Garuda - Garba Rujukan Digital

p-Index From 2021 - 2026

0.817

P-Index

This Author published in this journals

All Journal International Journal of Electrical and Computer Engineering International Journal of Evaluation and Research in Education (IJERE) Bulletin of Electrical Engineering and Informatics Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Bulletin of Electrical Engineering and Informatics Bulletin of Electrical Engineering and Informatics Indonesian Journal of Electrical Engineering and Computer Science

Shahreen Kasim

Universiti Tun Hussein Onn Malaysia

Author-ID : 2372845

Computer Science & IT Education Electrical & Electronics Engineering Engineering

Published : 23 Documents Claim Missing Document

Claim Missing Document

Articles

Title

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.

Co-Authors Ahmad Firdaus Ahmad Firdaus Zainal Abidin Ahmad Heryanto Ahmad Hoirul Basori Ahmed Hussein Ali Anis Farihan Mat Raffei Anis Farihan Mat Raffei Arda Yunianta Azizul Azhar Ramli Choon Sen Seah Danakorn Nincarean Eh Phon Danakorn Nincarean Eh Phon Deris Stiawan Farid Zamani Che Rose Farizuwana Akma Zulkifle Haza Nuzly Abdul Hamed Hind Raad Ibraheem Hishammuddin Asmuni Ichwanul Muslim Karo Karo John Arthur Jupin Lina Handayani Mohammad Alsajri Mohammad Nazir Ahmad Mohammed K. Ali Mohd Arfian Ismail Mohd Arfian Ismail Mohd Faaizie Darmawan Mohd Faizal Ab Razak Mohd Faizal Ab Razak Mohd Farhan Md Fudzee Mohd Saberi Mohamad Mohd Shahizan Othman Mohd Zamri Osman Muhammed E. Abd Alkhalec Tharwat Ngahzaifa Ab. Ghani Nithya Dewi Subramaniam Chetty Noor Azida Binti Sahabudin Noor Hidayah Binti Zakaria Norhasyimah Hamzah Nur Aqilah Khadijah Rosili Nur Ariffin Mohd Zin Nur Shamsiah Abdul Rahman Nur Sholihah Zaini Nur Syaza Izzati Mohd Rafei Nurul Najihah Che Razali Nuryono Satya Widodo Ong Vienna Lee Razib M. Othman Rd. Rohmat Saedudin Rodziah Atan Rohayanti Hassan Rosfuzah Roslan Shahid Anwar Shahliza Abd Halim Syifak Izhar Hisham Tole Sutikno Waleed Mugahed Al-Rahmi Wan Isni Sofiah Wan Din Yundari, Yundari Zahraa Faiz Hussain Zalmiyah Zakaria Zuraina Ali

Title Search

Found 4 Documents Search Journal : International Journal of Electrical and Computer Engineering

Abstract

Abstract

Abstract

Abstract

Title

Found 4 Documents
Search
Journal : International Journal of Electrical and Computer Engineering