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Gulo, Senang Hati
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Computer Science & IT

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Penerapan Multi-Layer Perceptron untuk Mengklasifikasi Penduduk Kurang Mampu Gulo, Senang Hati; Lubis, Andre Hasudungan
Explorer Vol 4 No 2 (2024): July 2024
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/explorer.v4i2.1146

Abstract

The classification of the less capable population in Afulu Sub-district is currently reliant on a manual system, resulting in prolonged processing times. To address this issue, this research endeavors to develop a practical application for the classification of population data, with the primary objective of expediting the processing of population data in Afulu Sub-district. The study will focus on nine villages within the sub-district, encompassing a total population of 11,722 individuals, with a sample size of 386. The present study utilizes the Multilayer Perceptron, a classical algorithm that continues to be the most widely employed method in numerous researches. The findings of the present study indicate that out of the total sample size, 152 individuals were classified as capable, 86 individuals were classified as moderately capable, and a substantial number of 148 individuals were classified as less capable. The classification results were evaluated using a confusion matrix. The 3-5-1 architecture, comprising of 3 input layers, 5 hidden layers, and 1 output layer, was found to be the most superior. This architecture demonstrated an accuracy value of 96.9%, a recall value of 92%, a precision value of 98.5%, and an F-score value of 94.9%. A detailed elucidation of the parameters employed, the formulas utilized, and several computations performed are explained further.
Co-Authors Andre Hasudungan Lubis