PERBANDINGAN ALGORITMA MACHINE LEARNING UNTUK PENENTUAN KLASIFIKASI KEMISKINAN MULTIDIMENSI DI PROVINSI NUSA TENGGARA TIMUR - Kristanto Setyo Utomo (Jurnal Statistika Terapan (ISSN 2807-6214), 2022)
The Covid-19 pandemic has proven to directly impact the percentage of poverty in the Province of East Nusa Tenggara. However, the determination of the size of poverty so far has been carried out using an economic dimension approach, namely the poverty line. This study classifies multidimensional poverty, namely the dimensions of health, education, economy, and life worthiness. In this multidimensional poverty classification, this research utilizes machine learning algorithms. The test results show that the Decision Tree algorithm is the best algorithm for classifying multidimensional poverty in East Nusa Tenggara Province with an accuracy rate of 82.69 percent, precision of 84.08 percent, and recall 97.56 percent. This algorithm shows that the birth attendant indicators on the health dimension and primary education on the education dimension have a high gain value. These two indicators become the primary decision node in the Decision Tree to determine multidimensional poverty that needs serious attention by the East Nusa Tenggara Provincial government.