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All Journal Prosiding Semnastek RABIT: Jurnal Teknologi dan Sistem Informasi Univrab Philanthropy: Journal of Psychology MATAPPA: Jurnal Pengabdian Kepada Masyarakat Sebatik Systemic: Information System and Informatics Journal JSAI (Journal Scientific and Applied Informatics) JI-Tech Malcom: Indonesian Journal of Machine Learning and Computer Science Prosiding Seminar Nasional Unimus INOVTEK Polbeng - Seri Informatika
Safitri Jaya
Universitas Pembangunan Jaya
Humanities Computer Science & IT Education Engineering Health Professions Nursing Public Health Social Sciences Other

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PENGEMBANGAN SISTEM DETEKSI INFORMASI PALSU PADA MEDIA ONLINE MENGGUNAKAN ALGORITMA BERT: DEVELOPMENT OF A FAKE INFORMATION DETECTION SYSTEM ON ONLINE MEDIA USING THE BERT ALGORITHM Ferdy Agustian; Safitri Jaya
Rabit : Jurnal Teknologi dan Sistem Informasi Univrab Vol 11 No 2 (2026): Juli
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36341/rabit.v11i2.7885

Abstract

The spread of fake news through digital media platforms has become a serious problem affecting social, political, and public health conditions in Indonesia. The speed of information dissemination has exceeded the capacity of existing manual verification mechanisms. This study aims to develop an automated fake news detection system for Indonesian-language news using a fine-tuning approach on two BERT variants, namely IndoBERT and multilingual BERT (mBERT), with a multi-field input strategy that combines news headlines and narratives into a single sequence using the [SEP] separator token. The dataset consists of 22,400 balanced samples collected from TurnBackHoax.id, Antaranews, Kompas, and Detik, and is divided into training, validation, and testing sets using a 70:15:15 ratio. Both models were trained using identical hyperparameter configurations on the Kaggle platform with dual T4 GPU acceleration. Evaluation results on the test set show that mBERT achieved an accuracy of 99.79% and an F1-score of 0.9979, slightly outperforming IndoBERT which achieved an accuracy of 99.67% and an F1-score of 0.9967. The system was implemented as a web application using FastAPI and HTML/JavaScript, featuring real-time prediction, automated article scraping from seven news websites, and LIME-based explainability visualization integrated directly into the user interface. Black-box testing achieved a pass rate of 95.5%, while white-box testing achieved 100%, confirming that the system functions according to the designed specifications.  
IndoBERT vs SVM: Analisis Sentimen Komentar Penonton Film Penyalin Cahaya: IndoBERT vs SVM: Sentiment Analysis of Audience Comments on the Film Photocopier Rahmadi, Daffa Junichi; Jaya, Safitri
MALCOM: Indonesian Journal of Machine Learning and Computer Science Vol. 6 No. 3 (2026): MALCOM July 2026
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/malcom.v6i3.2784

Abstract

Penelitian ini membuktikan keunggulan Indonesian Bidirectional Encoder Representations from Transformers (IndoBERT) dibandingkan dengan Support Vector Machine (SVM) dalam analisis sentimen pada data komentar di platform digital, khususnya terkait isu yang diangkat dalam film Penyalin Cahaya. Data untuk penelitian ini diperoleh melalui scraping dari lima platform digital, dengan total 12.473 komentar. Tahap preprocessing yang diterapkan meliputi cleaning, normalization, tokenization, penghapusan stopword, dan stemming. Untuk mengatasi ketidakseimbangan data, penelitian ini menggunakan metode Random Over-Sampling (ROS) dan Synthetic Minority Over-Sampling Technique (SMOTE). Hasil evaluasi menunjukkan bahwa IndoBERT memperoleh nilai F1-Score 0.80, sedangkan SVM memperoleh 0.74. Meskipun kedua model memiliki nilai AUC sebesar 0.94. IndoBERT memiliki performa yang lebih baik dalam memahami konteks kalimat, khususnya pada sentimen netral dan negatif. IndoBERT juga lebih unggul dalam menghasilkan prediksi yang lebih akurat dan seimbang pada setiap kategori sentimen. Hasil analisis juga memperlihatkan bahwa opini publik didominasi oleh sentimen positif yang mencerminkan apresiasi terhadap film serta dukungan terhadap isu yang diangkat.
Co-Authors Abimanyu Luthfi Rizq Ramadhan Anugerah Ganesha Prasojo Aura Natasya Carla Suci Lestari Chaerul Anwar Edwin Tandira Ferdy Agustian Giacinta Galuh Dwi Widowati Hardi, Azkaa Rahiila Hendi Hermawan Ida Nurhaida M. Nasucha Marcello Singadji Markalef Wijaya Indraningrat Methodius Antonio Bernhard Wago Mochamad Sobiri Muhammad Wildan Fadhilah Nur Uddin Pandu Purwandaru Prio Handoko Rahmadi, Daffa Junichi Rahmat, Revo Raihan Ashil Zaki Renintha Trianjani Susilo Rifqi Muhammad Rio Azhari Ramadhan Rizki Fadillah Ryan Arya Prakoso Salwa Ahmad Zanjabila Sindy Arif Sembiring Suci Rahmadhona Tamboto, Stanley Alexsander Theofilus Handoyo Yane Arliana Kurniawati Yulianto, Aries