Penerapan Machine Learning untuk Deteksi Hoaks di Media Sosial Indonesia: Systematic Literature Review
DOI:
https://doi.org/10.24114/cess.v10i2.66584Keywords:
Machine Learning, Deteksi Hoaks, media sosial, Pemrosesan Bahasa Alami, Systematic Literature ReviewAbstract
Maraknya penyebaran hoaks di media sosial Indonesia telah mendorong peningkatan minat terhadap penggunaan kecerdasan buatan, khususnya machine learning (ML), untuk mendeteksi informasi palsu secara otomatis. Penelitian ini bertujuan untuk mengidentifikasi tren, metodologi, dan efektivitas pendekatan ML dalam deteksi hoaks pada media sosial Indonesia melalui kajian pustaka sistematis. Metode systematic literature review (SLR) digunakan dengan merujuk pada panduan PRISMA untuk menelusuri publikasi dari database Scopus, IEEE Xplore, dan Google Scholar selama lima tahun terakhir. Dari total 754 artikel awal, sebanyak 52 artikel memenuhi kriteria kelayakan dan dianalisis lebih lanjut. Evaluasi dilakukan melalui sintesis tematik dan komparasi performa algoritma ML berdasarkan metrik akurasi, presisi, recall, dan F1-score. Hasil kajian menunjukkan bahwa model berbasis deep learning, khususnya BERT, memberikan performa terbaik (akurasi hingga 91.2%), diikuti oleh LSTM dan CNN. Selain itu, pendekatan multi-modal dan penggunaan data lokal Indonesia menjadi faktor signifikan yang mempengaruhi efektivitas deteksi. Kajian ini menyimpulkan bahwa integrasi model berbasis transformer dan data kontekstual Indonesia berpotensi meningkatkan akurasi sistem deteksi hoaks. Artikel ini memberikan kontribusi berupa pemetaan komprehensif metode ML dalam konteks lokal serta rekomendasi implementatif untuk pengembangan sistem deteksi hoaks berbasis AI yang adaptif dan akurat.References
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