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All Journal Aptisi Transactions on Technopreneurship (ATT) Operations Research: International Conference Series International Journal of Global Operations Research International Journal of Quantitative Research and Modeling International Journal of Ethno-Sciences and Education Research International Journal of Applied Mathematics, Sciences, and Technology for National Defense International Journal of Mathematics, Statistics, and Computing
Ismail, Muhammad Iqbal Al-Banna
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Religion Humanities Biochemistry, Genetics & Molecular Biology Chemistry Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Engineering Environmental Science Languange, Linguistic, Communication & Media Mathematics Physics Social Sciences Transportation Other

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Exploring the Impact of Traditional Games on Children's Motor Skills Development: A Literature Review Saefullah, Rifki; Pirdaus, Dede Irman; Ismail, Muhammad Iqbal Al-Banna
International Journal of Ethno-Sciences and Education Research Vol. 4 No. 2 (2024): International Journal of Ethno-Sciences and Education Research (IJEER)
Publisher : Research Collaboration Community (Rescollacom)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46336/ijeer.v4i2.612

Abstract

This study explores the potential of traditional games in enhancing children's motor skills, focusing on eye-hand-foot coordination. Through a comprehensive literature review, various traditional games were identified, including Boy-Boyan, Fireball, Lato-lato, Blowgun, Sipak Rago, Post Box, Gatrik, Throwing Bananas, Stilts, Clogs, Spinning Top, Throw the Can, Marbles, Clap Stick, Sorolok Rifle, Bekel Ball, Kite, Chicken Feather Football, Angklek, and Pleto. Each game involves different coordination aspects, contributing to the development of children's motor skills. This study highlights the importance of preserving traditional games as cultural heritage and promoting their role in children's physical development.
Comparison of Machine Learning Models for Breast Cancer Diagnosis Classification Ibrahim, Riza; Yuningsih, Siti Hadiaty; Ismail, Muhammad Iqbal Al-Banna
International Journal of Global Operations Research Vol. 6 No. 4 (2025): International Journal of Global Operations Research (IJGOR), November 2025
Publisher : iora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47194/ijgor.v6i4.431

Abstract

Breast cancer remains one of the most pressing global public health challenges, with approximately 2.3 million women diagnosed worldwide in 2022 and around 670,000 deaths attributed to the disease. Despite the widespread application of machine learning algorithms for breast cancer classification, findings across studies remain highly varied, and there is still no consistent conclusion regarding which algorithm is most superior for breast cancer diagnosis. This study aims to analyze and compare the performance of four machine learning algorithms Logistic Regression, Support Vector Machine (SVM), Random Forest, and K-Nearest Neighbors (KNN) in predicting breast cancer. The dataset used was the Breast Cancer Wisconsin (Diagnostic) Data Set obtained from Kaggle, containing morphological characteristics of tumor cells. Data preprocessing involved cleaning, label encoding, feature normalization using StandardScaler, and an 80:20 train-test split. Model performance was evaluated using confusion matrix, precision, recall, F1-score, accuracy, and ROC-AUC. The results showed that all four models achieved excellent performance with overall accuracy ranging from 95.61% to 97.37%. SVM emerged as the most accurate model (97.37%) with perfect recall (1.00) for the Benign class. Logistic Regression demonstrated the highest ROC-AUC value (0.9960), indicating excellent discriminative ability. Random Forest and KNN showed slightly lower performance, particularly in detecting Malignant cases with recall of 0.90. These findings confirm that machine learning can serve as an effective tool to support breast cancer diagnosis, with algorithm selection depending on data characteristics and clinical priorities.
A Comparative Policy Analysis of Education Systems and TVET Teacher Competencies in Indonesia and Malaysia Thomas, Fendy; Noviandani, Pradika; Munawwiroh, Anis; Ismail, Muhammad Iqbal Al-Banna
International Journal of Ethno-Sciences and Education Research Vol. 6 No. 1 (2026): International Journal of Ethno-Sciences and Education Research (IJEER)
Publisher : Research Collaboration Community (Rescollacom)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46336/ijeer.v6i1.1182

Abstract

Educational systems and the competence of Technical and Vocational Education and Training (TVET) teachers play a crucial role in shaping the quality of human resources and national competitiveness. This study aims to comparatively analyze the education systems and TVET teacher competencies in Indonesia and Malaysia, two neighboring countries with similar cultural backgrounds but different educational governance and policy orientations. The study adopts a qualitative descriptive approach based on document analysis of laws, regulations, policy reports, and relevant literature related to national education systems and TVET teacher development in both countries. The findings indicate that Indonesia and Malaysia differ significantly in terms of educational structure, governance, funding mechanisms, and curriculum implementation, particularly at the secondary and vocational education levels. Malaysia demonstrates a more centralized and systematically evaluated education policy framework, supported by higher public investment in education. In contrast, Indonesia faces challenges related to educational quality, equity, and consistency in policy implementation. At the TVET level, both countries encounter similar issues, especially regarding teacher quality, uneven distribution of vocational teachers, and limited industry experience among educators. In Indonesia, the Professional Teacher Education Program (PPG) is expected to address shortages and improve the quality of vocational teachers. Meanwhile, Malaysia emphasizes the Modern Apprenticeship or dual system to enhance teachers’ industry exposure and practical competence. Despite differences in terminology, both countries recognize four essential domains of TVET teacher competence: pedagogical, professional, personal, and social or communication competence. The study concludes that strengthening industry collaboration and continuous professional development is essential to improving TVET teacher competence and aligning vocational education outcomes with labor market needs.
Co-Authors anis munawwiroh Atmadi Dihna, Elza Rahma Dwiputra, Muhammad Bintang Eighista Etty Puji Lestari Hernawati, Depie Ibrahim, Riza Khafsah Joebaedi Lase, Sepandil Laras Muhammad Afif Negara, I Putu Aditya Brama Putra Cakra Noviandani, Pradika Pirdaus, Dede Irman Primayesa, Elvina Saefullah, Rifki Saputra, Renda Sandi Sidiq, Fahmi Sucihatiningsih Dian Wisika Prajanti Supena, Yusup Syalaisa, Najlaa Tarigan, Nieldy R Thomas, Fendy Yuningsih, Siti Hadiaty