[2] A. Junaedi, Analisis Data Cabai Provinsi Jawa Timur 2019. Badan Pusat Statistik Provinsi Jawa Timur, 2019.
[3] M. A. Rofiq, “Peramalan Komoditas Strategis Pertanian Cabai Menggunakan Metode Backpropagation Neural Network,” Institut Teknologi Sepuluh Nopember Surabaya, 2017.
[4] Y. Swastika, Syamsinar, and D. A. U. Mega, “Analisis Faktor-Faktor yang Mempengaruhi Fluktuasi Harga (Studi Kasus Di Pasar Niaga Daya ),” Agribus. Socioecon. J., vol. 1, no. 1, pp. 12–20, 2022, doi: https://doi.org/10.59638/asejournal.v1i01.18.
[5] D. Nauly, “Fluktuasi dan Disparitas Harga Cabai di Indonesia,” J. Agrosains dan Teknol., vol. 1, no. 1, pp. 56–69, 2016, doi: 10.24853/jat.1.1.57-70.
[6] B. R. P. M. Basnayake, K. D. Kaushalya, R. H. M. Wickaramarathne, M. A. K. Kushan, and N. V. Chandrasekara, “An Approach for Prediction of Weekly Prices of Green Chili in Sri Lanka: Application of Artificial Neural Network Techniques,” J. Agric. Sci. - Sri Lanka, vol. 17, no. 2, pp. 333–349, 2022, doi: http://doi.org/10.4038/jas.v17i2.9746.
[7] F. Petropoulos et al., “Forecasting: theory and practice,” Int. J. Forecast., vol. 38, no. 3, pp. 705–871, 2022, doi: 10.1016/j.ijforecast.2021.11.001.
[8] Y. Wang, Y. Lei, X. Fan, and Y. Wang, “Intuitionistic Fuzzy Time Series Forecasting Model Based on Intuitionistic Fuzzy Reasoning,” Math. Probl. Eng., vol. 2016, pp. 1–12, 2016, doi: 10.1155/2016/5035160.
[9] W. Qiu, X. Liu, and H. Li, “A generalized method for forecasting based on fuzzy time series,” Expert Syst. Appl., vol. 38, no. 8, pp. 10446–10453, 2011, doi: 10.1016/j.eswa.2011.02.096.
[10] M. Muhammad, S. Wahyuningsih, and M. Siringoringo, “Peramalan Nilai Tukar Petani Subsektor Peternakan Menggunakan Fuzzy Time Series Lee,” Jambura J. Math., vol. 3, no. 1, pp. 1–15, 2021, doi: 10.34312/jjom.v3i1.5940.
[11] H. Ahyar et al., Buku Metode Penelitian Kualitatif dan Kualitatif, Cetakan I., no. April. Yogyakarta: CV. Pustaka Ilmu Group Yogyakarta, 2020.
[12] L. H. Ajuna, H. H. Dukalang, and M. Ardi, “Bank Syariah Indonesia Share Price Prediction Using Fuzzy Time Series Model Lee Method,” Madania J., vol. 25, no. 2, pp. 233–242, 2021.
[13] P. Singh, “A Brief Review of Modeling Approaches Based on Fuzzy Time Series,” Int. J. Mach. Learn. Cybern., vol. 8, no. 2, pp. 397–420, 2017, doi: 10.1007/s13042-015-0332-y.
[14] D. Devianto, A. Zuardin, and Maiyastri, “Time Series Modeling of Natural Gas Future Price With Fuzzy Time Series Chen, Lee and Tsaur,” BAREKENG J. Math. Its Appl., vol. 16, no. 4, pp. 1185–1196, 2022, doi: doi.org/10.30598/barekengvol16iss4pp1185-1196.
[15] A. Al Yammahi and Z. Aung, “Forecasting the Concentration of NO2 Using Statistical and Machine Learning Methods: A Case Study in the UAE,” Heliyon, vol. 9, no. 2, pp. 1–16, 2022, doi: 10.1016/j.heliyon.2022.e12584.
- Abstract viewed - 322 times
- PDF downloaded - 306 times
Affiliations
Vida Komaria
Universitas Jember
Nova El Maidah
Universitas Jember
Muhammad Ariful Furqon
Universitas Jember
How to Cite
Prediksi Harga Cabai Rawit di Provinsi Jawa Timur Menggunakan Metode Fuzzy Time Series Model Lee
Vol 12 No 2 (2023): Komputika: Jurnal Sistem Komputer
Abstract
ABSTRACT - East Java is the province with the most significant amount of chili pepper production in Indonesia based on data from BPS in 2021 which is around 41.75. Chili pepper is a commodity that high price fluctuations that will impact several parties, so a mechanism is needed to predict the price of chili pepper to become a consideration in decision making. Lee's fuzzy time series method can be used to predict time series stationary or non-stationary data. The research was conducted using historical data on the price of red and green chili peppers in East Java Province from April 2017 to February 2023 with a weekly data period of 307 data. The Z1 and Z2 values used to get the smallest error results are Z1 = 950 and Z2=400 for red chili peppers while for green chili peppers values the Z1 and Z2=100. The error value of forecasting red chili pepper prices is MAE = 4,469.04 RMSE = 6,138.64 MAPE = 13.09% (good MAPE value category) and the error value for green chili pepper is MAE = 1,486.15 RMSE = 2,211.06 and MAPE = 6.72% (very good MAPE value category).
Keywords – forecasting; Lee’s fuzzy time series; chili pepper price; MAPE; Python