PERAMALAN JUMLAH PENDUDUK DKI JAKARTA DENGAN METODE REGRESI LINEAR UNTUK MEMPERSIAPKAN INDONESIA EMAS 2045
Analisis Pertumbuhan Penduduk Usia Produktif dalam Mendukung Indonesia Emas 2045
DOI:
https://doi.org/10.31848/jkri.v3i2.4717Keywords:
Forecasting, Linear regression, Golden Indonesian, Demografic bonusAbstract
Indonesia has been experiencing a demographic bonus since 2015. Without careful planning, the demographic bonus can turn into a demographic burden. Therefore, this study aims to analyze and predict the number of productive age human resources (15-64 years) and non-productive human resources (>65 years) to welcome the 2045 golden Indonesia mission using the linear regression forecasting method based on the last 8 years of data from the DKI Jakarta Central Bureau of Statistics. Based on the prediction results, the Productive Age Population in DKI Jakarta in Indonesia Emas 2045 is estimated to reach 9,015,054 people, and the non-productive age is estimated to reach 3,056,602 people. Based on the results of these predictions, a dependency ratio of 33.91% is obtained, which shows that the dependency value is low so that the country can maximize the demographic bonus. Therefore, it is hoped that the government can provide equitable access to learning, facilities for fostering various labor professions and expanding employment opportunities to achieve sustainable development goals that will support the success of the 2045 Golden Indonesia vision and mission so that Indonesia can become a developed country.
References
Ababil, O. J., Wibowo, S. A., & Zulfia Zahro’, H. (2022). Penerapan Metode Regresi Linier Dalam Prediksi Penjualan Liquid Vape Di Toko Vapor Pandaan Berbasis Website. JATI (Jurnal Mahasiswa Teknik Informatika), 6(1), 186–195. https://doi.org/10.36040/jati.v6i1.4537
Badan Pusat Statistik. (2023). Bonus Demografi dan Visi Indonesia Emas 2045. Badan Pusat Statistik, 1–12. https://bigdata.bps.go.id/documents/datain/2023_01_2_Bonus_Demografi_dan_Visi_Indonesia Emas_2045.pdf
Harlan, J. (2018). Analisis Regresi Linear. In Journal of Chemical Information and Modeling (Vol. 53, Issue 9).
Makridakis, & Wheelwright. (1983). Forecasting Methods and Applications 2nd Edition.
Nurulita. (2012). Penerapan Metode Peramalan Arima (Autoregressive Integrated Moving Average) Untuk Penentuan Tingkat Safety Stock Pada Industri Elektronik.
Rachman, R. (2018). Penerapan Metode Moving Average Dan Exponential Smoothing Pada Peramalan Produksi Industri Garment. Jurnal Informatika, 5(2), 211–220. https://doi.org/10.31311/ji.v5i2.3309
Ruspendi, Rusmalah, & Nurmutia, S. (2022). Teknik Peramalan. Lembaga Penerbit dan Publikasi Universitas Pamulang (Issue 1).
Sya’adah, A., Salim Dahda, S., & Ismiyah, E. (2023). Perbandingan Keakuratan Peramalan Produksi Obat Dengan Metode Winter Dan Metode Dekomposisi. Jurnal Rekayasa Sistem Industri, 9(1), 15–20. https://doi.org/10.33884/jrsi.v9i1.8143
