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All Journal BAREKENG: Jurnal Ilmu Matematika dan Terapan
Atika Ratna Dewi
Data Science Study Program, Telkom University, Purwokerto Campus, Indonesia
Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Energy Engineering Mathematics Mechanical Engineering Physics Transportation

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MULTIVARIATE TIME SERIES MODELING USING VECTOR AUTOREGRESSION FOR RICE PRICE PREDICTION IN INDONESIA Atika Ratna Dewi; Andreas Rony Wijaya; Mirza Ghanimi; Talitha Veda Azaria Ramadhani
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 20 No 3 (2026): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol20iss3pp2195-2212

Abstract

This study analyzes the dynamic relationship between rice prices and selected economic variables using a Vector Autoregression (VAR) model. The analysis utilizes daily data from January 2022 to December 2023, encompassing rice prices, chicken meat prices, chicken egg prices, the Rupiah-to-USD exchange rate, inflation, and crude oil prices. The estimated VAR model is stable, as all eigenvalues lie within the unit circle. Residual diagnostics based on the Portmanteau (Ljung–Box) test indicate no residual autocorrelation across all equations (LB statistics with df = 1, p-values > 0.05), confirming the adequacy of the model specification. The model demonstrates good predictive performance for the rice-price series, achieving a Mean Absolute Percentage Error (MAPE) of 0.42% over the out-of-sample testing period (the last 20% of observations). Empirical results suggest that rice prices are influenced by dynamic interactions within the system, particularly through their relationships with chicken meat prices and the Rupiah–USD exchange rate. These findings offer valuable policy insights for maintaining rice price stability, a crucial component of national food security.
A COMPARATIVE EVALUATION OF SARIMA AND FUZZY TIME SERIES CHEN MODELS FOR RAINFALL FORECASTING IN MAKASSAR Gavrilla Claudia; Atika Ratna Dewi; Aina Latifa Riyana Putri
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 20 No 2 (2026): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol20iss2pp1389-1404

Abstract

High rainfall intensity in Makassar often leads to flooding. Therefore, forecasting the amount of rainfall is necessary as a reference for taking appropriate mitigation measures. This study was conducted to select the best model between the SARIMA and Fuzzy Time Series (FTS) Chen based on a comparison of their forecasting accuracy, as well as to forecast the amount of rainfall in Makassar for 2024 using the best model. For this study, monthly rainfall data covering the period from January 2014 to December 2024 were collected from the official website of the Central Statistics Agency (BPS) Makassar. Based on the analysis results, SARIMA(7,2,3)(1,1,1)12 was selected as the best model, with an MAE value of 2.654 and an RMSE value of 3.846. The contribution of this study lies in providing an empirical comparison between SARIMA and FTS Chen for rainfall forecasting in tropical regions. However, the limitation of this study is that the forecasting relies solely on historical rainfall data, without incorporating other meteorological variables that may influence rainfall patterns.
Co-Authors Aina Latifa Riyana Putri Andreas Rony Wijaya Gavrilla Claudia Mirza Ghanimi Talitha Veda Azaria Ramadhani