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All Journal JOIV : International Journal on Informatics Visualization
A Hamid, Isredza Rahmi
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Computer Science & IT

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A Multi-tier Model and Filtering Approach to Detect Fake News Using Machine Learning Algorithms Chang Yu, Chiung; A Hamid, Isredza Rahmi; Abdullah, Zubaile; Kipli, Kuryati; Amnur, Hidra
JOIV : International Journal on Informatics Visualization Vol 8, No 2 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.2.2703

Abstract

Fake news trends have overgrown in our societies over the years through social media platforms. The goal of spreading fake news can easily mislead and manipulate the public’s opinion. Many previous researchers have proposed this domain using classification algorithms or deep learning techniques. However, machine learning algorithms still suffer from high margin error, which makes them unreliable as every algorithm uses a different way of prediction. Deep learning requires high computation power and a large dataset to operate the classification model. A filtering model with a consensus layer in a multi-tier model is introduced in this research paper. The multi-tier model filters the news label correctly predicted by the first two-tier layer. The consensus layer acts as the final decision when collision results occur in the first two-tier layer. The proposed model is applied to the WEKA software tool to test and evaluate the model from both datasets. Two sequences of classification models are used in this research paper: LR_DT_RF and LR_NB_AdaBoost. The best performance of sequence for both datasets is LR_DT_RF which yields 0.9892 F1-Score, 0.9895 Accuracy, and 0.9790 Matthews Correlation Coefficient (MCC) for ISOT Fake News Dataset, and 0.9913 F1-Score, 0.9853 Accuracy, and 0.9455 MCC for CHECKED Dataset. This research could give researchers an approach for fake news detection on different social platforms and feature-based
Verification of Ph.D. Certificate using QR Code on Blockchain Ethereum Noorhizama, Nur Khairunnisa; Abdullah, Zubaile; Kasim, Shahreen; A Hamid, Isredza Rahmi; Mat Isa, Mohd Anuar
JOIV : International Journal on Informatics Visualization Vol 7, No 3 (2023)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.7.3.1584

Abstract

One of the major challenges the university faces is to provide real-time verification of their student's degree certification upon request by other parties. Conventional verification systems are typically costly, time-consuming and bureaucratic against certificate credential misconduct. In addition, the forgery of graduation degree certificates has become more efficient due to easy-to-use scanning, editing, and printing technologies. Therefore, this research proposes verifying Ph.D. certificates using QR codes on the Ethereum blockchain to address certificate verification challenges. Blockchain technology ensures tamper-proof and decentralized management of degree certificates as the certificates stored on the blockchain are replicated across the network. The issuance of certificates requires the use of the issuer's private key, thus preventing forgery. The system was developed using Solidity for the smart contract, PHP, HTML/CSS for the web-based implementation, and MetaMask for blockchain integration. User testing confirmed the successful implementation and functionality of the system. Users can add, update, and delete certificates, generate and scan QR codes, and receive instant verification feedback. The verification system effectively meets all requirements, providing a robust solution for validating Ph.D. certificates. Future research may focus on scalability and adoption, privacy and data protection, user experience, and integration with existing systems. Other researchers can optimize the verification system for widespread adoption and utilization by exploring these areas. This research contributes to securing and efficiently verifying academic certificates using QR codes on the Ethereum blockchain. Ultimately, this work advances the field of certificate verification and promotes trust in academic credentials.
Co-Authors Abdullah, Zubaile Chang Yu, Chiung Hidra Amnur Kipli, Kuryati Mat Isa, Mohd Anuar Noorhizama, Nur Khairunnisa Shahreen Kasim, Shahreen