Development of alguard website fake news detector using nlp, lime and cnn-rnn algorithms/ Christian Kelly A. Biag, Mark Jason R. Tabiliran, Lance Nathan B. Tubeo, Merham John H. Ungad, and Benjie D. Vega.--

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Biag, Christian Kelly A [author]

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TextPublication details: Manila: Technological University of the Philippines, 2025.Description: xii, 127pages: 29cmContent type:

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BTH QA 76.9 B53 2025

Dissertation note: College of Industrial Technology.-- Bachelor of engineering technology major in computer engineering technology: Technological University of the Philippines, 2025. Summary: The study ALGUARD: A Website Fake News Detector using NLP, LIME, CNN-RNN, is made to determine the facts of local online outlets' fake news sources in the Philippines. The goal of this study is to classify and disseminate counterfeit news that is shared on the nation's internet media platforms. and give relevant and accurate analysis on the news. The URL is copied to the scanner sensor QR codes that analyzes the percentage of accuracy of the given news based on the keywords compared with the original and fake news. The sixty percent result must be achieved in the comparison of URLs to the local news sources to identify whether the URL article is authentic or not. The algorithms used are Natural Language Processing (NLP), Local Interpretable Model-agnostic Explanations (LIME), and a Convolutional Neural Network-Recurrent Neural Network (CNN-RNN). The study makes use of an agile development methodology and incorporates iterative testing and enhancements. The system performance was tested F1 scoring, and user feedback from social media users and journalists. There were 30 respondents who were composed of different internet users . The ISO/IEC 25002:2024 instrument of evaluation was used to assess the performance of the ALGUARD system and obtained an overall mean 4.37 which is described as “Very Satisfactory”. This defines the developed system’s capability to detect fake news. This study supports UN-SDG 16 by promoting access to reliable information and protecting media integrity and freedom. UN-SDG 12 helps promote the responsible consumption of fake news on online platforms, reducing fake news articles from spreading online. UN-SDG 11 helps create a safe and reliable internet community through safe and authentic news coverage in the Philippines.

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Item type	Current library	Shelving location	Call number	Copy number	Status	Date due	Barcode
Bachelor's Thesis CIT	TUP Manila Library	Thesis Section-2nd floor	BTH QA 76.9 B53 2025 (Browse shelf(Opens below))	c.1.	Not for loan		BTH0006325

Bachelor's thesis

College of Industrial Technology.-- Bachelor of engineering technology major in computer engineering technology: Technological University of the Philippines, 2025.

Includes bibliographic references and index.

The study ALGUARD: A Website Fake News Detector using NLP, LIME, CNN-RNN, is made to
determine the facts of local online outlets' fake news sources in the Philippines. The goal of this
study is to classify and disseminate counterfeit news that is shared on the nation's internet media
platforms. and give relevant and accurate analysis on the news. The URL is copied to the scanner
sensor QR codes that analyzes the percentage of accuracy of the given news based on the
keywords compared with the original and fake news. The sixty percent result must be achieved in
the comparison of URLs to the local news sources to identify whether the URL article is authentic
or not. The algorithms used are Natural Language Processing (NLP), Local Interpretable
Model-agnostic Explanations (LIME), and a Convolutional Neural Network-Recurrent Neural
Network (CNN-RNN). The study makes use of an agile development methodology and
incorporates iterative testing and enhancements. The system performance was tested F1 scoring,
and user feedback from social media users and journalists. There were 30 respondents who were
composed of different internet users . The ISO/IEC 25002:2024 instrument of evaluation was
used to assess the performance of the ALGUARD system and obtained an overall mean 4.37
which is described as “Very Satisfactory”. This defines the developed system’s capability to
detect fake news. This study supports UN-SDG 16 by promoting access to reliable information
and protecting media integrity and freedom. UN-SDG 12 helps promote the responsible
consumption of fake news on online platforms, reducing fake news articles from spreading
online. UN-SDG 11 helps create a safe and reliable internet community through safe and
authentic news coverage in the Philippines.

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