Predicting Consumer Price Index Movements with SARIMA: A Case from Kediri City
DOI:
https://doi.org/10.24191/jibe.v10i2.8620Keywords:
Akaike Information Criterion, Consumer Price Index, SARIMA, StationarityAbstract
The city of Kediri plays a significant role in driving the regional economy. One factor that plays an important role in regional economic stability is the Consumer Price Index (CPI). This study aims to predict the Consumer Price Index (CPI) of Kediri City using the Seasonal ARIMA (SARIMA) model, taking into account seasonal patterns. The data used consists of monthly CPI secondary data from January 2020 to December 2023 obtained from the Central Statistics Agency (BPS). The analysis was conducted through several stages, including a stationarity test using the Augmented Dickey-Fuller (ADF) method, model identification using ACF and PACF, parameter estimation, model selection based on the Akaike Information Criterion (AIC) value, and forecasting. The results of the study indicate that the SARIMA(2,2,0)(1,0,0)[12] model has the lowest AIC value. With a narrow confidence interval and a stable trend, this model can accurately predict the CPI for 2024. The forecasting results show that the CPI continues to increase each year. It is hoped that these findings will contribute academically to seasonal time series modelling and assist the government and businesses in developing data-driven economic strategies in area.
References
Afiyah, S. N., & Wijaya, D. K. (2018). Sistem peramalan indeks harga konsumen (IHK) menggunakan metode double exponential smoothing. Jurnal Ilmiah Teknologi Informasi Asia, 12(1), 56–64. https://doi.org/10.32815/jitika.v12i1.243
Aktivani, S. (2020). Uji stasioneritas data inflasi kota padang. Statistika, 20(2), 83–90.
Dabral, P. P., & Murry, M. Z. (2017). Modelling and forecasting of rainfall time series using sarima. Environmental Processes, 4(2), 399–419. https://doi.org/10.1007/s40710-017-0226-y
Dewi, A. F., & Ahadiyah, K. (2022). Agglomerative hierarchy clustering pada penentuan kelompok kabupaten/kota di jawa timur berdasarkan indikator pendidikan. Zeta - Math Journal, 7(2), 57–63. https://doi.org/10.31102/zeta.2022.7.2.57-63
Dimashanti, A. R., & Sugiman. (2021). Peramalan indeks harga konsumen kota semarang menggunakan sarima berbantuan software minitab. Prisma, Prosiding Seminar Nasional Matematika, 4, 565–576. https://journal.unnes.ac.id/sju/index.php/prisma/
Faradis, N., Ainya, N., Fauzah, A., Ichsan, M., & Anshori, A. (2023). Media sosial dan persepsi publik: analisis strategi kampanye digital calon presiden indonesia 2024. Prosiding Seminar Nasional, 643–652.
Fathurahman, M. (2017). Pemilihan model regresi terbaik menggunakan metode akaike’s information criterion dan schwarz information criterion. Jurnal Informatika Mulawarman, 4(3), 37–41.
Ganessa. N, A, P., Alphenia, S., Zanuarizqi, A, P., & Widodo, E. (2021). Analisis faktor yang mempengaruhi indeks harga konsumen. Jurnal Khazanah, 13(1), 14–23. https://doi.org/10.20885/khazanah.vol13.iss1.art2
Irfansyah, R. A. (2024). pengaruh inflasi terhadap laju pertumbuhan ekonomi di kota kediri tahun 2020-2022. Jurnal Ilmiah Wahana Pendidikan. 10(19), 784–794.
Kristinae, V. (2018). Analisis pengaruh indeks harga konsumen terhadap inflasi (studi kasus pada inflasi kota palangka raya dan kab. sampit di kalimantan tengah. Jurnal Aplikasi Manajemen, Ekonomi Dan Bisnis, 3(1), 1–11.
Lin, Y., & Chen, S. (2021). A centroid auto-fused hierarchical fuzzy c-means clustering. IEEE Transactions on Fuzzy Systems, 29(7), 2006–2017. https://doi.org/10.1109/TFUZZ.2020.2991306
Lukmaini, S., Nugraheni, K., & Istiqomah, N. (2023). Peramalan inflasi provinsi kalimantan timur tahun 2016-2022 menggunakan metode seasonal autoregressive integrated moving average (SARIMA). Prosiding Seminar Nasional Matematika, Statistika, Dan Aplikasinya, III, 80–89.
Ma’in, M., Nordin, N., Zailan, . I. H., Sulaiman, S., & Ismail, Z. (2018). Investment and Economic Indicators in Malaysia. Journal of International Business, Economics and Entrepreneurship, 3(2), 23. https://doi.org/10.24191/jibe.v3i2.14429
Nafisah, N., & Respatiwulan, R. (2019). Analisis faktor indeks harga konsumen kota semarang. Indonesian Journal of Applied Statistics, 2(2), 113. https://doi.org/10.13057/ijas.v2i2.34903
Noor, H. S., & Komala, C. (2019). Analisis indeks harga konsumen (ihk) menurut kelompok pengeluaran nasional tahun 2018. Jurnal Perspektif, 3(2), 110. https://doi.org/10.15575/jp.v3i2.48
Rizki, M. I., & Taqiyyuddin, T. A. (2021). Penerapan model sarima untuk memprediksi tingkat inflasi di indonesia. Jurnal Sains Matematika Dan Statistika, 7(2). https://doi.org/10.24014/jsms.v7i2.13168
Rosdianawati, R., & Surjanto, S. D. (2023). Peramalan inflasi kota kediri berdasarkan indeks harga konsumen menggunakan metode exponential smoothing. Jurnal Sains Dan Seni ITS, 12(1). https://doi.org/10.12962/j23373520.v12i1.91757
Ruhiat, D., & Effendi, A. (2018). Pengaruh faktor musiman pada pemodelan deret waktu untuk peramalan debit sungai dengan metode sarima. Teorema, 2(2), 117. https://doi.org/10.25157/.v2i2.1075
Sharma, A., Sharma, D., Panda, S. K., Sunder, M. S. S., & Dubey, K. S. (2024). Seasonal analysis of long-term (1970–2020) rainfall variability using clustering and wavelet transform approach in the Mahi River Basin, India. Acta Geophysica, 72(3), 1879–1894. https://doi.org/10.1007/s11600-023-01094-5
Togatorop, A. M. H., Darmawan, D. W., Hidayati, R. (2024). Transformasi digital dalam mencapai keberlanjutan di bidang ekonomi dan keuangan. Manajemen Business Innovation Conference-MBIC, 7, 16. https://jurnal.untan.ac.id/index.php/MBIC/index
Wahyuningtyas, A. R., Pratiwii, W. P., Wasono, R., & Utami, T. W. (2021). Peramalan indeks harga konsumen kabupaten banyumas dengan metode sarima. Jurnal Litbang Edusaintech (JLE), 3(1), 56–60. https://doi.org/10.51402/jle.v3i1.77
Yahya, A. (2022). Peramalan indeks harga konsumen indonesia menggunakan metode seasonal-arima (sarima). Jurnal Gaussian, 11(2), 313–322. https://doi.org/10.14710/j.gauss.v11i2.35528
Yusuf, N. H. M., Hilmi, N. A. M., Abdoh, W. M. Y. M., Zain, R. S., & Shah, N. S. B. (2020). Determinants of macroeconomic variables on islamic stock index: evidence from frontier market. Journal of International Business, Economics and Enterpreneurship, 5(1), 23–29. https://doi.org/10.24191/jibe.v5i1.14288
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