DETEKSI CYBERBULLYING PADA DATA TWEET MENGGUNAKAN METODE RANDOM FOREST DAN SELEKSI FITUR INFORMATION GAIN

Rachmad Masbadi Hatullah Nurnaryo, Mulaab Mulaab, Ika Oktavia Suzanti, Doni Abdul Fatah, Andharini Dwi Cahyani, Fifin Ayu Mufarroha

Abstract


Indonesia merupakan salah satu negara dengan pengguna media sosial terbanyak. Dengan banyaknya pengguna media sosial, hal ini dapat memicu munculnya cyberbullying. Cyberbullying adalah tindakan berulang yang melecehkan, mempermalukan, mengancam, atau mengganggu orang lain melalui komputer, ponsel, dan perangkat elektronik lainnya, termasuk situs web jejaring sosial online. Twitter merupakan salah satu media sosial yang sering digunakan untuk melakukan cyberbullying. Deteksi cyberbullying merupakan langkah penting untuk membuat lingkungan yang baik dalam interaksi media sosial. Penelitian ini mendeteksi cyberbullying yang berasal dari tweet berbahasa Indonesia dengan menggunakan metode Random Forest sebagai pengklasifikasi. Seleksi fitur information gain juga digunakan untuk menyeleksi fitur yang berupa atribut. Penelitian ini bertujuan untuk mengetahui akurasi deteksi cyberbullying dari metode Random Forest dan memilih fitur penting untuk meningkatkan kinerja metode. Dari hasil pengujian, didapatkan nilai Accuracy tertinggi sebesar 72.1% dengan atribut berjumlah 1295 dari 2277 atribut. Hal ini berarti, pemilihan fitur yang baik dapat meningkatkan performa dari metode machine learning.

Kata kunci: Cyberbullying, Information Gain, Random Forest, Tweet


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DOI: https://doi.org/10.21107/simantec.v11i1.17256

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