Deteksi Tingkat Keparahan Cyberbullying dengan Model Pendekatan Machine Learning Multi Kelas

• ARIF RAHMAN HAKIM
• 14210167

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ABSTRAK

ABSTRAK

Nama              : Arif Rahman Hakim

NIM                 : 14210167

Program Studi : Ilmu Komputer (S2)

Fakultas          : Teknologi Informasi

Jenjang           : Strata Dua (S2)

Konsentrasi     : Data Mining

Judul            : “Deteksi Tingkat Keparahan Cyberbullying dengan Model Pendekatan Machine Learning Multi Kelas”

Cyberbullying adalah tindakan yang disengaja dan berulang yang dilakukan oleh individu atau kelompok dengan maksud untuk menyakiti seseorang melalui media sosial. Meskipun tindakan Cyberbullying terjadi tanpa kekerasan fisik, tetap akan merugikan korban. Korban Cyberbullying dapat mengalami kelelahan mental, depresi, gangguan kecemasan hingga mengakhiri hidup atau bunuh diri. Dengan memanfaatkan algoritma machine learning, penelitian ini bertujuan untuk mendeteksi sebuah teks pada platform media sosial dan mengklasifikasikannya ke dalam kategori cyberbullying atau non-cyberbullying, serta mengetahui tingkat keparahannya. Metode penelitian yang dilakukan terdiri dari beberapa tahapan, seperti pengumpulan data, tahapan pre-processing atau pengolahan data awal, ekstraksi fitur, generalisasi fitur, seleksi fitur, model klasifikasi, dan evaluasi. Hasil menunjukkan bahwa model klasifikasi dengan menggunakan algoritma Random Forest memiliki akurasi sebesar 90,46%, SVM sebesar 90,30%, dan Decision Tree sebesar 88,62%. Selain itu, penelitian ini juga berhasil menciptakan leksikon cyberbullying dengan metode PMI-SO

KATA KUNCI

Twitter,Cyberbullying,SVM,Decision Tree,Random Forest

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