Deteksi Ujaran Kebencian di Media Sosial menggunakan Machine Learning

• WURI TIRTAWATI
• 14210164

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ABSTRAK

ABSTRAK

Nama              : Wuri Tirtawati

NIM                 : 14210164

Program Studi : Ilmu Komputer

Fakultas          : Teknologi Informasi

Jenjang           : Strata Dua (S2)

Konsentrasi     : Data Mining

Judul               : “Deteksi Ujaran Kebencian di Media Sosial menggunakan Machine Learning”

Media sosial telah menjadi alat yang sangat kuat untuk pertukaran informasi, karena memungkinkan pengguna untuk tidak hanya mengonsumsi informasi tetapi juga berbagi dan mendiskusikan berbagai hal yang menarik bagi mereka. Namun, di balik kemudahan ini, platform media sosial seringkali dihadapkan pada masalah ujaran kebencian - konten yang mencerminkan ekspresi kebencian terhadap individu atau kelompok tertentu. Jenis konten semacam ini bisa menimbulkan ketakutan, intimidasi, atau bahkan menghasut pengguna lain untuk bertindak dengan kekerasan. Salah satu kompleksitas dalam pengawasan media sosial adalah kebebasan pengguna untuk mengekspresikan pemikiran mereka dalam bentuk teks tanpa harus mematuhi aturan tata bahasa yang ketat. Ini menimbulkan tantangan dalam mengidentifikasi dan menganalisis konten ujaran kebencian dengan akurat dan efisien. Walaupun kesadaran tentang masalah yang diakibatkan oleh konten negatif di media sosial semakin meningkat, namun hingga saat ini, solusi yang dapat diandalkan untuk mendeteksi ujaran kebencian masih belum sepenuhnya memadai. Oleh karena itu, tujuan utama dari penelitian ini adalah mengembangkan alat yang handal untuk mendeteksi tweet yang mengandung ujaran kebencian. Dalam penelitian ini, sebuah pendekatan inovatif diajukan untuk mendeteksi dan mengklasifikasikan konten ujaran kebencian dengan menggunakan data dari komunitas yang secara khusus mengidentifikasi diri sebagai kelompok yang menyebarkan kebencian di platform Twitter. Dengan adanya pendekatan yang lebih cermat dan tepat sasaran ini, diharapkan dapat memberikan kontribusi dalam menangani masalah ujaran kebencian yang semakin mendalam di dunia media sosial. Hasil dari percobaan menggunakan algoritma klasifikasi Machine Learning menunjukkan algoritma Logistic Regression mencapai kinerja yang sama dengan hasil perhitungan Voting Ensemble dalam algoritma deteksi ujaran kebencian dengan nilai akurasi sebesar 96,67%.

KATA KUNCI

Ujaran kebencian,machine learning,Klasifikasi Teks,Analisis Sentimen

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DAFTAR PUSTAKA

DAFTAR PUSTAKA

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