Cayenne Pepper Price Forecast in Singkawang City Based on Rainfall using Transfer Function Model

Citra Cipta Maharani; Yundari Yundari; Neva Satyahadewi

doi:10.30598/pijmathvol4iss2pp53-62

Citra Cipta Maharani Mathematics Study Program, Faculty of Mathematics and Natural Sciences, Universitas Tanjungpura, Indonesia
Yundari Yundari Mathematics Study Program, Faculty of Mathematics and Natural Sciences, Universitas Tanjungpura, Indonesia
Neva Satyahadewi Statistics Study Program, Faculty of Mathematics and Natural Sciences, Universitas Tanjungpura, Indonesia

DOI: https://doi.org/10.30598/pijmathvol4iss2pp53-62

Keywords: Price fluctuations; Price stabilization; Prewhitening; MAPE

Abstract

Fluctuations in the price of cayenne pepper are a significant problem in Indonesia’s agricultural sector, especially in Singkawang City. Weather conditions, including rainfall are often the main factor affecting the production and distribution of cayenne pepper, causing price instability. This study aims to analyze the relationship between rainfall and the price of cayenne pepper, and build a forecasting model using a transfer function approach. In this study, the input series used is rainfall, while the output series is the price of cayenne pepper. The data used is secondary data obtained from the Central Statistics Agency in Singkawang City from January 2016 to December 2023. The data is analyzed through the stationarity stage, then the identification of the ARIMA model for the input series. After that, prewhitening and cross-correlation analysis were carried out to identify the parameter values and determine the noise series ARMA model. The results show that the transfer function model with parameters with ARMA noise series is the best model for forecasting the price of cayenne pepper. The results of forecasting the price of cayenne pepper in Singkawang City have a MAPE value of , so it can be concluded that the transfer function model is quite good at forecasting the price of cayenne pepper in Singkawang City with the highest forecasting result of IDR 61,899 in May 2024 and the lowest is IDR 32,206 in April 2024. This study focuses solely on the transfer function model because it is specifically designed to analyze the dynamic relationship between an input variable (rainfall) and an output variable (price). Other forecasting methods such as ARIMA or exponential smoothing only capture internal patterns within a single series and cannot represent the influence of external factors. Therefore, the transfer function approach is considered more appropriate for the purpose of this study.

Downloads

Download data is not yet available.

References

N. . . Palar, P. A. . Pangemanan, and E. G. . Tangkere, “Faktor-Faktor Yang Mempengaruhi Harga Cabai Rawit Di Kota Manado,” Agri-Sosioekonomi, vol. 12, no. 2, p. 105, 2016, doi: 10.35791/agrsosek.12.2.2016.12278.

K. Tampubolon and F. N. Sihombing, “Pengaruh Curah Hujan dan Hari Hujan Terhadap Produksi Pertanian serta Hubungannya Dengan PDRB Atas Harga Berlaku di Kota Medan,” J. Pembang. Perkota., vol. 5, no. 1, pp. 35–41, 2017, [Online]. Available: http://ejpp.balitbang.pemkomedan.go.id/index.php/balitbangpemkomedan/issue/view/1/Koko Tampubolon %3A Fransisca Natalia Sihombing

N. Almumtazah, N. Azizah, Y. L. Putri, and D. C. R. Novitasari, “Prediksi Jumlah Mahasiswa Baru Menggunakan Metode Regresi Linier Sederhana,” J. Ilm. Mat. Dan Terap., vol. 18, no. 1, pp. 31–40, 2021, doi: 10.22487/2540766x.2021.v18.i1.15465.

M. G. Ahmad and M. Prastuti, “Peramalan Harga Cabai Rawit di Kabupaten Tuban Berdasarkan Curah Hujan Menggunakan Fungsi Transfer,” J. Sains dan Seni ITS, vol. 11, no. 6, pp. 2337–3520, 2022, doi: 10.12962/j23373520.v11i6.89310.

E. Paradita, A. Agoestanto, and P. Hendikawati, “Pemodelan Fungsi Transfer Untuk Meramalkan Tingkat Inflasi Indonesia,” UNNES J. Math., vol. 7, no. 1, pp. 57–65, 2018, [Online]. Available: http://journal.unnes.ac.id/sju/index.php/ujm

N. Draper and S. H. Smith, Analisis Regresi Terapan. Jakarta: Gramedia Pustaka, 1998.

K. Wijayanti, S. Martha, and N. N. Debataraja, “Perbandingan Model ARIMAX dan Fungsi Transfer pada Peramalan Curah Hujan,” Bul. Ilm. Math. Stat. dan Ter., vol. 10, no. 2, pp. 233–242, 2021.

S. Assauri, Teknik dan Metode Peramalan. Penerapannya Dalam Ekonomi dan Dunia Usaha Edisi Satu. Jakarta: LP Fakultas Ekonomi, 1984.

N. M. Huda and N. Imro’ah, Pengantar Analisis Deret Waktu Studi Kasus: Data Kalimantan Barat dengan Aplikasi R. Pontianak: UNTAN PRESS, 2023.

I. K. P. ADNYANA, I. W. SUMARJAYA, and I. K. G. SUKARSA, “Peramalan Jumlah Kunjungan Wisatawan Mancanegara Yang Bekunjung Ke Bali Menggunakan Fungsi Transfer,” E-Jurnal Mat., vol. 5, no. 4, p. 139, 2016, doi: 10.24843/mtk.2016.v05.i04.p133.

W. W. S. Wei, Time Series Analysis : Univariate and Multivariate Methods, Second. United States of America: Greg Tobin, 2006.

Indra, M. N. Mara, and S. Martha, “Peramalan Curah Hujan di Kabupaten Kubu Raya dengan Menggunakan Model Fungsi Transfer Multivariat,” Bimaster Bul. Ilm. Mat. Stat. dan Ter., vol. 4, no. 3, pp. 191–200, 2015.

D. C. Montgomery, C. L. Jennings, and M. Kulahci, Introduction to Time Series Analysis and Forecasting, vol. 11, no. 1. United States of America: A John Wiley & Sons, Inc., Publication, 2008. [Online]. Available: http://scioteca.caf.com/bitstream/handle/123456789/1091/RED2017-Eng-8ene.pdf?sequence=12&isAllowed=y%0Ahttp://dx.doi.org/10.1016/j.regsciurbeco.2008.06.005%0Ahttps://www.researchgate.net/publication/305320484_SISTEM_PEMBETUNGAN_TERPUSAT_STRATEGI_MELESTARI