Determination of Eligibility Standards for Teacher Certification Using the Particle Swarm Optimization (PSO) Method and Neural Network Classification Algorithm (NN)

Abstract

To improve the quality of national education, the government through the Ministry of Education issued a certification policy. This is of course attractive for the community to be part of this program, many of whom choose to become teachers, even though they are not from higher education based education. One of the factors that attracts it is the allowances that will be obtained for teachers who have passed the certification exam. The government, through the Teacher Law, issues regulatory policies which later can be used as the basis for determining the eligibility of teachers as professionals, so that their profession is entitled to an allowance. However, conditions in the field were found that some teachers were not yet eligible to hold certification, because not a few scored below the standard Teacher Compotency Test (UKG). Therefore, in this study a system is proposed to be built using the Neural Network method and optimized with the Particle Swarm Otimation algorithm, to determine the feasibility of giving certification so that similar cases do not happen again. This study provides an overview that not all certified teachers deserve this predicate. The application of the Neural Network method which is optimized with the Particle Swarm Optimization algorithm, provides a higher accuracy with an accuracy rate of 99.70% compared to the neural network algorithm model of 99.60%.