Article Per Year (5 Year)
p-Index From 2019 - 2024
0.877
P-Index
This Author published in this journals
All Journal Perfecting a Video Game with Game Metrics IJEBD (International Journal Of Entrepreneurship And Business Development) Indonesian Journal of Electrical Engineering and Computer Science
Zailani Abdullah
Universiti Malaysia Kelantan
Computer Science & IT Economics, Econometrics & Finance

Published : 4 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 4 Documents
Search

 1 
Regression test selection model: a comparison between ReTSE and pythia Amir Ngah; Malcolm Munro; Zailani Abdullah; Masita A. Jalil; Mohamad Abdallah
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 17, No 2: April 2019
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v17i2.10332

Abstract

As software systems change and evolve over time regression tests have to be run to validate these changes. Regression testing is an expensive but essential activity in software maintenance. The purpose of this paper is to compare a new regression test selection model called ReTSE with Pythia. The ReTSE model uses decomposition slicing in order to identify the relevant regression tests. Decomposition slicing provides a technique that is capable of identifying the unchanged parts of a system. Pythia is a regression test selection technique based on textual differencing. Both techniques are compare using a Power program taken from Vokolos and Frankl’s paper. The analysis of this comparison has shown promising results in reducing the number of tests to be run after changes are introduced.
i-Eclat: performance enhancement of eclat via incremental approach in frequent itemset mining Wan Aezwani Wan Abu Bakar; Mustafa Man; Mahadi Man; Zailani Abdullah
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 18, No 1: February 2020
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v18i1.13497

Abstract

One example of the state-of-the-art vertical rule mining technique is called equivalence class transformation (Eclat) algorithm. Neither horizontal nor vertical data format, both are still suffering from the huge memory consumption. In response to the promising results of mining in a higher volume of data from a vertical format, and taking consideration of dynamic transaction of data in a database, the research proposes a performance enhancement of Eclat algorithm that relies on incremental approach called an Incremental-Eclat (i-Eclat) algorithm. Motivated from the fast intersection in Eclat, this algorithm of performance enhancement adopts via my structured query language (MySQL) database management system (DBMS) as its platform. It serves as the association rule mining database engine in testing benchmark frequent itemset mining (FIMI) datasets from online repository. The MySQL DBMS is chosen in order to reduce the preprocessing stages of datasets. The experimental results indicate that the proposed algorithm outperforms the traditional Eclat with 17% both in chess and T10I4D100K, 69% in mushroom, 5% and 8% in pumsb_star and retail datasets. Thus, among five (5) dense and sparse datasets, the average performance of i-Eclat is concluded to be 23% better than Eclat.
An Overview of COVID-19 Impacts on Commercial E-Commerce Platforms in Southeast Asia Region Zailani Abdullah; Noorshella Che Nawi
IJEBD (International Journal of Entrepreneurship and Business Development) Vol 4 No 1 (2021): January 2021
Publisher : LPPM of NAROTAMA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (336.002 KB) | DOI: 10.29138/ijebd.v4i1.1124

Abstract

Purpose: Coronavirus disease (COVID-19) is an infectious disease caused by a newly discovered coronavirus in Wuhan, China. Due to this pandemic, almost all countries in Southeast Asia had executed either lockdown or state of emergency action. However, an online marketplace via an E-Commerce platform is still needed to operate and also directly affected by this pandemic. Therefore, this overview reveals the impacts of the top 3 E-commerce platforms during pandemic COVID-19 with regard to the consumers in the Southeast Asia region (specifically in Malaysia, Indonesia & Thailand). Design/methodology/approach: Due to the COVID-19 pandemic, the revenue of E-commerce platforms companies might be slightly a bit dropped due to uncertainty factors in the countries that have been affected by this virus. Indeed many countries around the globe already implemented and still progressing either lockdown or state of emergency protocol. In this study, we selected the top three E-commerce platforms in Southeast Asia namely Lazada, Shopee, and Tokopedia. Findings: The results found that there were several initiations by them to battle against COVID-19. In addition, there are still a lot of improvements that can be effectively played by them. Research limitations/implications: This research is a literature review research. Practical implications: The result of this research can be used as references to run a digital business. Originality/value: This paper is original. Paper type: Research paper
Mining Association Rules: A Case Study on Benchmark Dense Data Mustafa Bin Man; Wan Aezwani Wan Abu Bakar; Zailani Abdullah; Masita@Masila Abd Jalil; Tutut Herawan
Indonesian Journal of Electrical Engineering and Computer Science Vol 3, No 3: September 2016
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v3.i3.pp546-553

Abstract

Data mining is the process of discovering knowledge and previously unknown pattern from large amount of data. The association rule mining (ARM) has been in trend where a new pattern analysis can be discovered to project for an important prediction about any issues. Since the first introduction of frequent itemset mining, it has received a major attention among researchers and various efficient and sophisticated algorithms have been proposed to do frequent itemset mining. Among the best-known algorithms are Apriori and FP-Growth. In this paper, we explore these algorithms and comparing their results in generating association rules based on benchmark dense datasets. The datasets are taken from frequent itemset mining data repository. The two algorithms are implemented in Rapid Miner 5.3.007 and the performance results are shown as comparison. FP-Growth is found to be better algorithm when encountering the support-confidence framework.
Co-Authors Amir Ngah Mahadi Man Malcolm Munro Masita A. Jalil Masita@Masila Abd Jalil Mohamad Abdallah Mustafa Bin Man Mustafa Man Noorshella Che Nawi Tutut Herawan Wan Aezwani Wan Abu Bakar Wan Aezwani Wan Abu Bakar