Forecasting Population of Madiun Regency Using ARIMA Method

Yuniar Farida, Mayandah Farmita, Nurissaidah Ulinnuha, Dian Yuliati

Abstract


The high population growth of the Madiun Regency can cause population density that can have implications for other problems, both in terms of social, economic, welfare, security, land availability, availability of clean water, and food needs. This study aims to predict the population growth of Madiun Regency using the ARIMA method. The ARIMA (Autoregressive Integrated Moving Average) method is popular for forecasting time series data, which is reliable because the calculation process is done gradually. This study uses annual population data of Madiun Regency from 1983 to 2021 and produces an ARIMA forecasting model (0,2,1) with a MAPE value of 8.42%. The results of this study are expected to be used as information from the Madiun Regency government in anticipating the emergence of problems caused by the population level of Madiun Regency in the future.

Keywords


ARIMA; Forecasting; Population; Time Series Analysis

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References


C. Christiani, P. Tedjo, and B. Martono, “Analisis Dampak Kepadatan Penduduk Terhadap Kualitas Hidup Masyarakat Provinsi Jawa Tengah,” Ilmiah, vol. 3, no. 1, pp. 102–114, 2014.

J. Dai and S. Chen, “The application of ARIMA model in forecasting population data,” J. Phys. Conf. Ser., vol. 1324, no. 1, 2019, doi: 10.1088/1742-6596/1324/1/012100.

F. Fejriani, M. Hendrawansyah, L. Muharni, S. F. Handayani, and Syaharuddin, “Forecasting Peningkatan Jumlah Penduduk Berdasarkan Jenis Kelamin menggunakan Metode Arima,” J. Kajian, Penelit. dan Pengemb. Pendidik., vol. 8, no. 1 April, pp. 27–36, 2020, [Online]. Available: http://journal.ummat.ac.id/index.php/geography/article/view/2261/pdf.

Direktorat Statistik Kependudukan dan Ketenagakerjaan, Potret Sensus Penduduk 2020 Menuju Satu Data Kependudukan Indonesia. Jakarta: BPS RI, 2021.

BPS Kabupaten Madiun, Kabupaten Madiun Dalam Angka Madiun Regency In Figures 2021. 2021.

Haslina, Hasmah, K. W. Fitriani, M. Asbar, and Asrirawan, “Penerapan Metode ARIMA (Autoregressive Integrated Moving Average) Box Jenkins Untuk Memprediksi Pertambahan Jumlah Penduduk Transmigran (Jawa dan Bali) di Kecamatan Sukamaju, Kabupaten Luwu Utara Propinsi Sulawesi Selatan,” Dinamika, vol. 9, no. 1, pp. 55–67, 2018.

H. Yoshikura, “Negative impacts of large population size and high population density on the progress of measles elimination,” Jpn. J. Infect. Dis., vol. 65, no. 5, pp. 450–454, 2012, doi: 10.7883/yoken.65.450.

N. L. A. K. Yuniastari and I. W. W. Wirawan, “Peramalan Permintaan Produk Perak Menggunakan Metode Simple Moving Average Dan Single Exponential Smoothing,” Sist. dan Inform., vol. 9, no. 1, pp. 97–106, 2016.

H. S. Pakpahan, Y. Basani, and R. R. Hariani, “Prediksi Jumlah Penduduk Miskin Kalimantan Timur Menggunakan Single dan Double Exponential Smoothing,” Inform. Mulawarman J. Ilm. Ilmu Komput., vol. 15, no. 1, pp. 47–51, 2020.

I. Mardiyah, W. D. Utami, D. C. R. Novitasari, M. Hafiyusholeh, and D. Sulistiyawati, “ANALISIS PREDIKSI JUMLAH PENDUDUK DI KOTA PASURUAN MENGGUNAKAN METODE ARIMA,” Ilmu Mat. dan Terap., vol. 15, no. 3, pp. 525–534, 2021.

T. Nyoni, C. Mutongi, and N. Munyaradzi, “Population dynamics in Gambia: An ARIMA approach,” Munich Pers. RePEc Arch., 2019.

T. Nyoni, “The population question in Zimbabwe : reliable projections from the Box - jenkins ARIMA approach,” Munich Pers. RePEc Arch., pp. 0–15, 2019.

M. S. K. Abhilash, A. Thakur, D. Gupta, and B. Sreevidya, “Time series analysis of air pollution in bengaluru using ARIMA model,” Adv. Intell. Syst. Comput., vol. 696, pp. 413–426, 2018, doi: 10.1007/978-981-10-7386-1_36.

Nurviana, R. P. Sari, U. Nabilla, and T. Talib, “Forecasting Rice Paddy Production in Aceh Using ARIMA and Exponential Smoothing Models,” CAUCHY, vol. 7, no. 2, pp. 281–292, 2022.

H. Alabdulrazzaq, M. N. Alenezi, Y. Rawajfih, B. A. Alghannam, A. A. Al-Hassan, and F. S. Al-Anzi, “On the accuracy of ARIMA based prediction of COVID-19 spread,” Results Phys., vol. 27, p. 104509, 2021, doi: 10.1016/j.rinp.2021.104509.

A. Swaraj, K. Verma, A. Kaur, G. Singh, A. Kumar, and L. Melo de Sales, “Implementation of stacking based ARIMA model for prediction of Covid-19 cases in India,” J. Biomed. Inform., vol. 121, no. August 2020, p. 103887, 2021, doi: 10.1016/j.jbi.2021.103887.

B. Guha and G. Bandyopadhyay, “Gold Price Forecasting Using ARIMA Model,” J. Adv. Manag. Sci., no. March, pp. 117–121, 2016, doi: 10.12720/joams.4.2.117-121.

D. Banerjee, “Forecasting of Indian stock market using time-series ARIMA model,” Int. Conf. Bus. Inf. Manag. ICBIM 2014, pp. 131–135, 2014, doi: 10.1109/ICBIM.2014.6970973.

E. Grigonytė and E. Butkevičiūtė, “Short-term wind speed forecasting using ARIMA model,” Energetika, vol. 62, no. 1–2, pp. 45–55, 2016, doi: 10.6001/energetika.v62i1-2.3313.

J. Sun, “Forecasting COVID-19 pandemic in Alberta, Canada using modified ARIMA models,” Comput. Methods Programs Biomed. Updat., vol. 1, no. September, p. 100029, 2021, doi: 10.1016/j.cmpbup.2021.100029.

C. B. A. Satrio, W. Darmawan, B. U. Nadia, and N. Hanafiah, “Time series analysis and forecasting of coronavirus disease in Indonesia using ARIMA model and PROPHET,” Procedia Comput. Sci., vol. 179, no. 2020, pp. 524–532, 2021, doi: 10.1016/j.procs.2021.01.036.

S. Ozturk and F. Ozturk, “Forecasting Energy Consumption of Turkey by Arima Model,” J. Asian Sci. Res., vol. 8, no. 2, pp. 52–60, 2018, doi: 10.18488/journal.2.2018.82.52.60.

D. Didiharyono and M. Syukri, “Forecasting with arima model in anticipating open unemployment rates in south sulawesi,” Int. J. Sci. Technol. Res., vol. 9, no. 3, pp. 3838–3841, 2020.

D. S. Domingos, J. F. L. de Oliveira, and P. S. G. de Mattos Neto, “An intelligent hybridization of ARIMA with machine learning models for time series forecasting,” Knowledge-Based Syst., vol. 175, pp. 72–86, 2019, doi: 10.1016/j.knosys.2019.03.011.

F. A. Chyon, M. N. H. Suman, M. R. I. Fahim, and M. S. Ahmmed, “Time Series Analysis and Predicting COVID-19 Affected Patients by ARIMA Model Using Machine Learning,” J. Virol. Methods, vol. 301, no. December 2021, p. 114433, 2021, doi: 10.1016/j.jviromet.2021.114433.

N. Ulinnuha and Y. Farida, “Prediksi Cuaca Kota Surabaya Menggunakan Autoregressive Integrated Moving Average (Arima) Box Jenkins dan Kalman Filter,” J. Mat. “MANTIK,” vol. 4, no. 1, pp. 59–67, 2018, doi: 10.15642/mantik.2018.4.1.59-67.

T. Yunita, “Peramalan Jumlah Penggunaan Kuota Internet Menggunakan Metode Autoregressive Integrated Moving Average ( ARIMA ),” J. Math. Theory Appl., vol. 1, no. 2, pp. 16–22, 2019.

L. Wulandari, Y. Farida, A. Fanani, and M. Syai’in, “Optimization of Autoregressive Integrated Moving Average (ARIMA) for Forecasting Indonesia Sharia Stock of Index (ISSI) using Kalman Filter,” pp. 295–303, 2020, doi: 10.5220/0008906902950303.

M. B. S. Junianto, “Fuzzy Inference System Mamdani dan the Mean Absolute Percentage Error (MAPE) untuk Prediksi Permintaan Dompet Pulsa pada XL Axiata Depok,” J. Inform. Univ. Pamulang, vol. 2, no. 2, p. 97, 2017, doi: 10.32493/informatika.v2i2.1511.

P. K. Madiun, Data Demografi, Ekonomi dan Sosial Budaya Kota Madiun 2017. Madiun: Pemerintah Kota Madiun, 2017.




DOI: https://doi.org/10.18860/ca.v7i3.16156

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