Prediksi Penduduk Miskin di Daerah Tertinggal Indonesia dengan Algoritma Prophet

Authors

  • Budi Wijaya Rauf Sistem dan Teknologi Informasi, Universitas Muhammadiyah Kendari

Keywords:

Underdeveloped regions, Forecasting, Poverty, Machine Learning, prophet

Abstract

Poverty is one of the common problems faced by every country in the world, including Indonesia. Governments from various regions in Indonesia are striving to eradicate poverty, particularly in underdeveloped areas. According to the Central Bureau of Statistics (BPS), the poverty rate has increased from 18.87% in 2015 to 24.56% in 2022 in these underdeveloped areas, which consist of 62 regions. By predicting the number of poor people in these regions, the government hopes to take steps to combat poverty. This study employs the Prophet algorithm, which is then integrated into a machine learning model to predict the number of poor people in underdeveloped areas. The data used covers the years 2015 to 2022, and the aim of this research is to assess the accuracy and error rate using the Mean Squared Error (MSE) and Mean Absolute Percentage Error (MAPE) parameters. The prediction results indicate an increase in the number of impoverished individuals to 35% in the year 2024, and it is projected to continue rising to 35% in 2027, with an MSE parameter of 2% and an MAPE parameter of 1%.

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Published

2023-08-12

How to Cite

Wijaya Rauf, B. (2023). Prediksi Penduduk Miskin di Daerah Tertinggal Indonesia dengan Algoritma Prophet. Jurnal Ilmu Manajemen Sosial Humaniora (JIMSH), 5(2), 116–125. Retrieved from https://journal.umkendari.ac.id/jimsh/article/view/400

Issue

Vol. 5 No. 2 (2023): August, Jurnal Ilmu Manajemen Sosial Humaniora (JIMSH)

Section

Articles

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Copyright (c) 2023 Budi Wijaya Rauf

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