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All Journal International Journal of Electrical and Computer Engineering Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Indonesian Journal of Electrical Engineering and Computer Science
Azizul Azhar Ramli
Universiti Tun Hussein Onn Malaysia
Computer Science & IT Electrical & Electronics Engineering

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Multi-objective NSGA-II based community detection using dynamical evolution social network Muhammed E. Abd Alkhalec Tharwat; Mohd Farhan Md Fudzee; Shahreen Kasim; Azizul Azhar Ramli; Mohammed K. Ali
International Journal of Electrical and Computer Engineering (IJECE) Vol 11, No 5: October 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v11i5.pp4502-4512

Abstract

Community detection is becoming a highly demanded topic in social networking-based applications. It involves finding the maximum intraconnected and minimum inter-connected sub-graphs in given social networks. Many approaches have been developed for community’s detection and less of them have focused on the dynamical aspect of the social network. The decision of the community has to consider the pattern of changes in the social network and to be smooth enough. This is to enable smooth operation for other community detection dependent application. Unlike dynamical community detection Algorithms, this article presents a non-dominated aware searching Algorithm designated as non-dominated sorting based community detection with dynamical awareness (NDS-CD-DA). The Algorithm uses a non-dominated sorting genetic algorithm NSGA-II with two objectives: modularity and normalized mutual information (NMI). Experimental results on synthetic networks and real-world social network datasets have been compared with classical genetic with a single objective and has been shown to provide superiority in terms of the domination as well as the convergence. NDS-CD-DA has accomplished a domination percentage of 100% over dynamic evolutionary community searching DECS for almost all iterations.
Sentiment Analysis in Karonese Tweet using Machine Learning Ichwanul Muslim Karo Karo; Mohd Farhan Md Fudzee; Shahreen Kasim; Azizul Azhar Ramli
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 10, No 1: March 2022
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v10i1.3565

Abstract

Recently, many social media users expressed their conditions, ideas, emotions using local languages ​​on social media, for example via tweets or status. Due to the large number of texts, sentiment analysis is used to identify opinions, ideas, or thoughts from social media. Sentiment analysis research has also been widely applied to local languages. Karonese is one of the largest local languages ​​in North Sumatera, Indonesia. Karo society actively use the language in expression on twitter. This study proposes two things: Karonese tweet dataset for classification and analysis of sentiment on Karonese. Several machine learning algorithms are implemented in this research, that is Logistic regression, Naive bayes, K-nearest neighbor, and Support Vector Machine (SVM). Karonese tweets is obtained from timeline twitter based on several keywords and hashtags. Transcribers from ethnic figures helped annotating the Karo tweets into three classes: positive, negative, and neutral. To get the best model, several scenarios were run based on various compositions of training data and test data. The SVM algorithm has highest accuracy, precision, recall, and F-1 scores than others. As the research is a preliminary research of sentiment analysis on Karonese language, there are many feature works to improvement.
User behaviour pattern for online learning system: UiTM iLearn portal case Siti Fairuz Nurr Sadikan; Azizul Azhar Ramli; Mohd Farhan Md. Fudzee; Siti Sapura Jailani; Mohd Ali Mohd Isa; Prasanna Ramakrisnan; Roslani Embi
Indonesian Journal of Electrical Engineering and Computer Science Vol 15, No 1: July 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v15.i1.pp382-390

Abstract

A Web server log files contain an entire record of the user’s browsing history such as referrer, date and time access, path, operating system (OS), browser and IP address. User navigation pattern discovery involves learning of user’s browsing behaviour to gain the pattern from web server log file. This paper emphasizes on identifying user navigation pattern from web server log file data of iLearn portal. The study implements the framework for user navigation including phases of acquisition of weblog, log query parser, preprocessor, navigational pattern modelling, clustering, and classification. This study is conducted in the context of the actual data logs of the iLearn portal of Universiti Teknologi MARA (UiTM). This study revealed the navigational patterns of online learners which relatively related to their intake or group along the semester of 14 weeks. Besides, access patterns for students along the semester are different and can be classified into three (3) quarter, namely Q1, Q2 and Q3 based on the total of week per semester. Future work will focus on the development of prototype to improve the security of online learning especially during the assessment progress such as online quiz, test and examination.
Rain prediction using fuzzy rule based system in North-West malaysia Noor Zuraidin Mohd Safar; Azizul Azhar Ramli; Hirulnizam Mahdin; David Ndzi; Ku Muhammad Naim Ku Khalif
Indonesian Journal of Electrical Engineering and Computer Science Vol 14, No 3: June 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v14.i3.pp1564-1573

Abstract

The warm and humid condition is the characteristic of Malaysia tropical climate. Prediction of rain occurrences is important for the daily operations and decisions for the country that rely on agriculture needs. However predicting rainfall is a complex problem because it is effected by the dynamic nature of the tropical weather parameters of atmospheric pressure, temperature, humidity, dew point and wind speed. Those parameters have been used in this study. The rainfall prediction are compared and analyzed.   Fuzzy Logic and Fuzzy Inference System can deal with ambiguity that often occurred in meteorological prediction; it can easily incorporate with expert knowledge and empirical study into standard mathematical. This paper will determine the dependability of Fuzzy Logic approach in rainfall prediction within the given approximation of rainfall rate, exploring the use of Fuzzy Logic and to develop the fuzzified model for rainfall prediction. The accuracy of the proposed Fuzzy Inference System model yields 72%
Co-Authors David Ndzi Hirulnizam Mahdin Ichwanul Muslim Karo Karo Ku Muhammad Naim Ku Khalif Mohammed K. Ali Mohd Ali Mohd Isa Mohd Farhan Md Fudzee Mohd Farhan Md. Fudzee Muhammed E. Abd Alkhalec Tharwat Noor Zuraidin Mohd Safar Prasanna Ramakrisnan Roslani Embi Shahreen Kasim Siti Fairuz Nurr Sadikan Siti Sapura Jailani