). Product inventory control at a local brand forum outlet using ABC analysis. Agrointek : Jurnal Teknologi Industri Pertanian, 18(2), 246–255. https://doi.org/10.21107/agrointek.v18i2.18819

Primandari, A. H. (2024). A Multivariate Approach: Forecasting Jakarta Composite Using Prophet Facebook. Jurnal Statistika Dan Aplikasinya, 8(1), 128–137. https://doi.org/10.21009/jsa.08111

Purnamasari, D. I., Permadi, V. A., Saepudin, A., & Agusdin, R. P. (2023). Demand Forecasting for Improved Inventory Management in Small and Medium-Sized Businesses. Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI), 12(1), 56–66. https://doi.org/10.23887/janapati.v12i1.57144

Rahayu, A., Lattu, A., & Mupaat, M. (2022). Analysis of product stock inventory forecasting using weighted moving average method. Jurnal Teknik Informatika (Jutif), 3(6), 1631-1638. https://doi.org/10.20884/1.jutif.2022.3.6.421

Sunki, A., SatyaKumar, C., Surya Narayana, G., Koppera, V., & Hakeem, M. (2024). Time series forecasting of stock market using ARIMA, LSTM and FB prophet. MATEC Web of Conferences, 392, 01163. https://doi.org/10.1051/matecconf/202439201163

Suryawan, I. G. T., Putra, I. K. N., Meliana, P. M., & Sudipa, I. G. I. (2024). Performance Comparison of ARIMA, LSTM, and Prophet Methods in Sales Forecasting. Sinkron, 8(4), 2410–2421. https://doi.org/10.33395/sinkron.v8i4.14057

Zach, A. (2025). Data-Driven Forecasting and Inventory Optimization in Retail Supply Chains Using Hybrid Machine Learning Models. International Journal of Economics and Commerce Research (IJECR), 5(1), 28–34.