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All Journal Journal of Computing Theories and Applications Maddana: Jurnal Pengabdian Kepada Masyarakat
Ramadhan, As’ary
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Humanities Computer Science & IT Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Social Sciences Other

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Pengenalan metode permainan motorik kasar anak pada guru TK di Banjarmasin Amalia, Bonita; Siahaan, Jurdan Martin; Ramadhan, As’ary; Pratiwi, Endang; Fauzi, Ahmat
MADDANA Jurnal Pengabdian Kepada Masyarakat Vol 3 No 2 (2023): MADDANA: Jurnal Pengabdian Kepada Masyarakat
Publisher : Fakultas Keguruan dan Ilmu Pendidikan Universitas Islam 45 Bekasi

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Abstract

Tujuan sosialisasi pengabdian masyarakat ini untuk memudahkan pihak sekolah dalam memberikan pembelajaran yang tepat dalam proses pembentukan motorik anak, sehingga dengan terlaksananya kegiatan ini guru mampu memberikan permainan di sekolah yang sesuai dan tepat. Kegiatan pengabdian ini dilakukan dengan cara tutorial dan diskusi.untuk teori, serta praktik/latihan dalam kelompok dan diskusi untuk pengalaman praktik. Metode pelaksanaa melalui ceramah dan diskusi tentang teori-teori program pemulihan olahraga, serta praktik pemulihan olahraga, sehingga peserta pelatihan memiliki landasan pemahaman pengetahuan teori dan praktek tentang program pemulihan olahraga. Pengabdian kepada masyarakat yang diberikan oleh Bonita Amalia, M.Pd. dilaksanakan pada tanggal 14 Mei 2023 dengan hasil kegiatan yaitu materi yang diberikan sangat membantu peserta dalam proses pembelajaran motorik kasar antara lain ialah lokomotor dan manipulatif. Kegiatan pengabdian masyarakat berjalan dengan lancar dimana tim pengabdian memberikan materi berupa metode permainan motorik kasar dan mencontohkan atau mempraktekkan secara bersama guru-guru TK di Banjarmasin. Pada program pengabdian masyarakat kali ini sangat diperlukan pengembangan dalam hal yang lebih luas dan diharapkan agar para guru-guru TK di Banjarmasin dapat lebih kritis lagi dalam berpikir.
Enhancing Software Defect Prediction through Hybrid Multi-Filter Feature Selection and Imbalance Handling Maulana, Muhammad Khalid; Saputro, Setyo Wahyu; Faisal, Mohammad Reza; Nugroho, Radityo Adi; Ramadhan, As’ary
Journal of Computing Theories and Applications Vol. 3 No. 4 (2026): JCTA 3(4) 2026
Publisher : Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/jcta.15943

Abstract

Software Defect Prediction (SDP) aims to identify defective modules early in the software development lifecycle to improve software quality and reduce maintenance costs. However, SDP datasets commonly suffer from high dimensionality, feature redundancy, and class imbalance, which can degrade model performance and stability. This study proposes a hybrid feature selection framework to address these challenges and enhance prediction performance. The proposed approach integrates Combined Correlation and Mutual Information (CONMI), which combines the Pearson Correlation Coefficient (PCC) and Mutual Information (MI) to capture both linear and nonlinear feature relevance. The selected features are further refined through Top-K selection, correlation-based filtering to reduce multicollinearity, and Backward Elimination (BE) to obtain an optimal feature subset. To address class imbalance, SMOTE-Tomek is applied by combining over-sampling and data cleaning techniques. Experiments are conducted on twelve NASA MDP datasets using Logistic Regression (LR) and Naïve Bayes (NB) classifiers. The results show that the proposed framework consistently achieves the best performance, with Logistic Regression combined with SMOTE-Tomek obtaining the highest average AUC of 0.7923 ± 0.0714, while NB achieves 0.7554 ± 0.0580. Statistical analysis using a paired t-test indicates that the proposed method significantly outperforms MI+SMOTE-Tomek and BE+SMOTE-Tomek for Logistic Regression, whereas no significant differences are observed for NB. In addition to improving overall classification performance (AUC), the proposed approach also enhances minority class detection, as reflected in improved Recall and F1-score. Overall, the proposed hybrid framework provides an effective and reliable solution for software defect prediction, particularly for high-dimensional and imbalanced datasets.
Co-Authors Amalia, Bonita Ida Ayu Putu Sri Widnyani Maulana, Muhammad Khalid Muhammad Reza Faisal, Muhammad Reza Pratiwi, Endang Radityo Adi Nugroho Saputro, Setyo Wahyu Siahaan, Jurdan Martin