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All Journal JOIV : International Journal on Informatics Visualization INTECH (Informatika dan Teknologi)
Praha, Tohpatti Crippa
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Computer Science & IT

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Indonesian Fake News Classification Using Transfer Learning in CNN and LSTM Praha, Tohpatti Crippa; Widodo, Widodo; Nugraheni, Murien
JOIV : International Journal on Informatics Visualization Vol 8, No 3 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.2.2126

Abstract

Fake news spreads quickly and is challenging to stop due to the ease of accessing and sharing information online. Deep learning techniques are a method that can be used to identify fake news quickly and accurately. The types of neural networks commonly utilized in deep learning architectures include Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM), which can perform well when managing the task of classifying fake news, according to several pertinent studies. Regarding handling instances of Indonesian fake news classification, this study compares how well the CNN and LSTM models perform. However, given that Indonesian is a low-resource language with scant documentation, it is challenging to build an adequate data set. At the same time, the CNN and LSTM classification models require significant training data. We proposed a transfer learning method by combining two classification models with a pre-trained IndoBERT language model. 1340 news text data were used, including 643 actual news texts from CNN Indonesia, Liputan6, and Detik and 697 fake news texts from TurnBackHoax. As a result, the performance of the combination of the LSTM classification model with IndoBERT outperformed that of the CNN classification model with IndoBERT, which only produced an accuracy of 92.91%, down by 6%, and was able to produce an accuracy of up to 97.76%, an increase of 4.8% from before. Furthermore, the results show that the LSTM classification model outperforms the CNN classification model in capturing the representation created by IndoBERT. Additionally, these insights may serve as a basis for future research on identifying fake news in Indonesia, helping to improve methods for combatting misinformation in Indonesia.
Penerapan Metode Scrum pada Pembuatan Aplikasi Sistem Tanda Tangan Digital dengan QR Code Berbasis Website Kamila, Cahya; Putra, Yoga Rizky; Praha, Tohpatti Crippa
INTECH Vol. 3 No. 1 (2022): INTECH (Informatika Dan Teknologi)
Publisher : Informatics Study Program, Faculty of Engineering and Computers, Baturaja University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54895/intech.v3i1.1175

Abstract

Sign systems that are still traditional are considered less effective and are starting to be replaced by digital because they can save resources and time compared to having to meet in person the traditional way. According to several studies that have been reviewed previously, the use of digital signatures using a QR Code based on a website has proven to be effective and efficient to implement. At this time the use of digital signatures has begun to be used in various places, because signatures that are done manually using a wet pen are considered less efficient because it requires a long process and is easy to manipulate, which causes the validity of the signature to decrease. . With these problems, we developed a Website-Based Digital Signature System for the Informatics and Computer Engineering Education Study Program, State University of Jakarta by applying the scrum method. Scrum is an additional responsive framework for developing software and managing product or application development. Scrum is a complex process in which many factors affect the final result. Based on our development, we conclude that it is proven that the existence of a website-based signature application using a QR Code can make the signature process more efficient and effective and can increase the level of validity of the signature because QR Codes are difficult to manipulate. that we created can be applied in many places and reduce the problem of the length of the process of requesting a signature.
Co-Authors Kamila, Cahya Murien Nugraheni Putra, Yoga Rizky Widodo Widodo