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All Journal Jurnal Teknologi Informasi Cyberku
Kurnia Prayoga Wicaksono
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Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science

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OPTIMASI PARTFICLE SWARM OPTIMIZATION (PSO) PADA ALGORITMA KLASIFIKASI NEURAL NETWORK (NN) DALAM PENENTUAN KELAYAKAN PEMBERIAN SERTIFIKASI GURU Kurnia Prayoga Wicaksono; M. Arif Soeleman; Ricardus Anggi Pramunendar
Jurnal Teknologi Informasi - Cyberku (JTIC) Vol 14 No 1 (2018): Jurnal Teknologi Informasi CyberKU Vol.14 no 1 2018
Publisher : Program Pascasarjana Magister Teknik Informatika, Universitas Dian Nuswantoro

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Abstract

To improve the quality of national education, the government through the Ministry of Education. This is of course interesting for the community to be part of this program, many of whom choose to become teachers, though not from an education-based college. One of the factors that is the main attraction is the benefits that will be obtained for teachers who have passed the certification exam. Government through Master's law, the issuance of a regulated policy of preparedness can be the basis for establishing Master's eligibility as a professional, so that the profession is allowance. owever, conditions in the field, found some teachers who are not yet eligible to hold the certification, there are still many teachers under the standard Teacher Compotency Test. Therefore, built a system made using Artificial Neural Network optimized with Particle Swarm Otimation, to determine the feasibility of certification so that later this case does not happen again. In this study gives a general idea that certified teachers are not all worthy of the predicate. Artificial Neural Network is optimized with Particle Swarm Optimization algorithm, giving higher accuracy with 80.80% accuracy level compared with 79.65% neural network algorithm model.
Co-Authors M. Arif Soeleman Ricardus Anggi Pramunendar