The Forecasting Result Study of the Poverty Line and Number of Poor Population in DIY using DES and ARIMA
DOI:
https://doi.org/10.20956/j.v21i2.36734Keywords:
ARIMA, Poverty Line, Double Exponential SmoothingAbstract
The poverty rate in DIY, based on BPS data, stands at 11.04%, which remains above the national average of 9.36%. This study aims to predict poverty patterns in the Special Region of Yogyakarta (DIY) using DES and ARIMA methods. The data utilized in this research is sourced from BPS, focusing on poverty line data and the number of impoverished individuals. The DES model is employed to estimate the increase in the poverty line, demonstrating good accuracy with a MAPE value of 2.968%. Meanwhile, the ARIMA(0,2,1) model is applied to forecast a reduction in the number of impoverished individuals, yielding a MAPE of 3.543% through 2028. The findings of this study indicate that government policies have had a positive impact on reducing poverty, although challenges remain. The results of this analysis are expected to guide policymakers in crafting more effective and targeted poverty alleviation strategies in the DIY region. These findings suggest that government policies have had a positive impact on reducing poverty, despite ongoing challenges.
References
[1] Adji, A., Hidayat, T., Tuhiman, H., Kurniawati, S., Maulana, A., & kurniawati, 2020, Pengukuran Garis Kemiskinan di Indonesia: Tinjauan Teoritis dan Usulan Perbaikan. Jakarta Pusat: Tim Nasional Percepatan Penanggulangan Kemiskinan.
[2] Agustine, V., Indra, Z., & Nasution, H., 2022, Implementation of Double Exponential Smoothing Holt Method in Forecasting Commercial Rice Sales in Perum Bulog Sub Divre Medan, Zero: Jurnal Sains, Matematika, dan Terapan, Vol. 6, No. 2, 53–59.
[3] Atussaliha, N. A., Purnawansyah, P., & Darwis, H., 2020, Metode Double Exponential Smoothing pada Sistem Peramalan Tingkat Kemiskinan Kabupaten Pangkep, ILKOM Jurnal Ilmiah, Vol. 12, No. 3, 183–190.
[4] Baniadi, P. & Mustofa, 2018, Kebijakan Pengentasan Kemiskinan di Daerah Istimewa Yogyakarta (Government Policy to Reduce Poverty in the Special Region of Yogyakarta).
[5] BAPPEDA Yogyakarta, 2021, Rencana Pembangunan Jangka Menengah Daerah (RPJMD) Daerah Istimewa Yogyakarta 2022-2027. Yogyakarta.
[6] BPS DIY, 2022, Profil Kemiskinan Daerah Istimewa Yogyakarta.”
[7] Fadliani, I. & Purnamasari, I., 2021, Peramalan dengan Metode SARIMA Pada Data Inflasi dan Identifikasi Tipe Outlier (Studi Kasus: Data Inflasi Indonesia Tahun 2008-2014).
[8] Han, Y., Liu, L., Sui, Q., & Zhou, J., 2021, Big Data Spatio-Temporal Correlation Analysis and LRIM Model Based Targeted Poverty Alleviation through Education, ISPRS Int J Geoinf, Vol. 10, No. 12.
[9] Handayani, R. & Hidayat, P., 2020, Analisis Faktor-Faktor yang Mempengaruhi Kemiskinan di Provinsi Daerah Istimewa Yogyakarta, Jurnal Ekonomi & Studi Pembangunan, 37–52.
[10] Hasri, D., 2020, Metode Autoregresive Integrated Moving Average (ARIMA) untuk Peramalan Tingkat Kemiskinan di Kabupaten Sumbawa, Jurnal Riset Kajian Teknologi & Lingkungan, Vol. 3, No. 2, 196–202.
[11] Kartikasari, M. D., 2020, PPT Exponential Smoothing.
[12] Lewis, C. D., 1982, Industrial and Business Forecasting Methods: A Practical Guide to Exponential Smoothing and Curve Fitting. Butterworth-Heinemann.
[13] Muchayan, A., 2019, Comparison of Holt and Brown’s Double Exponential Smoothing Methods in The Forecast of Moving Price for Mutual Funds, Journal of Applied Science, Engineering, Technology, and Education, Vol. 1, No. 2, 183–192.
[14] Nurulita, 2010, Penerapan Metode Peramalan ARIMA (Autoregressive Integrated Moving Average) untuk Penentuan Tingkat Safety Stock pada Industri Elektronik, Depok.
[15] Santosa, E., 2005, Dimensi Pengukuran Kemiskinan, 868–883.
[16] Saragih, J., 2015, Kebijakan Pengentasan Kemiskinan di Daerah Istimewa Yogyakarta, 59.
[17] Septriawan, M. R. & Anan, M., 2023, Peramalan Jumlah Penduduk Miskin di Kota Medan Melalui Analisis ARIMA Time-Series Forecasting Methods dengan Kenaikan Harga Bahan Bakar Minyak (BBM) sebagai Variabel Moderating.
[18] Suryawati, 2004, Teori Ekonomi Mikro. Yogyakarta: UPP AMP YKPN.
[19] Wei, W., 1990, Time Series Analysis: Univariate and Multivariate Methods. Canada: Addison Wesley.
[20] Wini, H., 2005, Analisis Faktor-Faktor Yang Mempengaruhi Jumlah Penduduk Miskin di Wilayah Pemekaran Tingkat Kabupaten, Yogyakarta.
[21] Yuspira, P., Sugara, I., Bukit, R., Suprayetno, E., & Rangkuty, D., 2023, Studi Kajian Garis Kemiskinan dan Penduduk Miskin di Kabupaten Deli Serdang, Jurnal Mahasiswa Kreatif, Vol. 1, No. 4, 228–234.
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