OPTIMASI DETEKSI SPAM EMAIL DENGAN RANDOM FOREST DAN RANDOM SEARCH
DOI:
https://doi.org/10.32897/sobat.2025.7.1.5288Keywords:
Hyperparameter-Tuning, Random Forest, Randomized Search Classifier, Deteksi Spam, EmailAbstract
Deteksi spam pada email merupakan komponen penting dalam sistem keamanan dan penyaringan informasi digital. Untuk meningkatkan akurasi dalam klasifikasi spam, digunakan algoritma Random Forest Classifier yang dikenal karena stabilitas dan keandalannya dalam memproses data dengan banyak fitur. Penelitian ini menerapkan teknik hyperparameter tuning menggunakan metode Random Search Cross-Validation untuk mengoptimalkan kinerja model dengan menemukan kombinasi parameter terbaik. Random Search Cross-Validation memungkinkan proses pencarian parameter yang efisien tanpa harus menguji seluruh kemungkinan seperti pada Grid Search Cross-Validation. Hasil evaluasi menunjukkan bahwa gabungan antara model Random Forest dan Hyperparameter-Tuning menggunakan metode Random Search Cross-Validation memiliki performa yang lebih tinggi dibandingkan model default, terutama dalam hal akurasi dan presisi deteksi spam. Sistem ini dapat membantu pengguna maupun organisasi dalam mengelola email secara lebih efektif dan aman.
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