Deteksi Berita Hoaks Berbahasa Indonesia Menggunakan 1-Dimensional Convolutional Neural Network
Abstract
Kemajuan teknologi informasi yang pesat telah memfasilitasi penyebaran informasi secara global, tetapi juga meningkatkan prevalensi berita hoaks, khususnya di Indonesia. Berita hoaks memiliki potensi untuk menciptakan disinformasi yang dapat memengaruhi opini publik, stabilitas sosial, dan keamanan. Oleh karena itu, diperlukan solusi berbasis teknologi yang efektif untuk mendeteksi dan mengidentifikasi hoaks atau berita palsu. Penelitian ini bertujuan untuk mengembangkan dan mengoptimalkan model 1-dimentional convolutional neural network (1D CNN) guna mendeteksi berita hoaks dengan tingkat akurasi yang tinggi. Dataset yang digunakan terdiri atas 12.151 artikel, yang mencakup 5.276 berita valid dan 6.875 berita hoaks, yang diperoleh dari sumber tepercaya serta platform antihoaks. Tahapan prapemrosesan teks meliputi pembersihan data, case folding, penghapusan tanda baca, penghapusan angka, dan penghapusan stopword. Data tekstual selanjutnya diproses melalui tahapan tokenisasi dan padding untuk persiapan pelatihan model. Arsitektur 1D-CNN yang diusulkan mengintegrasikan lapisan embedding, conv1d, batch normalization, globalmaxpooling1d, dense, dan dropout layer, untuk meningkatkan kemampuan generalisasi serta mengurangi risiko overfitting. Model dilatih menggunakan optimizer Adam dan evaluasi kinerja menggunakan 10-fold cross-validation. Hasil eksperimen menunjukkan bahwa model menghasilkan rata-rata akurasi 97,74%, presisi 97,75%, recall 97,74%, dan F1-score 97,73%. Model yang dikembangkan mampu mengungguli metode sebelumnya, seperti kombinasi convolutional neural network- bidirectional long short – term memory neural network (CNN-BiLSTM), gated recurrent unit (GRU), dan metode konvensional seperti naïve Bayes atau support vector machine (SVM), baik dari segi akurasi maupun efisiensi waktu pelatihan. Penelitian ini menunjukkan bahwa model memiliki kemampuan yang andal dalam mengidentifikasi berita hoaks, baik dari segi tingkat deteksi yang akurat maupun konsistensi kinerja.
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