PENDEKATAN KOMPARATIF ALGORTIMA MACHINE LEARNING UNTUK PREDIKSI KEMISKINAN GLOBAL
Abstract
Kemiskinan masih menjadi masalah sosial yang butuh perhatian khusus untuk ditangani. Banyak dampak yang ditimbulkan, diantaranya permasalahan pertumbuhan otak anak, meningkatnya penyakit jangka panjang, dan meningkatnya konflik sosisal dan keamanan. Maka perlu usaha untuk mengatasi kasus kemiskinan secara efektif dengan pendekatan yang inovatif dan berbasis data yaitu penggunaan Algoritma machine learning. Algoritma ini dapat menganalisis data kemiskinan, mengidentifikasi pola, dan memprediksi risiko kemiskinan dengan lebih akurat. Penelitian ini fokus menganalisis performa algoritm machine learning yaitu Decision Trees dan Naïve Bayes. Hasil penelitian menunjukkan algoritma Decision Trees memiliki akurasi lebih baik (84,2%) dibandingkan akurasi algortima Naïve Bayes (78,9%). Namun performa algoritma Naïve Bayes dalam memprediksi berbagai kelas lebih stabil dibanding algoritma Decision Trees berdasarkan nilai presisi, recall, F1-score dan specificity.
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DOI: https://doi.org/10.15548/map.v6i2.10063
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