Article Per Year (5 Year)
p-Index From 2021 - 2026
0.61
P-Index
This Author published in this journals
All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control) IJEBD (International Journal Of Entrepreneurship And Business Development) Indonesian Journal of Electrical Engineering and Computer Science NEUTRON : Research from all areas of the Civil Engineering
Zailani Abdullah
Universiti Malaysia Kelantan
Civil Engineering, Building, Construction & Architecture Computer Science & IT Economics, Econometrics & Finance Engineering Transportation

Published : 6 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 6 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.
INTERNET OF THINGS (IoT) ARCHITECTURE AND PREDICTIVE ANALYTICS FOR SMART MARITIME TRANSPORTATION: A CONCEPTUAL FRAMEWORK INFORMED BY INDONESIAN PORT STATISTICS Aji Ilham Atthoriq; Sri Wiwoho Mudjanarko; Zailani Abdullah
Neutron Vol 24 No 01 (2024): JULY 2024
Publisher : NAROTAMA UNIVERSITY, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29138/neutron.v24i01.295

Abstract

Indonesia’s inter-island transport network motivates scalable and interoperable maritime information systems. This study develops a four-layer Internet of Things (IoT) and predictive-analytics architecture for port and vessel monitoring and synthesizes relevant peer-reviewed literature with official Indonesian sea-transport publication metadata for 2020–2024. The framework links field sensing (RFID, GNSS and environmental/engine telemetry), multi-protocol communications, edge analytics and port decision-support applications. The original draft reports cargo, ship-call and passenger series; these manuscript-supplied numerical observations are reproduced for editorial traceability but are not independently confirmed against the individual tables in the BPS source PDFs. Consequently, they are treated as figures requiring verification rather than validated empirical findings. The literature supports the technical relevance of IoT-enabled visibility and AIS-based vessel arrival forecasting, but the proposed Indonesian architecture has not been implemented or subjected to operational performance tests. The paper specifies testable performance indicators, data provenance requirements and a deployment pathway for future pilot validation. The principal contribution is therefore a source-traceable conceptual design and an explicit separation between reported statistical observations and untested technological benefits
INTEGRATION OF ARTIFICIAL INTELLIGENCE AND SUSTAINABLE MARITIME TRANSPORTATION IN OPTIMIZING INDONESIA’S BLUE ECONOMY: A DPSIR FRAMEWORK ANALYSIS BASED ON BPS SDLP 2023 OFFICIAL DATA Ni'matul Fahdiyah; Koespiadi Koespiadi; Zailani Abdullah
Neutron Vol 24 No 01 (2024): JULY 2024
Publisher : NAROTAMA UNIVERSITY, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29138/neutron.v24i01.296

Abstract

This paper examines how artificial intelligence (AI) could be integrated with sustainable maritime transportation and fisheries governance to support Indonesia’s blue economy. It develops a conceptual extension of the Drivers–Pressures–State–Impact–Response (DPSIR) framework using secondary statistics in Indonesia’s official Statistics of Marine and Coastal Resources 2023 (BPS SDLP 2023) and traceable maritime, environmental and policy literature. Reported fishery gross domestic product at current prices grew from IDR 385.9 trillion in 2018 to IDR 505.1 trillion in 2022, while 2022 fishery exports reached 1,224,060 tonnes and US$6.242 billion [1]. The 2022 marine capture fleet comprised 893,258 craft, of which 100,109 were non-powered and 429,113 were outboard-motor boats [1]. An AI-enabled architecture is proposed for vessel monitoring, satellite-image analysis, route decision support and conservation surveillance. These functions are mapped to DPSIR response mechanisms, but have not been experimentally implemented or validated in this study. The findings therefore concern documented sector conditions and a research-informed integration framework, not demonstrated AI-derived fuel savings or environmental impacts.
Co-Authors Aji Ilham Atthoriq Amir Ngah koespiadi koespiadi Mahadi Man Malcolm Munro Masita A. Jalil Masita@Masila Abd Jalil Mohamad Abdallah Mustafa Bin Man Mustafa Man Ni'matul Fahdiyah Noorshella Che Nawi Sri Wiwoho Mudjanarko, Sri Wiwoho Tutut Herawan Wan Aezwani Wan Abu Bakar Wan Aezwani Wan Abu Bakar