RECALIBRATING REVERSE MIGRATION FACTORS THROUGH A MACHINE LEARNING MODEL IN MALAYSIA
DOI:
https://doi.org/10.24191/myse.v13i2.12548Keywords:
Reverse, Migration, Machine Learning, Recalibration, MalaysiaAbstract
Reverse migration is the catalyst that reverberates across social, economic, and demographic landscapes, influencing both the destination and origin regions. With an increase of understanding that continues to grow, it is not possible in a resource-constrained world, to assume that rural-to-cities migration will continue to grow indefinitely. It needs to be re-examined. This paper presents a comprehensive analysis of the factors that motivate migrants to move from rural-to-cities and back again. As migration predictions are notorious and complex, this study adopted a machine learning technique in predicting reverse migration based on the dataset by the Department of Statistical Malaysia (DOSM). These features and the significance of the reverse migration factors were compared between the three types of tree-based machine learning (Gradient Boosted Trees, Random Forest, and Decision Tree). The results depicted that the ‘destination state’ stood out as the most important feature in all the machine learning reverse migration prediction models. Notably, although this factor exhibited the lowest correlation coefficient in the Random Forest algorithm, the combined contributions of other factors, each with lower coefficient values, collectively rendered Random Forest the most exceptional algorithm, achieving a remarkable accuracy rate of 93.3%. The findings of this study are valuable for authorities in formulating effective strategies for sustainable development, allocating resources, and human settlements in both urban and rural regions.
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Copyright (c) 2026 Nur Huzeima Mohd Hussain, Suraya Masrom, Thuraiya Mohd, Azreen Anuar
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