PENGGUNAAN PARTICLE SWARM OPTIMIZATION PADA JARINGAN SYARAF TIRUAN UNTUK KLASIFIKASI SINYAL RADAR
Abstract
Klasifikasi sinyal radar merupakan salah satu tugas penting yang memiliki aplikasi luas, termasuk dalam domain militer, navigasi, dan pengawasan cuaca. Jaringan Syaraf Tiruan (JST) telah terbukti efektif dalam menyelesaikan tugas klasifikasi kompleks berkat kemampuannya dalam memodelkan pola dan hubungan non-linear dalam data. Salah satu tantangan mendasar dalam implementasi JST adalah penentuan jumlah node optimal pada hidden layer, yang secara signifikan memengaruhi performa model. Penelitian ini mengusulkan pendekatan berbasis Particle Swarm Optimization (PSO) untuk mengoptimalkan konfigurasi JST dalam klasifikasi sinyal radar. PSO, sebagai algoritma optimasi berbasis populasi yang terinspirasi dari perilaku sosial kawanan, memungkinkan eksplorasi ruang solusi secara lebih efisien dan efektif dibandingkan metode tradisional. Hasil penelitian menunjukkan bahwa penerapan PSO pada JST secara signifikan meningkatkan metrik performa model, termasuk accuracy, precision, recall, dan F1-score, dibandingkan dengan metode baseline. Namun demikian, penggunaan PSO tidak memberikan peningkatan efisiensi dalam hal waktu komputasi. Temuan ini memberikan kontribusi penting dalam pengembangan model pembelajaran mesin yang lebih akurat untuk aplikasi praktis seperti pengawasan cuaca dan sistem pertahanan, sekaligus memperkaya kajian teoretis di bidang optimasi dan jaringan syaraf tiruan.
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DOI: https://doi.org/10.15548/map.v6i2.8961
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