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All Journal Indonesian Journal of Electrical Engineering and Computer Science
Mokhairi Makhtar
Universiti Sultan Zainal Abidin

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University course timetabling model using ant colony optimization algorithm approach Munirah Mazlan; Mokhairi Makhtar; Ahmad Firdaus Khair Ahmad Khairi; Mohamad Afendee Mohamed
Indonesian Journal of Electrical Engineering and Computer Science Vol 13, No 1: January 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v13.i1.pp72-76

Abstract

Due to the increased number of students and regulations, all educational institutions have renewed their interest to appear in the number of complexity and flexibility since the resources and events are becoming more difficult to be scheduled. Timetabling is the type of problems where the events need to be organized into a number of timeslots to prevent the conflicts in using a given set of resources. Thus in the intervening decades, significant progress has been made in the course timetabling problem monitoring with meta-heuristic adjustment. In this study, ant colony optimization (ACO) algorithm approach has been developed for university course timetabling problem. ACO is believed to be a powerful solution approach for various combinatorial optimization problems. This approach is used according to the data set instances that have been collected. Its performance is presented using the appropriate algorithm. The results are arguably within the best results range from the literature. The performance assessment and results are used to determine whether they are reliable in preparing a qualifying course timetabling process.
The patterns of accessing learning management system among students Akibu Mahmoud Abdullahi; Mokhairi Makhtar; Suhailan Safie
Indonesian Journal of Electrical Engineering and Computer Science Vol 13, No 1: January 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v13.i1.pp15-21

Abstract

Learning Management System (LMS) is an online software that was hosted on a server and designed specifically to manage learners’ information, course registration, learning content, and assessment tool. Educational data mining is a way of evaluating and using methods for examining the unique and large dataset that come from educational field, and applying those in order to understand how students learn and the settings in which they learn. Many students use to miss some of the activities posted by their instructors, due to the short deadline, and they are not accessing the LMS regularly or every day. The purpose of this paper is to explore the way on how student access LMS and which day is the most frequent accessed. The findings show that, the total number of accessing LMS among 33 students is 16060, and the mean is 486.67, S16 recorded the highest number of accessing the LMS (965 access), while S24 as the least number of access (275). And the correlation between Tuesdays is significant, positive and strong correlation with Wednesdays (0.546), and positive, but weak with Thursdays (0.292), Fridays (0.244), Saturdays (0.334), and Sundays (0.291).
An Efficient Approach to Detecting Missing Tags in RFID Data Stream Nur’Aifaa Zainudin; Hairulnizam Mahdin; Deden Witarsyah; Mokhairi Makhtar; Mohd Izuan Hafez Ninggal; Zirawani Baharum
Indonesian Journal of Electrical Engineering and Computer Science Vol 11, No 3: September 2018
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v11.i3.pp1204-1213

Abstract

RFID technology is a Radio frequency identification system that provides a reader reading the data item from its tag. Nowadays, RFID system has rapidly become more common in our life because of its autonomous advantages compared to the traditional barcode. It can detect hundreds of tagged items automatically at a time. However, in RFID, missing tag detection can occur due to signal collisions and interferences. It will cause the system to report incorrect tag’s count due to an incorrect number of tags being detected. The consequences of this problem can be enormous to business, as it will cause incorrect business decisions to be made. Thus, a Missing Tag Detection Algorithm (MTDA) is proposed to solve the missing tag detection problem. There are many other existing approaches has been proposed including Window Sub-range Transition Detection (WSTD), Efficient Missing-Tag Detection Protocol (EMD) and Multi-hashing based Missing Tag Identification (MMTI) protocol. The result from experiments shows that our proposed approach performs better than the other in terms of execution time and reliability.
A comparative analysis of classification techniques on predicting flood risk Nazri Mohd Nawi; Mokhairi Makhtar; Mohd Zaki Salikon; Zehan Afizah Afip
Indonesian Journal of Electrical Engineering and Computer Science Vol 18, No 3: June 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v18.i3.pp1342-1350

Abstract

Flood is a temporary overflow of a dry area due to overflow of excess water, runoff surface waters or undermining of shoreline. In Malaysia itself in 2014, the country grieved with the catastrophic flood event in Kuala Krai, Kelantan, which caused of human lives, public assets and money lost. Due to uncertainties in flooding event, it is vital for Malaysia to have pre-warning system that assist related agencies in to categorize land areas that face high risk of flood so preventive actions can be planned in place. This paper conducts a comparative analysis of three classifications in classifying the risk of flood, whether high or low. The classification experiment conducts using three variants of Bayesian approaches, which are Bayesian Networks (BN), Naive Bayes (NB), and Tree Augmented Naive Bayes (TAN). The outcome of this research shows that Tree Augmented Naive Bayes (TAN) has the best algorithms as compared to others algorithms in classifying the risk of flood.
Parkinson disease classification: a comparative analysis on classification techniques Nazri Mohd Nawi; Mokhairi Makhtar; Zehan Afizah Afip; Mohd Zaki Salikon
Indonesian Journal of Electrical Engineering and Computer Science Vol 18, No 3: June 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v18.i3.pp1351-1358

Abstract

Parkinson’s disease (PD) among Alzheimer’s and epilepsy are one of the most common neurological disorders which appreciably affect not only live of patients but also their households. According to the current trend of aging social behaviour, it is expected to see a rise of Parkinson’s disease. Even though there is no cure for PD, a proper medication at the early stage can help significantly in alleviating the symptoms. Since, the traditional method for identifying PD is rather invasive, expansive and complicated for self-use, there is a high demand for using classification method on PD detection. This paper compares the performance of Neural Network and decision tree for classifying and discriminating healthy people for people with Parkinson’s disease (PD) by distinguishing dysphonia. The simulation results demonstrate that Neural Network outperformed decision tree by giving accurate results with 87% accuracy as compared to decision tree with only 84% accuracy in determining the classification of healthy and people with Parkinson’s.
An ant colony algorithm for universiti sultan zainal abidin examination timetabling problem Ahmad Firdaus Khair; Mokhairi Makhtar; Munirah Mazlan; Mohamad Afendee Mohamed; Mohd Nordin Abdul Rahman
Indonesian Journal of Electrical Engineering and Computer Science Vol 13, No 1: January 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v13.i1.pp191-198

Abstract

The real-life construction of examination timetabling problem is considered as a common problem that always encountered and experienced in educational institution whether in school, college, and university. This problem is usually experienced by the academic management department where they have trouble to handle complexity for assign examination into a suitable timeslot manually. In this paper, an algorithm approach of ant colony optimisation (ACO) is presented to find an effective solution for dealing with Universiti Sultan Zainal Abidin (UniSZA) examination timetabling problems. A combination of heuristic with ACO algorithm contributes the development solution in order to simplify and optimize the pheromone occurrence of matrix updates which include the constraints problem. The implementation of real dataset instances from academic management is applied to the approach for generating the result of examination timetable. The result and performance that obtained will be used for further use to evaluate the quality and observe the solution whether our examination timetabling system is reliable and efficient than the manual management that can deal the constraints problem.
Co-Authors Ahmad Firdaus Khair Ahmad Firdaus Khair Ahmad Khairi Akibu Mahmoud Abdullahi Deden Witarsyah Hairulnizam Mahdin Mohamad Afendee Mohamed Mohd Izuan Hafez Ninggal Mohd Nordin Abdul Rahman Mohd Zaki Salikon Munirah Mazlan Nazri Mohd Nawi Nur’Aifaa Zainudin Suhailan Safie Zehan Afizah Afip Zirawani Baharum