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Social media for collaborative learning Nur Shamsiah Abdul Rahman; Lina Handayani; Mohd Shahizan Othman; Waleed Mugahed Al-Rahmi; Shahreen Kasim; Tole Sutikno
International Journal of Electrical and Computer Engineering (IJECE) Vol 10, No 1: February 2020
Publisher : Institute of Advanced Engineering and Science

Research on the field of using social media has gained more importance in the recent days due to the rapid development of social media technologies. Looking at the behavioral intention and attitude of using social media for collaborative learning within Malaysian higher educational institutions and the influencing factors in this regard has received little attention by researchers. The study aims at examining the determinants that affect learners’ attitude and behavior intention regarding their use social media to achieve collaborative learning. Such examination is carried out by using the Theory Acceptance Model (TAM) and Unified Theory of Acceptance and Usage of Technology (UTAUT). A total of 243 participants were recruited for this study. The findings indicated that students’ attitudes and behavior are strong indicators of their intentions in terms of using social media in collaborative learning.

A new model for iris data set classification based on linear support vector machine parameter's optimization Zahraa Faiz Hussain; Hind Raad Ibraheem; Mohammad Alsajri; Ahmed Hussein Ali; Mohd Arfian Ismail; Shahreen Kasim; Tole Sutikno
International Journal of Electrical and Computer Engineering (IJECE) Vol 10, No 1: February 2020
Publisher : Institute of Advanced Engineering and Science

Data mining is known as the process of detection concerning patterns from essential amounts of data. As a process of knowledge discovery. Classification is a data analysis that extracts a model which describes an important data classes. One of the outstanding classifications methods in data mining is support vector machine classification (SVM). It is capable of envisaging results and mostly effective than other classification methods. The SVM is a one technique of machine learning techniques that is well known technique, learning with supervised and have been applied perfectly to a vary problems of: regression, classification, and clustering in diverse domains such as gene expression, web text mining. In this study, we proposed a newly mode for classifying iris data set using SVM classifier and genetic algorithm to optimize c and gamma parameters of linear SVM, in addition principle components analysis (PCA) algorithm was use for features reduction.

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

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.

Random forest age estimation model based on length of left hand bone for Asian population Mohd Faaizie Darmawan; Ahmad Firdaus Zainal Abidin; Shahreen Kasim; Tole Sutikno; Rahmat Budiarto
International Journal of Electrical and Computer Engineering (IJECE) Vol 10, No 1: February 2020
Publisher : Institute of Advanced Engineering and Science

In forensic anthropology, age estimation is used to ease the process of identifying the age of a living being or the body of a deceased person. Nonetheless, the specialty of the estimation models is solely suitable to a specific people. Commonly, the models are inter and intra-observer variability as the qualitative set of data is being used which results the estimation of age to rely on forensic experts. This study proposes an age estimation model by using length of bone in left hand of Asian subjects range from newborn up to 18-year-old. One soft computing model, which is Random Forest (RF) is used to develop the estimation model and the results are compared with Artificial Neural Network (ANN) and Support Vector Machine (SVM), developed in the previous case studies. The performance measurement used in this study and the previous case study are R-square and Mean Square Error (MSE) value. Based on the results produced, the RF model shows comparable results with the ANN and SVM model. For male subjects, the performance of the RF model is better than ANN, however less ideal than SVM model. As for female subjects, the RF model overperfoms both ANN and SVM model. Overall, the RF model is the most suitable model in estimating age for female subjects compared to ANN and SVM model, however for male subjects, RF model is the second best model compared to the both models. Yet, the application of this model is restricted only to experimental purpose or forensic practice.

Learning styles and teaching styles determine students’ academic performances Nithya Dewi Subramaniam Chetty; Lina Handayani; Noor Azida Binti Sahabudin; Zuraina Ali; Norhasyimah Hamzah; Shahreen Kasim
International Journal of Evaluation and Research in Education (IJERE) Vol 8, No 4: December 2019
Publisher : Institute of Advanced Engineering and Science

Individuals learn in different ways using several learning styles, but lecturers may not always share material and learning experiences that match students’ learning preferences. Mismatches between learning and teaching styles can lead to disappointment with students are taking, and lead to underperformance among them. The aim of this study is to identify the learning styles of the students enrolled in Universiti Malaysia Pahang who were registered in Programming Technique course and to investigate the relationship between students’ learning styles and teachers’ teaching styles. Five lecturers and 251 students were involved in the study as participants and. Data from students were collected using Leonard, Enid’s VAK Learning Style Survey. Meanwhile, the teaching styles of the lecturers were identified using Grasha and Reichmann’s Teaching Style Survey. The findings revealed that majority of the student’s preferred visual learning style. The result also shows that the lecturers’ teaching styles give an impact towards the students’ academic performance. From this study, we can conclude that teaching styles have significant impacts on students’ learning styles and academic performances.

Review of the machine learning methods in the classification of phishing attack John Arthur Jupin; Tole Sutikno; Mohd Arfian Ismail; Mohd Saberi Mohamad; Shahreen Kasim; Deris Stiawan
Bulletin of Electrical Engineering and Informatics Vol 8, No 4: December 2019
Publisher : Institute of Advanced Engineering and Science

The development of computer networks today has increased rapidly. This can be seen based on the trend of computer users around the world, whereby they need to connect their computer to the Internet. This shows that the use of Internet networks is very important, whether for work purposes or access to social media accounts. However, in widely using this computer network, the privacy of computer users is in danger, especially for computer users who do not install security systems in their computer. This problem will allow hackers to hack and commit network attacks. This is very dangerous, especially for Internet users because hackers can steal confidential information such as bank login account or social media login account. The attacks that can be made include phishing attacks. The goal of this study is to review the types of phishing attacks and current methods used in preventing them. Based on the literature, the machine learning method is widely used to prevent phishing attacks. There are several algorithms that can be used in the machine learning method to prevent these attacks. This study focused on an algorithm that was thoroughly made and the methods in implementing this algorithm are discussed in detail.

Augmented reality: effect on conceptual change of scientific Danakorn Nincarean Eh Phon; Ahmad Firdaus Zainal Abidin; Mohd Faizal Ab Razak; Shahreen Kasim; Ahmad Hoirul Basori; Tole Sutikno
Bulletin of Electrical Engineering and Informatics Vol 8, No 4: December 2019
Publisher : Institute of Advanced Engineering and Science

In recent years, Augmented Reality (AR) has received increasing emphasis and researchers gradually promote it Over the worlds. With the unique abilities to generate virtual objects over the real-world environment, it can enhance user perception. Although AR recognised for their enormous positive impacts, there are still a ton of matters waiting to be discovered. Research studies on AR toward conceptual change, specifically in scientific concept, are particularly limited. Therefore, this research aims to investigate the effect of integrating AR on conceptual change in scientific concepts. Thirty-four primary school students participated in the study. A pre-test and post-test were used to assess participants’ understanding of the scientific concepts before and after learning through AR. The findings demonstrated that 82% among them had misconceptions about the scientific concepts before learning through AR. However, most of them (around 88%) able to correct their misconceptions and shifted to have a scientific conceptual understanding after learning through AR. These findings indicate that AR was effective to be integrated into education to facilitate conceptual change.

Adaboost-multilayer perceptron to predict the student’s performance in software engineering Ahmad Firdaus Zainal Abidin; Mohd Faaizie Darmawan; Mohd Zamri Osman; Shahid Anwar; Shahreen Kasim; Arda Yunianta; Tole Sutikno
Bulletin of Electrical Engineering and Informatics Vol 8, No 4: December 2019
Publisher : Institute of Advanced Engineering and Science

Software Engineering (SE) course is one of the backbones of today's computer technology sophistication. Effective theoretical and practical learning of this course is essential to computer students. However, there are many students fail in this course. There are many aspects that influence a student's performance. Currently, student performance analysis methods just focus on historical achievement and assessment methods given in the class. Need more research to predict student's performance to overcome the problem of student failing. The objective of this research is to perform a prediction for student's performance in the SE using enhanced Multilayer Perceptron (MLP) machine learning classification with Adaboost. This research also investigates the requirements of each student before registering in this course. This research achieved 87.76 percent accuracy in classifying the performance of SE students.

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

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.

Augmented reality: effect on conceptual change of scientific Danakorn Nincarean Eh Phon; Ahmad Firdaus Zainal Abidin; Mohd Faizal Ab Razak; Shahreen Kasim; Ahmad Hoirul Basori; Tole Sutikno
Bulletin of Electrical Engineering and Informatics Vol 8, No 4: December 2019
Publisher : Institute of Advanced Engineering and Science

In recent years, Augmented Reality (AR) has received increasing emphasis and researchers gradually promote it Over the worlds. With the unique abilities to generate virtual objects over the real-world environment, it can enhance user perception. Although AR recognised for their enormous positive impacts, there are still a ton of matters waiting to be discovered. Research studies on AR toward conceptual change, specifically in scientific concept, are particularly limited. Therefore, this research aims to investigate the effect of integrating AR on conceptual change in scientific concepts. Thirty-four primary school students participated in the study. A pre-test and post-test were used to assess participants’ understanding of the scientific concepts before and after learning through AR. The findings demonstrated that 82% among them had misconceptions about the scientific concepts before learning through AR. However, most of them (around 88%) able to correct their misconceptions and shifted to have a scientific conceptual understanding after learning through AR. These findings indicate that AR was effective to be integrated into education to facilitate conceptual change.

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