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Tangsi
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Computer Science & IT Education Mathematics

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Attention-Enhanced Convolutional Networks for Fine-Grained Batik Motif Classification with Statistical Feature Modeling Abdal, Nurul Mukhlisah; Tangsi
Journal of Mathematics and Applied Statistics Vol. 3 No. 1 (2025): June 2025
Publisher : Yayasan Insan Literasi Cendekia (INLIC) Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35914/mathstat.v3i1.309

Abstract

This study examines a hybrid method for classifying fine-grained Indonesian batik motifs under limited data conditions. The research focuses on two objectives: (1) assessing the contribution of attention mechanisms to the extraction of discriminative visual features, and (2) evaluating the role of Gray-Level Co-occurrence Matrix (GLCM) texture descriptors when combined with deep convolutional representations. The proposed approach employs a ResNet-50 backbone equipped with a Convolutional Block Attention Module (CBAM) and integrates second-order GLCM features through a feature-fusion framework. The dataset consists of authentic batik photographs representing 38 motif categories. Model performance is assessed using accuracy, macro-averaged metrics, Cohen’s Kappa, and ablation experiments supported by statistical tests. The model reaches a test accuracy of 75.90%, with a macro F1-score of 0.7598 and a Cohen’s Kappa value of 0.7456. Training and validation curves show stable behavior after the initial epochs. Per-class evaluation indicates that motifs with distinctive structural elements tend to be classified correctly, whereas motifs with subtle or overlapping patterns exhibit lower accuracy. The ablation study records a 4.79% accuracy increase attributed to CBAM and a 3.51% increase associated with GLCM features; both effects fall within statistically significant confidence intervals. The combination of both components yields an 8.38% improvement over the baseline model. Two-way ANOVA identifies main effects for attention and GLCM, with a small interaction term. These results provide information on how spatial attention and statistical texture features contribute to the classification of fine-grained batik motifs within the examined setting.
The Effectiveness of Mastery Learning Supported by Adaptive Quizzes on Backpropagation Material in Improving Learning Outcomes Abd Akbar Sutiawan; Tangsi; Nurul Nikma Salsabila; Nailul Hajar B; Neli Agustin; Noor Edy Wijaya Sari; Nur Fadhillah M. Aripa
Information Technology Education Journal Vol. 4, No. 4, November (2025)
Publisher : Jurusan Teknik Informatika dan Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/intec.v4i4.11193

Abstract

This study investigates the effectiveness of Mastery Learning supported by adaptive quizzes in improving students’ understanding of backpropagation, a key concept in machine learning. The problem addressed is the difficulty students face in mastering complex topics like backpropagation and the limitations of traditional lecture-based teaching methods in facilitating deep learning. A pretest-posttest experimental design was employed with 60 undergraduate students. The experimental group (n=30) used Mastery Learning and adaptive quizzes, while the control group (n=30) followed traditional lecture-based instruction. Data was collected via pretests, posttests, and a survey assessing perceived learning. Paired t-tests and independent t-tests were used for statistical analysis. The experimental group showed a significant improvement in posttest scores (mean = 85.4) compared to the control group (mean = 65.3). The effect size was large (Cohen’s d = 1.2), indicating substantial improvements in learning outcomes. Survey results showed that 94% of students in the experimental group found the adaptive quizzes helpful in understanding backpropagation and expressed a preference for continuing with this method. The study demonstrates the potential of combining Mastery Learning with adaptive quizzes in enhancing learning in machine learning courses. Limitations include the small sample size and the focus on a single topic (backpropagation). The results may not be generalized to other topics or populations. This study contributes to the literature by showing the effectiveness of adaptive learning systems in technical subjects like machine learning. Future research could explore the scalability of this method and its applicability to other machine learning concepts or fields.
Co-Authors Abd Akbar Sutiawan Abdal, Nurul Mukhlisah Dwi Wahyuni Hamka Jusbaeni Nailul Hajar B Neli Agustin Noor Edy Wijaya Sari Nur Fadhillah M. Aripa Nurul Nikma Salsabila Siti Asmaulul Izmi Sri Riski Wulandari