Machine Learning to Predict Food Prices in Aceh Province Using the Fuzzy Time Series Method Based on Average

Authors

  • Rizky Fadillah Universitas Malikussaleh, Indonesia
  • Munirul Ula Universitas Malikussaleh, Indonesia
  • Rizki Suwanda Universitas Malikussaleh, Indonesia

DOI:

10.33395/sinkron.v9i2.14649

Keywords:

Food Commodities, Fuzzy Time Series Based Average, Machine Learning, Prediction, Time Series

Abstract

This study aims to develop a food commodity price prediction system based on Fuzzy Time Series (FTS) using average-based methods, with a case study of price data from 2018 to 2023. The system is designed to predict the prices of five main commodities: Super Quality Rice, Fresh Chicken Meat, Fresh Chicken Eggs, Bulk Cooking Oil, and Premium Quality Sugar. The prediction process involves constructing the Universe of Discourse, intervals, and fuzzy logic relations (FLR and FLRG) to model historical price patterns. The results show that this model provides accurate predictions, with the best Mean Absolute Percentage Error (MAPE) value of 0.49% for Super Quality Rice, while MAPE for other commodities ranges from 0.69% to 1.44%. The comparison graph between actual data and prediction results demonstrates consistent pattern alignment, suitable for commodities with both high price fluctuations and stable trends. This system proves effective in projecting future food prices with low error rates, making it a reliable tool to support strategic decision-making in managing food commodity prices during the five-year analysis period.

GS Cited Analysis

Downloads

Download data is not yet available.

References

Bose, M., & Mali, K. (2019). Designing fuzzy time series forecasting models: A survey. International Journal of Approximate Reasoning, 111, 78–99. https://doi.org/10.1016/j.ijar.2019.05.002

Fungki Wahyu, & Billy Hendrik. (2023). Perbandingan Algoritma Time Series Dan Fuzzy Inference System Dalam Analisis Data Deret Waktu. Jurnal Penelitian Teknologi Informasi Dan Sains, 1(3), 16–24. https://doi.org/10.54066/jptis.v1i3.711

Kadek, I., Nuryana, D., Mashuri, C., & Suhartanto, M. (2022). Rainfall Prediction Information System in Jombang Regency Using the Fuzzy Time Series Method. International Journal of Mechanical Engineering, 7(3), 974–5823.

Kurniawan, R., Wintoro, P. B., Mulyani, Y., & Komarudin, M. (2023). Implementasi Arsitektur Xception Pada Model Machine Learning Klasifikasi Sampah Anorganik. Jurnal Informatika Dan Teknik Elektro Terapan, 11(2), 233–236. https://doi.org/10.23960/jitet.v11i2.3034

Mahajan, Y., Patil, R., Pattanaik, S., Firake, T. S., Kodulkar, R., Damre, S. S., & Uplaonkar, D. (2024). A Review of Techniques and Applications for Machine Learning and Deep Learning. International Journal of Intelligent Systems and Applications in Engineering, 12(16s), 182–187.

Pujiono, S., Astuti, R., & Muhamad Basysyar, F. (2024). Implementasi Data Mining Untuk Menentukan Pola Penjualan Produk Menggunakan Algoritma K-Means Clustering. JATI (Jurnal Mahasiswa Teknik Informatika), 8(1), 615–620. https://doi.org/10.36040/jati.v8i1.8360

Rahmadini, R., Enjel Erika LorencisLubis, Aji Priansyah, Yolanda R.W.N, & Tuti Meutia. (2023). Penerapan Data Mining Untuk Memprediksi Harga Bahan Pangan Di Indonesia Menggunakan Algoritma K-Nearest Neighbor. Jurnal Mahasiswa Akuntansi Samudra, 4(4), 223–235. https://doi.org/10.33059/jmas.v4i4.7074

Rangga, M., Pratama, A., & Siswanti, S. (2024). The Application of Fuzzy Time Series Method for Web-Based Prediction of Household Chemical Product Sales Stock. 4(1).

Xin, Y., Kong, L., Liu, Z., Chen, Y., Li, Y., Zhu, H., Gao, M., Hou, H., & Wang, C. (2018). Machine Learning and Deep Learning Methods for Cybersecurity. IEEE Access, 6, 35365–35381. https://doi.org/10.1109/ACCESS.2018.2836950

Almufqi, F. M., & Voutama, A. (2023). PERBANDINGAN METODE DATA MINING UNTUK MEMPREDIKSI PRESTASI AKADEMIK SISWA. Jurnal Teknika, 15(1), 61–66. https://doi.org/10.30736/jt.v15i1.929

Aprianto, A., Kanedi, I., & Prahasti, P. (2023). Penerapan Metode Logika Fuzzy Dalam Analisis Kepuasan Mahasiswa Terhadap Sistem Perkuliahan Online. Jurnal Media Infotama, 19(2), 439–446. https://doi.org/10.37676/jmi.v19i2.4350

Gita Srihidayati, & Suhaeni. (2022). Analisis Pengaruh Sektor Pertanian terhadap Pertumbuhan Ekonomi. Wanatani, 2(1), 21–26. https://doi.org/10.51574/jip.v2i1.18

Lestari, V. A., Yuli Ananta, A., & Basudewa, P. (2023). SISTEM INFORMASI PREDIKSI PERSEDIAAN OBAT DI APOTEK NAYLUN FARMA MENGGUNAKAN HOLT-WINTERS. JIP (Jurnal Informatika Polinema).

Tama, A. A., & Saputro, D. R. S. (2022). Algoritme Average-Based Fuzzy Time Series Markov-Chain.PRISMA. Prosiding Seminar Nasional Matematika, 5, 711–715.

Yoka Fathoni, M., Wijayanto, S., Panjaitan No, J. DI, Purwokerto Selatan, K., Banyumas, K., & Tengah, J. (2021). Forecasting Penjualan Gas LPG di Toko Sembako Menggunakan Metode Fuzzy Time Series. JUPITER (Jurnal Penelitian Ilmu Dan Teknologi Komputer), 13(2), 87–96.

Downloads


Crossmark Updates

How to Cite

Fadillah, R., Ula, M., & Suwanda, R. (2025). Machine Learning to Predict Food Prices in Aceh Province Using the Fuzzy Time Series Method Based on Average. Sinkron : Jurnal Dan Penelitian Teknik Informatika, 9(2), 755-761. https://doi.org/10.33395/sinkron.v9i2.14649

Issue

Vol. 9 No. 2 (2025): Research Articles April 2025

Section

Articles

License

Copyright (c) 2025 Rizky Fadillah, Munirul Ula, Rizki Suwanda

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

Crossref
Scopus
Google Scholar
Europe PMC