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All Journal JURNAL SISTEM INFORMASI BISNIS JURNAL MEDIA INFORMATIKA BUDIDARMA
Prastyo, Edwin Hari Agus
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Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Electrical & Electronics Engineering

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Meningkatkan Akurasi Deteksi Berita Palsu dengan Pendekatan Berbasis Lexicon dan LSTM melalui Text Preprocessing dan Model Training Prastyo, Edwin Hari Agus; Faisal, M
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 8, No 3 (2024): Juli 2024
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v8i3.7847

Abstract

Hoax news is an issue that is troubling the global community, including Indonesia. The spread of hoax news can cause various negative impacts, such as social division, public unrest, and can even endanger life safety. Hoaxes have become an epidemic in Indonesia, with 11,357 hoax issues identified by the Ministry of Communication and Information from August 2018 to March 2023. The combined approach of Lexicon-Based and LSTM results in improved accuracy in detecting hoax news. The combination of lexicon filters and pre-trained LSTM enables the model to identify hoax keywords and classify news with an accurate final score. Experimental results show that the use of Adam's optimizer produces high accuracy, achieving precision =1.0, recall=1.0, F1-score =1.0, and accuracy of 0.99. The model is able to perfectly distinguish between hoax and non-hoax news, demonstrating the effectiveness of using combined techniques and the right optimizer. However, there are some drawbacks that need to be considered, such as the reliance on a lexicon that may be incomplete and the potential for overfitting of the LSTM model. The results of this study provide insight into the importance of combined techniques in fake news detection, as well as the need for parameter adjustments and optimization strategies to minimize the drawbacks.
Meningkatkan Akurasi Deteksi Berita Palsu dengan Pendekatan Berbasis Lexicon dan LSTM melalui Text Preprocessing dan Model Training Prastyo, Edwin Hari Agus; Faisal, M
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 8, No 3 (2024): Juli 2024
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v8i3.7847

Abstract

Hoax news is an issue that is troubling the global community, including Indonesia. The spread of hoax news can cause various negative impacts, such as social division, public unrest, and can even endanger life safety. Hoaxes have become an epidemic in Indonesia, with 11,357 hoax issues identified by the Ministry of Communication and Information from August 2018 to March 2023. The combined approach of Lexicon-Based and LSTM results in improved accuracy in detecting hoax news. The combination of lexicon filters and pre-trained LSTM enables the model to identify hoax keywords and classify news with an accurate final score. Experimental results show that the use of Adam's optimizer produces high accuracy, achieving precision =1.0, recall=1.0, F1-score =1.0, and accuracy of 0.99. The model is able to perfectly distinguish between hoax and non-hoax news, demonstrating the effectiveness of using combined techniques and the right optimizer. However, there are some drawbacks that need to be considered, such as the reliance on a lexicon that may be incomplete and the potential for overfitting of the LSTM model. The results of this study provide insight into the importance of combined techniques in fake news detection, as well as the need for parameter adjustments and optimization strategies to minimize the drawbacks.
Improving Fake News Detection Accuracy with Lexicon-based Approach and LSTM through Text Preprocessing Mashuri, Chamdan; Prastyo, Edwin Hari Agus; Hariri, Fajar Rohman
Jurnal Sistem Informasi Bisnis Vol 15, No 2 (2025): Volume 15 Number 2 Year 2025
Publisher : Diponegoro University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/vol15iss2pp285-292

Abstract

Fake news detection has become a critical issue in the digital era, especially with the rapid growth of social media and online platforms. This research aims to enhance the accuracy of detecting fake news in Indonesian by developing a model using lexicon-based and Long Short-Term Memory (LSTM) approaches. The study integrates sentiment analysis with lexicon-based scoring to identify key features in news articles, while LSTM is employed to analyze sequential patterns in the data. The methods were tested on a dataset consisting of both hoax and non-hoax news collected from reliable sources. The results indicate that the hybrid model significantly improves the detection accuracy, achieving an impressive accuracy rate of 99%. This research demonstrates the potential of combining lexicon-based and LSTM approaches to overcome challenges in detecting fake news, especially in low-resource languages like Indonesian. The findings contribute to advancing the development of reliable and efficient systems for combating misinformation in the digital age.
Co-Authors Fajar Rohman Hariri M Faisal Mashuri, Chamdan