Kohesi: Jurnal Sains dan Teknologi

Articles

Vol. 10 No. 6 (2025): Kohesi: Jurnal Sains dan Teknologi, ISSN 3025-1311

KLASIFIKASI KATEGORI WEBSITE DENGAN MENGGUNAKAN NAÏVE BAYES DAN SUPPORT VECTOR MACHINES

Submitted
November 15, 2025
Published
2025-11-16

Abstract

Seiring ledakan informasi pada abad ke-21, kelimpahan informasi menjadi tantangan utama di internet. Meskipun search engine membantu pengguna menilai nilai situs web berdasarkan topiknya, menemukan informasi spesifik tetap menantang. Penelitian ini mengusulkan pendekatan praktis dengan membuat catatan data otomatis yang merangkum konten setiap situs web untuk tujuan kategorisasi. Tugas klasifikasi teks (TC) secara otomatis menetapkan dokumen ke kategori tertentu. Penelitian ini fokus pada metode klasifikasi teks, termasuk Naïve Bayes dan Support Vector Machines (SVM), untuk mencapai akurasi yang baik. Selain dua metode tersebut, penelitian merinci penggunaan metode lain seperti K-Nearest Neighbors (KNN) dan Random Forest dalam konteks klasifikasi web phishing dan analisis sentimen. Dalam eksperimen ini, Naïve Bayes dan SVM dievaluasi untuk mengklasifikasikan kategori situs web. Data dibagi menjadi pelatihan (70%) dan pengujian (30%), dengan hasil akurasi sekitar 88% untuk Naïve Bayes dan 85% untuk SVM. Studi ini memberikan pemahaman lebih dalam tentang kinerja dua metode klasifikasi berbeda dalam konteks pengkategorian situs web.

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