Kombinasi Analytical Hierarchy Process, C4.5, dan Particle Swarm Optimization pada Klasifikasi Pegawai

Abstract

Decision Tree C4.5 is widely implemented in various research fields in determining classification, but there are still weaknesses in Decision Tree C4.5, one of which is that it cannot rank each alternative. In this study, to overcome the weakness of Decision Tree C4.5, a combination of Analytical Hierarchy Process (AHP) methods, Decision Tree C4.5, and Particle Swarm Optimization (PSO) methods is proposed in the case study of employee classification for promotion recommendations. The research begins by determining the criteria and weighting criteria from the interview results which are then processed with AHP to produce employee ratings and eligibility labels for the classification process. The classification process uses the Decision Tree C4.5 method which is optimized with the PSO algorithm so as to produce employee eligibility data for promotions. The results of the combined research of AHP, Decision Tree C4.5, and PSO methods show that AHP can produce employee ratings based on performance and potential criteria, and Decision Tree C4.5 classification and optimization with PSO have better accuracy results, namely 95.80% compared to Decision Tree C4.5 method without PSO optimization is 93.40%. Based on the results of the ranking and classification of this research can be used as a basis for promotion of employees.