Time-Series Forecasting of Red Chili Prices in Bogor City Indonesia
DOI:
https://doi.org/10.58526/jsret.v5i4.1276Keywords:
ARIMA, Bogor City, price forecasting, red chili prices, time seriesAbstract
Red chili is an important horticultural commodity in Indonesia, characterized by substantial price fluctuations that can affect household expenditure and food market stability. This study aims to forecast consumer-level red chili prices in Bogor City, Indonesia, using the Autoregressive Integrated Moving Average (ARIMA) method. The study uses 218 weekly price observations obtained from Statistics Indonesia (BPS) for the 2022–2026 period. The Box–Jenkins procedure was applied, including stationarity testing, model identification, parameter estimation, and diagnostic checking. The results indicate that the data became stationary after first-order differencing. Among four candidate models, ARIMA(3,1,1) was selected based on the lowest Akaike Information Criterion (AIC) and Hannan–Quinn Criterion (HQC), with values of 21.58900 and 21.61417, respectively. The model forecasts a gradual increase in red chili prices from 42,658 in the second week of February 2026 to 43,356 in the fourth week of December 2026, in the price units reported by BPS. These findings provide a local price outlook that may support consumer expenditure planning and market monitoring. However, forecast accuracy should be further evaluated using out-of-sample error measures, and future research may compare ARIMA with seasonal models and models incorporating external variables.Downloads
References
Andayani, S., Ismail, A., Permana, N., Nainggolan, M., & Sembiring, S. (2025). Price predictions on red chili (Capsicum annuum) commodities in Indonesia. African Journal of Food, Agriculture, Nutrition and Development, 25(5), 26600–26628. https://doi.org/10.18697/ajfand.142.24495
Chung, S. Y., Senapathi, V., Sekar, S., & Kim, T. H. (2018). Time series analyses of hydrological parameter variations and their correlations at a coastal area in Busan, South Korea. Hydrogeology Journal, 26(6), 1875–1885. https://doi.org/10.1007/s10040-018-1739-9
Falah, R. A., & Rachmaniah, M. (2022). Price Prediction Model for Red and Curly Red Chilies using Long Short Term Memory Method. Indonesian Journal of Statistics and Its Applications, 6(1), 143–160. https://doi.org/10.29244/ijsa.v6i1p143-160
Hegde, G., Hulipalled, V. R., & Simha, J. B. (2024). Fuzzified input data tuning for agriculture commodities price prediction. IAES International Journal of Artificial Intelligence (IJ-AI), 13(3), 2725. https://doi.org/10.11591/ijai.v13.i3.pp2725-2735
Mathenge Mutwiri, R. (2019). Forecasting of Tomatoes Wholesale Prices of Nairobi in Kenya: Time Series Analysis Using Sarima Model. International Journal of Statistical Distributions and Applications, 5(3), 46. https://doi.org/10.11648/j.ijsd.20190503.11
Maulana, O., Indra, I., & Kasimin, S. (2023). Price transmission analysis of red chili peppers (capsicum annuum l.) In aceh province. International Journal of Economic Business Accounting Agriculture Management and Sharia Administration (IJEBAS), 3(2), 605–620. https://doi.org/10.54443/ijebas.v3i2.739
Nurjati, E. (2021). Price volatility of red chili peppers in central java. Agrisocionomics: Jurnal Sosial Ekonomi Pertanian, 5(2), 152–167. https://doi.org/10.14710/agrisocionomics.v5i2.9754
Prihandi, I., Wijono, S., Sembiring, I., & Maria, E. (2025). Implementation of ARIMA with Min-Max Normalization for predicting the Price and Production Quantity of Red Chili Peppers in North Sumatra Province considering Rainfall and Sunlight Duration Factors. Engineering, Technology & Applied Science Research, 15(2), 21876–21887. https://doi.org/10.48084/etasr.9875
Rizvi, M. F. (2024). ARIMA Model Time Series Forecasting. International Journal for Research in Applied Science and Engineering Technology, 12(5), 3782–3785. https://doi.org/10.22214/ijraset.2024.62416
Septarena, R., Arianti, N. N., & Sumantri, B. (2025). The forecasting of curly red chili price (capsicum annum l.) In bengkulu city. Journal of Agri Socio Economics and Business, 7(1). https://doi.org/10.31186/jaseb.07.1.57-72
Sonia, T., Karyani, T., & Susanto, A. (2019). Allocative efficiency analysis of red chili farming in sukalaksana village banyuresmi district garut regency. MIMBAR Agribisnis, 6(1). https://doi.org/http://dx.doi.org/10.25157/ma.v6i1.2613
Tami, M., & Owda, A. Y. (2024). Efficient commodity price forecasting using long short-term memory model. IAES International Journal of Artificial Intelligence (IJ-AI), 13(1), 994. https://doi.org/10.11591/ijai.v13.i1.pp994-1004
Windhy, A., & Jamil, A. (n.d.). Forecasting Indonesian Red Chilli Prices: The ARIMA Approach. AGRIEKSTENSIA, 20(1). https://doi.org/https://doi.org/10.34145/agriekstensia.v20i1.1502
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