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All Journal Parameter: Journal of Statistics Jurnal Riset Statistika
Komara Rifai, Nur Azizah
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Computer Science & IT Decision Sciences, Operations Research & Management Mathematics

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FORECASTING INFLATION IN INDONESIA USING THE AUTOREGRESSIVE INTEGRATED MOVING AVERAGE METHOD: PERAMALAN INFLASI DI INDONESIA MENGGUNAKAN METODE AUTOREGRESIVE INTEGRATED MOVING AVERAGE Komara Rifai, Nur Azizah; Zhahirulhaq, Mufdhil Afta
Parameter: Journal of Statistics Vol. 4 No. 1 (2024)
Publisher : Fakultas Matematika dan Ilmu Pengetahuan Alam Universitas Tadulako

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22487/27765660.2024.v4.i1.17130

Abstract

Indonesia faces significant economic challenges, particularly inflation, which affects the economic, social, and cultural sectors. High inflation can exacerbate poverty, alter consumption patterns, and contribute to social injustice, whereas low inflation can enhance national income and stimulate economic activities. Given its fluctuating nature, inflation in Indonesia requires accurate forecasting to inform policy-making and economic decisions. This study aims to forecast inflation in Indonesia for the next eight months using the Autoregressive Integrated Moving Average (ARIMA) method. Monthly inflation data from January 2020 to April 2024 obtained from Bank Indonesia were analyzed. The ARIMA model, suitable for short-term forecasting, was selected due to its ability to handle data trends, non-stationarity, and noise filtering. The Augmented Dickey-Fuller (ADF) and Kwiatkowski-Phillips-Schmidt-Shin (KPSS) tests to ensure stationarity. Initial ADF tests showed the presence of a unit root in the original data and the first differencing data, but data became stationary after the second differencing. The KPSS test confirmed a unit root in the original data and trend stationarity after the second and third differencing. Ordinary Least Squares (OLS) regression on the original data revealed a significant time trend, indicating deterministic trends. The optimal model identified was ARIMA(0,2,1) with AIC=51.81, as it met the criteria for normality, independence, and zero mean of residuals. This model effectively forecasts inflation from May to December 2024, which showed an increase with inflation values ​​of 3.02, 3.05, 3.07, 3.10, 3.12, 3.14, 3.17, and 3.19.
Penerapan Bayesian Dynamic Linear Models untuk Peramalan Harga Komoditas Beras Medium Ramdani, Mohamad Gilang; Komara Rifai, Nur Azizah
Jurnal Riset Statistika Volume 5, No. 2, Desember 2025, Jurnal Riset Statistika (JRS)
Publisher : UPT Publikasi Ilmiah Unisba

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29313/jrs.v5i2.8346

Abstract

Abstract. Medium‐quality rice prices in Indonesia play a crucial role in maintaining economic stability and national food security. The dynamic nature of price fluctuations, influenced by seasonal factors and long-term trend changes, requires a forecasting method that is adaptive. This study applies Bayesian Dynamic Linear Models (BDLM) to forecast medium-quality rice prices using monthly data from BPS and Bapanas for the period 2014-2023. The model consists of a second-order polynomial trend component and a harmonic seasonal component under a state space framework, updated sequentially using the Kalman filter. The results indicate that BDLM effectively captures variations in both trend and seasonality with high accuracy, as reflected by a MAPE value of 4.33%. These findings are consistent with previous studies, which highlight the superior adaptive capability of Bayesian dynamic models in handling structural changes in economic time series. Therefore, BDLM can serve as a reliable alternative for forecasting food commodity prices, particularly medium-quality rice, to support food policy formulation in Indonesia. Abstrak. Harga beras medium di Indonesia memiliki peran penting dalam menjaga stabilitas ekonomi dan ketahanan pangan nasional. Fluktuasi harga yang bersifat dinamis, dipengaruhi oleh faktor musiman dan perubahan tren jangka panjang, menuntut metode peramalan yang adaptif. Penelitian ini menerapkan Bayesian Dynamic Linear Models (BDLM) untuk meramalkan harga beras medium menggunakan data bulanan BPS dan Bapanas periode 2014-2023. Model yang digunakan terdiri atas komponen tren polinomial orde dua dan komponen musiman harmonik dengan pendekatan state space, serta diperbarui secara berurutan menggunakan Kalman filter. Hasil penelitian menunjukkan bahwa BDLM mampu menangkap variasi tren dan musiman secara efektif dengan tingkat akurasi yang tinggi, ditunjukkan oleh nilai MAPE sebesar 4,33%. Temuan ini sejalan dengan penelitian sebelumnya yang menyatakan bahwa model dinamis bayesian memiliki kemampuan adaptif yang unggul dalam memodelkan perubahan struktural pada deret waktu ekonomi. Dengan demikian, BDLM dapat dijadikan alternatif yang tepat dalam peramalan harga komoditas pangan, khususnya beras medium, guna mendukung formulasi kebijakan pangan di Indonesia.
Co-Authors Ramdani, Mohamad Gilang Zhahirulhaq, Mufdhil Afta