Penerapan Algoritma Support Vector Machine dan  Multi-Layer Perceptron pada Klasifikasi Topik Berita

Sudianto Sudianto; Asti Dwi Sripamuji; Imada Riski Ramadhanti; Risa Riski Amalia; Julian Saputra; Bagas Prihatnowo

Authors

Sudianto Sudianto Institut Teknologi Telkom Purwokerto
Asti Dwi Sripamuji Institut Teknologi Telkom Purwokerto
Imada Riski Ramadhanti Institut Teknologi Telkom Purwokerto
Risa Riski Amalia Institut Teknologi Telkom Purwokerto
Julian Saputra Institut Teknologi Telkom Purwokerto
Bagas Prihatnowo Institut Teknologi Telkom Purwokerto

Keywords:

Berita, Klasifikasi, Support Vector Machine, Multi-Layer Perceptron, TF-IDF

Abstract

Salah satu aktivitas masyarakat Indonesia di internet adalah mengakses berita online dengan persentase 15,5%. Dalam mencerna suatu informasi berita online, masyarakat Indonesia masih memiliki kebiasaan yang buruk ketika membaca lebih dari satu sumber media online pada kasus yang sama. Sehingga perlu adanya klasifikasi berita secara otomatis. Penelitian sebelumnya menggunakan Mutual Information and Bayesian Network untuk mengkategorikan suatu berita, dalam format data tekstual dengan akurasi 75.34%. Penelitian ini memberikan suatu inovasi baru berupa pengimplementasian algoritma Support Vector Machine dan Multi-Layer Perceptron untuk klasifikasi menggunakan pembelajaran supervised atau yang disebut backpropagation. Tujuan dari pengklasifikasian topik berita menggunakan Algoritma Support Vector Machine memudahkan pembaca untuk menemukan informasi yang pembaca butuhkan, dengan secara otomatis mengelompokkan berita ke dalam kategori tertentu sesuai informasi yang terkandung. Metode yang digunakan yaitu metode Term Frequency – Inverse Document Frequency untuk feature selection data pelatihan dan algoritma Support Vector Machines dan Multi-Layer Perceptron untuk klasifikasi. Hasil yang diperoleh menunjukan skor akurasi sebesar 74% pada SVM dan 78% pada MLP. Sementara itu, nilai precision dan recall yaitu 76% dan 74% pada SVM serta 79% dan 78% pada MLP.

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