Forecasting Farmer Exchange Rate (FER) in Southeast Sulawesi Province Using Cheng’s Fuzzy Time Series Method
DOI:
https://doi.org/10.34123/jurnalasks.v17i2.801Keywords:
Fuzzy Time Series, Fuzzy Time Series Cheng, FER, MAPE, ForecastingAbstract
Introduction/Main Objectives: This study aims to forecast the Farmer Exchange Rate (FER) in Southeast Sulawesi Province for 2024 as a basis for short-term economic assessment and policy-related analysis. Background Problems: FER is a key indicator of farmers’ purchasing power and agricultural welfare; however, its monthly dynamics are characterized by fluctuations and uncertainty, making conventional forecasting methods less effective in capturing its behavior. Novelty: This study contributes by implementing the the Fuzzy Time Series (FTS) Cheng approach for FER forecasting in Southeast Sulawesi, emphasizing its suitability for handling vagueness and nonlinear patterns inherent in agricultural economic indicators. Research Methods: The analysis utilizes monthly secondary FER data obtained from BPS-Statistics of Southeast Sulawesi Province, covering the period from January 2014 to December 2023. Forecast accuracy is evaluated using the Mean Absolute Percentage Error (MAPE). Finding/Results: The forecasting results indicate that the FER values for January, February, and March 2024 are each estimated at 105.93. The model achieved a MAPE of 0.3027%, corresponding to an accuracy level of 99.6973%, which places the forecasting performance in the “excellent” category.
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References
M. Arumsari. A. Tri. and R. Dani. “Peramalan data runtun waktu menggunakan model hybrid time series regression – Autoregressive integrated moving average.” vol. 02. no. 01. pp. 1–12. 2021.
Noviani. “Perbandingan metode fuzzy time series markov chain dan fuzzy time series cheng (Studi kasus: Rata-rata harga beras).” Universitas Islam Negeri Maulana Malik Ibrahim.. 2020.
C. Keumala and Z. Zainuddin. “Indikator kesejahteraan petani melalui nilai tukar petani ( NTP ) dan pembiayaan syariah sebagai solusi Cut Muftia Keumala Zamzami Zainuddin pendahuluan salah satu sumber kebutuhan utama manusia berasal dari sektor.” vol. 9. pp. 129–149. 2018.
BPS. “Statistik Nilai Tukar Petani Provinsi Sulawesi Tenggara Triwulan III.” 2023.
Purba. S. F.. Yulianti. A.. Raphael. Y.. & Khotimah. H. (2024). Determinan kesejahteraan petani tanaman pangan di Provinsi Jawa Barat. Jurnal Ilmu Pertanian Indonesia. 29(1). 59–67.
Rahmah. P. A.. Hayati. M. N.. Cahyaningsih. A. (2025). Peramalan nilai tukar petani Kalimantan Timur menggunakan metode neural network. Indonesian Journal of Applied Statistics and Data Science. 2(1).
Kuddi. B. F and A. D. Saputro (2024). Peramalan nilai tukar petani di Provinsi Papua menggunakan model hybrid ARIMA-NN. SAINTIFIK: Jurnal Matematika. Sains. dan Pembelajarannya. 10(1). 135-140
M. I. Riyadh. “Analisis nilai tukar petani komoditas tanaman pangan di Sumatera Utara ( Analysis of farmers term of trade of crops commodities in North Sumatra ).” no. 191. pp. 17–32. 2015.
L. N. Mustika. “Perbandingan metode fuzzy time series cheng dan markov chain pada peramalan nilai tukar petani di Indonesia.” Universitas Islam Negeri Maulana Malik Ibrahim.. 2021.
M. Muhammad et al.. “Peramalan nilai tukar petani subsektor peternakan menggunakan fuzzy time series lee.” vol. 3. no. 1. pp. 1–15. 2021.
Sumartini. M. N. Hayati. and S. Wahyuningsih. “Peramalan menggunakan metode fuzzy time series cheng forecasting using fuzzy time series cheng method.” vol. 8. pp. 51–56. 2017.
A. R. Prayogi. “Demand forecasting penggunaan energi listrik (Kwh) menggunakan fuzzy time series cheng.” Universitas Islam Indonesia. 2018.
M. L. Tauryawati and M. I. Irawan. “Perbandingan metode fuzzy time series cheng dan metode box-jenkins untuk memprediksi.” vol. 3. no. 2. 2014.
Prayetno. R. S.. F. Rakhmawati. (2025). Comparison of the cheng and lee fuzzy time series methods for predicting clean water supply in Asahan Regency. Jatilima: Jurnal Multimedia dan Teknologi Informasi 7(3). 507-515.
Tursina. R. Septiriana. & I. Varian. (2023). Prediksi indeks harga konsumen menggunakan metode fuzzy time serises cheng. Jurnal Locus: Penelitian & Pengabdian 2(1). 51-59.
Putri. I. A.. N. E. Maidah.. & M. A. Furqon. (2024). Penerapan metode fuzzy time series cheng pada peramalan inflasi di Indonesia. Komputika: Jurnal Sistem Komputer. 13(2). 183-191.
Amalani. M. Z. B.. Santoso. N. A.. & Syefudin. (2025). Perbandingan metode ARIMA dan fuzzy time series dalam peramalan harga eceran daging sapi di Indonesia. RIGGS: Journal of Artifical Intelligence and Digitas Business. 4(3). 2232-2242.
Ella. A.P.. & Deni. A. (2020). Penerapan fuzzy time series dalam peramalan harga minyak serai pada CV Agam Jaya Atsiri. Jember: Fakultas Teknik Universitas Muhammadiyah Jember.
T. Fahmi. Sudarno. and Y. Wilandari. “Perbandingan metode eksponensial tunggal dan fuzzy time series untuk memprediksi indeks harga saham gabungan.” Gaussian. vol. 2. no. 2. pp. 137–146. 2013.
A. Fadhillah. B. Martalelli. and N. Rita. “Perbandingan model chen dan model cheng pada algoritma fuzzy time series untuk prediksi harga bahan pokok.” Universitas Maritim Raja Ali Haji. 2017.
Bidin. J.. dkk. (2022). Cheng fuzzy time series model to forecast the price of crude oil in Malaysia. Journal of Computing Research and Innovation (JCRINN). 7(2). 196-210.
