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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
Computer Science & IT Education Electrical & Electronics Engineering Engineering

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Journal : Bulletin of Electrical Engineering and Informatics

 1 
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

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (723.905 KB) | DOI: 10.11591/eei.v8i4.1344

Abstract

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

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (411.711 KB) | DOI: 10.11591/eei.v8i4.1625

Abstract

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

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (496.113 KB) | DOI: 10.11591/eei.v8i4.1432

Abstract

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.
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