Forecasting Rice Price with Double Exponential Smoothing and Fuzzy Time Series Methods (Case Study: Price of Rice in Mataram City)
DOI:
https://doi.org/10.29303/emj.v5i2.123Keywords:
Double Exponential Smoothing Holt, Fuzzy Time Series Cheng, Rice Price , MAPE, MSEAbstract
Rice has become the main staple food for almost the entire population of Indonesia. However, in Indonesia, the price of food commodities (rice) often fluctuates in price. Due to the rapid fluctuation of rice prices and the uncertainty in the future, it is necessary to forecast rice prices. This study aims to predict the price of rice in the city of Mataram using the Holt double exponential smoothing method and the Cheng fuzzy time series. The model's performance is based on Mean Squared Error (MSE) and Mean Absolute Percentage Error (MAPE) indicators. Forecasting model based on Holt's double exponential smoothing method, the MSE value is 705967.4994 and the MAPE value is 7.91%. On the other hand, based on Cheng's fuzzy time series method, the performance of the forecasting model based on the MSE indicator is 627400.307 and based on the MAPE value of 7.39%. Based on these results, Cheng's fuzzy time series method is more accurate than Holt's double exponential smoothing method.References
BPS. (2017). Kota Mataram dalam Angka, Mataram.
Fahmi, T., Sudarno, & Wulandari, Y. (2013). Perbandingan Metode Pemulusan Eksponensial Tunggal dan Fuzzy Time Series untuk Memprediksi Indeks Harga Saham Gabungan. Jurnal Gaussian. Vol. 2. No. 2. Halaman 137-146.
Harsyiah, L., Fitriyani, N., & Salwa. (2020). Peramalan Jumlah Siswa Baru Madrasah Aliyah (MA) Manhalul Ma’arif Darek-Lombok Tengah. Eigen Mathematics Journal. Vol. 3. No. 2. Halaman 110 – 117.
Inayah. (2010). Tesis. Perbandingan Metode Holt dan Brown pada Double Exponential Smoothing.
Makridakis, S., Wheelwright, S. C., & Mcgee, V. E. (1999). Metode dan Aplikasi Peramalan. Edisi Kedua. Jakarta: Binarupa Aksara.
Mayang S., Yesy. (2020). Skripsi. Penerapan Metode Holt-Winters’ Additive Exponential Smoothing untuk Peramalan (forecasting) Harga Bawang Merah di Yogyakarta.
Ola, P. K., & Kartiko. (2019). Peramalan Menggunakan Metode Fuzzy Time Series Cheng dan Double Exponential Smoothing (Study Kasus: Jumlah Wisatawan Mancanegara di Candi Brobudur). Jurnal Statistika Industri dan Komputasi. Vol. 4. No. 1. Halaman 69-79.
Tauryawati, M. L., & Irawan, M. Isa. (2014). Perbandingan Metode Fuzzy Time Series Cheng dan Box-Jenkins untuk memprediksi IHSG. Jurnal Sains dan Seni Pomits Vol. 3. No. 2.
Rahmawati, Cynthia, E. P., & Susilowati, K. (2019). Metode Fuzzy Time Series Cheng dalam Memprediksi Jumlah Wisatawan di Provinsi Sumatra Barat. Journal of Education Informatic Technology and Science. Vol. 1. No. 1. Halaman 11-23.
Sumartini, Hayati, M. N., & Wahyuningsih, S,. (2017). Peramalan Menggunakan Metode Fuzzy Time Series Cheng. Jurnal Eksponensial. Vol. 8. No. 1. Halaman 51-56.
Sumaryanto. (2009). Analisis Volatilitas Harga Eceran Beberapa Komoditas Pangan Utama dengan Model ARCH/GARCH. Jurnal Agro Ekonomi. Vol. 27. No. 2. Halaman 135-163.
Uliana. (2017). Skripsi. Penerapan Metode Average - Based Fuzzy Time Series pada Pergerakan Data Harga Miyak.
Xihao, S., & Yimin, L. (2008). Average-Based Fuzzy Time Series Models for Forecasting Shanghai Compound Index. World Journal of Modelling and Simulation. Vol. 4. Halaman 104-111.
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