PENERAPAN PARTICLE SWARM OPTIMIZATION UNTUK PENINGKATAN AKURASI KELULUSAN SERTIFIKASI LISTRIK DENGAN ALGORITMA NEURAL NETWORK

Elin Panca Saputra, Aryo Tunjung Kusumo, Andi Saryoko

Abstract


Keberhasilan sebuah lembaga sertifikasi tidak hanya ditentukan oleh proses pelaksanaannya saja, namun juga didukng oleh kemampuan kelulusan yang memiliki kompetensi yang sangat baik, sehingga menjadi peluang yang besar untuk mendapatkan pekerjaan bagi siswa. adapun tujuan penelitian kami adalah mengembangkan serta mengevaluasi model prediktif, dengan menerapkan algoritma Particle Swarm Optimization (PSO) sebagai metode optimasi parameter pada model Neural Network yang digunakan untuk memprediksi tingkat kelulusan sertifikasi listrik sehingga dapat mencapai tingkat akurasi yang lebih tinggi dibandingkan dengan hanya menggunakan satu algoritma yaitu Neural Network adapun hasil yang didapat dengan hanya menggunakan Neural Network saja sebesar 76.50% dan hasil AUC dengan 0.783 selanjutnya dengan menerapkan Particle Swarm Optimization menghasilkan peningkatan kinerja dengan nilai akurasi dengan 83.00% serta nilai AUC dengan 0.796. artinya ada kenaikan sebesar nilai akurasi sebesar 6.5% dibandingkan dengan metode Neural Network tanpa optimasi, sementara nilai AUC juga mengalami peningkatan sebesar 0,013%. maka hasil penelitian kami dengan menerapkan algoritma Particle Swarm Optimization (PSO) terbukti dari penelitian sebelumnya yang menunjukkan peningkatan kerena PSO memiliki teknik optimasi yang sangat baik.

Keywords


PSO; NN; sertifikasi

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DOI: https://doi.org/10.29100/jipi.v11i3.9874

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JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika)
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