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All Journal JSAI (Journal Scientific and Applied Informatics)
Muhammad Al Hapiz
Universitas Sjakhyakirti, Palembang, Indonesia
Computer Science & IT

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Model Deteksi Berita Palsu Menggunakan BERT dan Bi-LSTM Berbasis Discriminative Approach Dwi Fitri Brianna; Paisal Paisal; M. Apreza Saputra; Muhammad Al Hapiz
JSAI (Journal Scientific and Applied Informatics) Vol 8 No 3 (2025): November
Publisher : Fakultas Teknik Universitas Muhammadiyah Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36085/jsai.v8i3.9384

Abstract

This study aimed to develop a text classification model for detecting hoaxes using a deep learning approach and text representation methods. The text data that had undergone preprocessing were then extracted using three approaches: Word2Vec, Doc2Vec, and Bidirectional Encoder Representations from Transformers (BERT). The research dataset consisted of 2,325 genuine news articles (label 0) and 2,287 fake news articles (label 1). In this study, BERT feature vectors with a dimension of 768 were combined with the Bidirectional Long Short-Term Memory (Bi-LSTM) algorithm to capture sequential dependencies in the text, along with the Support Vector Machine (SVM) algorithm as the final classifier. The training process was carried out on Dell Precision 7750 hardware using parameters of embedding dimension 128, 64 hidden units, a dropout rate of 0.3, and a learning rate of 0.001. Training and testing were conducted for 10 epochs with a batch size of 32. The results indicated that the Word2Vec and Bi-LSTM model achieved an accuracy of 87.4% with an F1-Score of 87.0%, while the Doc2Vec and Bi- LSTM model performed slightly lower with an accuracy of 85.6% and an F1- Score of 85.4%. The best performance was obtained by the BERT, Bi-LSTM, and SVM model, which achieved an accuracy of 93.8%, precision of 94.1%, recall of 93.5%, and an F1-Score of 93.7%.
Pemodelan Topik Berdasarkan Dokumen Penelitian Bidang Ilmu Komputer Menggunakan Text Mining Bakhtiar Bakhtiar; Azhar Andika Putra; Muhammad Al Hapiz; Firga Abel Astiawan
JSAI (Journal Scientific and Applied Informatics) Vol 8 No 3 (2025): November
Publisher : Fakultas Teknik Universitas Muhammadiyah Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36085/jsai.v8i3.9387

Abstract

This study aimed to develop a document clustering model using a combination of the IndoBERT model and the K-Means algorithm to group research abstracts in the field of computer science and technology. The data used consisted of 1000 research abstracts, divided into two parts: 80% for training data (800 abstracts) and 20% for testing data (200 abstracts). The IndoBERT model was used to represent the abstracts as embedding vectors, which were then processed with the K-Means algorithm to form 10 topic clusters, including artificial intelligence, computer systems and networks, programming, cybersecurity, and others. The training experiment used the training data to generate clusters and centroids for mapping new documents into the appropriate clusters. Evaluation was carried out using several metrics, including accuracy, cluster homogeneity, Davies-Bouldin Index, and Silhouette Score. The testing results showed that the developed model achieved an accuracy of 85%, indicating good performance in clustering the test data. The cluster homogeneity value of 0.90 indicated that documents that should belong to the same cluster were grouped together effectively. The Davies-Bouldin Index value was 0.34, while the Silhouette Score was 0.76.
Co-Authors Azhar Andika Putra Bakhtiar Bakhtiar Dwi Fitri Brianna Firga Abel Astiawan M. Apreza Saputra Paisal Paisal