Md Baharuddin bin Abdul Rahman
Universiti Sains Malaysia

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Development of Algebra Test Problems Based on Minimum Competency Assessment (MCA) for Junior High School Suryawati Suryawati; Khairul Umam; Nafisyah Laila; Ayu Mastura; Md Baharuddin bin Abdul Rahman
Didaktik Matematika Vol 10, No 2 (2023): October 2023
Publisher : Universitas Syiah Kuala

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24815/jdm.v10i2.34257

Abstract

The Minimum Competency Assessment (MCA) is one of the components of the National Assessment (NA) in Indonesia, which aims to measure students' cognitive abilities, including numeracy skills. Students with low numeracy skills can be affected by not being used to working on MCA-type problems. This study aims to develop MCA items on algebra that meet valid, practical, and effective criteria using the Tessmer model, including preliminary, self-evaluation, expert reviews, one-to-one, small group, and field test stages. The subjects in this study were 36 Year 7 students of one junior high school in Banda Aceh, Indonesia. The instruments used were validation sheets, practicality sheets, and student response problemnaires. The findings indicated that 15 MCA problems on algebra have met the validity, practicality, and effectiveness criteria. It shows teachers can use MCA items to train students' numeracy skills. Future researchers are expected to develop multiple problems within a single stimulus with more varied content and context; they will need to consider the novelty of the stimulus in real conditions in the field.
A Comparative Analysis of the Effectiveness and Efficiency of Manual and Automated Testing in Software Quality Assurance Amara Seviany Agustin; Nuur Wachid Abdul Majid; Md Baharuddin Bin Abdul Rahman
Recursive Journal of Informatics Vol. 4 No. 2 (2026): September 2026
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/rji.v4i2.64137

Abstract

Purpose: This study aims to analyze the effectiveness, bottlenecks, and reliability of automated testing and manual testing, as well as to compare the two approaches in detecting bugs and supporting software product quality. Accelerating release cycles can be counterproductive if functional stability is ignored; thus, evaluating these two quality assurance methods is crucial to finding an optimal balance in software development. Methods/Study design/approach: The study employed a quantitative approach through a survey of 105 respondents, comprising QA Engineers, software developers, and academics, using a five-level Likert scale questionnaire. The collected data were rigorously analyzed using descriptive statistics, the Wilcoxon Signed-Rank Test, and the Friedman Test to objectively evaluate the performance, reliability, and operational bottlenecks of both testing methods. Result/Findings: The results demonstrate that automated testing is highly effective for regression testing and repetitive execution, while manual testing remains significantly effective for UI/UX evaluation and complex dynamic cases. Both methods face distinct challenges. Automated testing is bottlenecked by script maintenance efforts and flaky tests, whereas manual testing is hindered by lengthy execution times and human error. Automated testing was found to be significantly more accurate in detecting recurring functional bugs. Furthermore, an integrated combination of both automated and manual testing was proven to substantially reduce escaped defects. Novelty/Originality/Value: This research provides empirical evidence confirming that despite the strong push for automation in modern Agile and DevOps environments, neither method can completely replace the other. Both testing approaches are most effective when utilized complementarily according to their intrinsic characteristics. This synergy is essential to prevent bottlenecks in the software release cycle and ensure superior product quality.