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Slangs and Short forms of Malay Twitter Sentiment Analysis using Supervised Machine Learning
Yin, Cheng Jet,Ayop, Zakiah,Anawar, Syarulnaziah,Othman, Nur Fadzilah,Zainudin, Norulzahrah Mohd International Journal of Computer ScienceNetwork S 2021 International journal of computer science and netw Vol.21 No.11
The current society relies upon social media on an everyday basis, which contributes to finding which of the following supervised machine learning algorithms used in sentiment analysis have higher accuracy in detecting Malay internet slang and short forms which can be offensive to a person. This paper is to determine which of the algorithms chosen in supervised machine learning with higher accuracy in detecting internet slang and short forms. To analyze the results of the supervised machine learning classifiers, we have chosen two types of datasets, one is political topic-based, and another same set but is mixed with 50 tweets per targeted keyword. The datasets are then manually labelled positive and negative, before separating the 275 tweets into training and testing sets. Naïve Bayes and Random Forest classifiers are then analyzed and evaluated from their performances. Our experiment results show that Random Forest is a better classifier compared to Naïve Bayes.
URL Phishing Detection System Utilizing Catboost Machine Learning Approach
Fang, Lim Chian,Ayop, Zakiah,Anawar, Syarulnaziah,Othman, Nur Fadzilah,Harum, Norharyati,Abdullah, Raihana Syahirah International Journal of Computer ScienceNetwork S 2021 International journal of computer science and netw Vol.21 No.9
The development of various phishing websites enables hackers to access confidential personal or financial data, thus, decreasing the trust in e-business. This paper compared the detection techniques utilizing URL-based features. To analyze and compare the performance of supervised machine learning classifiers, the machine learning classifiers were trained by using more than 11,005 phishing and legitimate URLs. 30 features were extracted from the URLs to detect a phishing or legitimate URL. Logistic Regression, Random Forest, and CatBoost classifiers were then analyzed and their performances were evaluated. The results yielded that CatBoost was much better classifier than Random Forest and Logistic Regression with up to 96% of detection accuracy.
Cyberbullying Detection in Twitter Using Sentiment Analysis
Theng, Chong Poh,Othman, Nur Fadzilah,Abdullah, Raihana Syahirah,Anawar, Syarulnaziah,Ayop, Zakiah,Ramli, Sofia Najwa International Journal of Computer ScienceNetwork S 2021 International journal of computer science and netw Vol.21 No.11
Cyberbullying has become a severe issue and brought a powerful impact on the cyber world. Due to the low cost and fast spreading of news, social media has become a tool that helps spread insult, offensive, and hate messages or opinions in a community. Detecting cyberbullying from social media is an intriguing research topic because it is vital for law enforcement agencies to witness how social media broadcast hate messages. Twitter is one of the famous social media and a platform for users to tell stories, give views, express feelings, and even spread news, whether true or false. Hence, it becomes an excellent resource for sentiment analysis. This paper aims to detect cyberbully threats based on Naïve Bayes, support vector machine (SVM), and k-nearest neighbour (k-NN) classifier model. Sentiment analysis will be applied based on people's opinions on social media and distribute polarity to them as positive, neutral, or negative. The accuracy for each classifier will be evaluated.
Development of Sustainable Home-Network Security Tool
Hamid, Erman,Hasbullah, M. Syafiq E.,Harum, Norharyati,Anawar, Syarulnaziah,Ayop, Zakiah,Zakaria, Nurul Azma,Shah, Wahidah Md International Journal of Computer ScienceNetwork S 2021 International journal of computer science and netw Vol.21 No.11
Home networking and its security issues are directly related. Previous studies have shown that home-network and understanding the security of it is a problem for non-technical users. The existing network management tools or ISP adapter tools are far too technical and difficult to be understood by ordinary home-network users. Its interface is not non-technical user-directed and does not address the home user's needs in securing their network. This paper presents an interactive security monitoring tool, which emphasizes support features for home-network users. The tool combines an interactive visual appearance with a persuasive approach that supports sustainability. It is not only an easy-to-use tool for all categories of home-network users but also acts as a monitoring feature for the user to secure their home-network.