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All Journal Jurnal EMT KITA AJAD : Jurnal Pengabdian kepada Masyarakat International Journal Software Engineering and Computer Science (IJSECS) International Journal of Management Science and Information Technology (IJMSIT) Jurnal Elektronika dan Teknik Informatika Terapan Jurnal Inovasi Bisnis Manajemen dan Akuntansi Insight Management And Business JCSE: Journal of Community Service and Empowerment DIBINOM(Digital Business and Economics Journal)
Andri Nofiar. Am
Politeknik Negeri Bengkalis
Humanities Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Social Sciences Other

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Prediksi Jumlah Penduduk Malaysia Berdasarkan Faktor Demografi dan Etnis Menggunakan Metode Random Forest Nurkholis Nurkholis; Andri Nofiar. Am; Hayatul Khairul Rahmat; Rahmad Akbar
Jurnal Elektronika dan Teknik Informatika Terapan ( JENTIK ) Vol. 3 No. 4 (2025): Desember: Jurnal Elektronika dan Teknik Informatika Terapan (JENTIK)
Publisher : Politeknik Kampar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59061/jentik.v3i4.1550

Abstract

Accurate population prediction is essential for development planning, particularly in multiethnic countries such as Malaysia, yet conventional statistical approaches generally do not explicitly incorporate ethnic composition. This study aims to develop a population prediction model for Malaysia using a Random Forest Regressor that accounts for demographic factors and the composition of six ethnic groups. Historical data from 16 Malaysian states spanning 1981–2025 (673 state-year observations) were processed through feature engineering, including population lag variables, growth rate, and ethnic proportions, yielding 21 independent variables. The model was trained using a time-aware train-test split (80% training, 20% testing) and validated with 5-fold time series cross-validation. Results on the test set (2020–2025) showed strong performance, with R² = 0.9769, RMSE = 259 thousand people, MAE = 97 thousand people, and sMAPE = 3.17%. Feature importance analysis revealed that the previous year’s population (pop_lag1) and gender composition were the most dominant predictors. Projections indicate Malaysia’s population will reach approximately 38.5 million by 2030. These findings demonstrate that Random Forest with demographic-ethnic features can produce more accurate population predictions, providing a data-driven policy support tool for the Malaysian government.
Co-Authors Antoni Pribadi Burhan Hafid Citi Sarah Custer, Johny Dzulhijjah Yetti Eli Anggre Yeny Febri Holong Marito Situmorang Hayatul Khairul Rahmat Larbiel Hadi Mohammad Alkadri Perdana Muhammad Arif Nafta Melda Wati Napitupulu Nurkholis Nurkholis Okprana, Harly Putrama Alkhairi Rahmad Akbar Ratu Natalia Marjani Ufayrah Safni Marwa Salwa Salwa Shahidah Ramadhani Slamet Triyanto Suci Tasya Ramadhani Supriati Tri Handayani Wan Junita Raflah