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All Journal Buletin Ilmiah Sarjana Teknik Elektro Jurnal Pendidikan Teknik Sipil (JPTS)
Wahyu Nur Hidayat
National Taiwan University of Science and Technology
Civil Engineering, Building, Construction & Architecture Education Electrical & Electronics Engineering Languange, Linguistic, Communication & Media Social Sciences

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Adaptive Feature Selection using Fisher-Based Supervised Hill Climbing for Dysgraphia Handwriting Classification Kartika Candra Kirana; Anik Nur Handayani; Nur Eva; Aji Prasetya Wibawa; Wahyu Nur Hidayat; Kohei Arai
Buletin Ilmiah Sarjana Teknik Elektro Vol. 8 No. 2 (2026): April
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/biste.v8i2.14983

Abstract

Dysgraphia features selection remains a challenge. Fisher’s criterion excels at highlighting the discriminative features of dysgraphia but lacks guidance for choosing the optimal number of features. Whereas Hill Climbing shows robust feature selection but often gets trapped in local optima. This study aims to avoid the Hill Climbing trap in local optima when selecting the best dysgraphia feature. Thus, the Fisher-Based Supervised Hill Climbing (FSHC) method is introduced. The contribution of this study is an optimized machine-learning-guided hill-climbing method that uses a classifier on a validation set as the objective function. A plateau mechanism also guided Hill Climbing exploration, not by a single Fisher point but by the neighboring subsets. The dataset used contains the graphomotor slant line task from 119 children aged 8-15 years (47.5% diagnosed with dysgraphia), with 10000 to 50000 data points per user. It is organized into kinematic, spatial, dynamic, and temporal features, yielding 117 sub-features. A stratified 5-fold cross-validation is set for training and testing, reaching 21 features. Comparative test—Linear SVM, SVM RBF, Sigmoid SVM, Polynomial SVM, Random Forest, AdaBoost, KNN, Decision Tree, Gradient Boosting, Gaussian Naive Bayes, and Gaussian Classifier—showed that linear SVM achieves the best performance with a weighted average precision, recall, and F1 score of 0.93. Linear SVM also outperformed the three approaches: no feature selection, the traditional Fisher, and machine-learning-based feature selection (weighted KNN and SVM). It can be concluded that the proposed method is more robust than the state of the art by highlighting key points for avoiding overfitting.
The Use of Stone Foundation Structure and Footplate Models as Learning by Doing Media to Improve Structural Understanding and Learning Motivation; Vocational High School (SMK) Department of Building Modeling and Information Design Dwi Palupi; Muhammad Aris Ichwanto; Wahyu Nur Hidayat
Jurnal Pendidikan Teknik Sipil Vol. 8 No. 1 (2026): June
Publisher : Universitas Negeri Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21831/jpts.v8i1.93642

Abstract

Background: This study is motivated by limited understanding of building structure concepts among vocational high school students, resulting from limited instructional media and the continued dominance of conventional teaching methods. This study aims to examine the effectiveness of the Learning by Doing approach, supported by physical mock-ups of rubble stone foundation and footplate structures, in improving conceptual understanding and learning motivation. Methods: This study employed a one-group pretest-posttest design involving 8 eleventh-grade students from the DPIB program at Vocational High School (SMK) Sunan Drajat Lamongan, selected through purposive sampling. Data were collected through essay tests, observation, and questionnaires, and were then analyzed both quantitatively (paired t-test, Cohen's d) and qualitatively. Results: The results showed an increase in the mean score from 26.63 to 35.50 (on a 0 – 60 scale), with an effect size (Cohen's d) of 0.97, classified as large. Although the statistical increase was not significant (p = 0.073), the large effect size indicates a meaningful practical effect despite the small sample size. Qualitative analysis revealed a clear improvement in conceptual understanding, such as students' ability to explain the function and mechanism of basic structural components more systematically. Affectively, most students (87.5%) reported that the mock-ups were very helpful in visualizing the concepts, and all students reported increased learning motivation. Conclusion: This model-based learning approach has the potential to serve as a cognitive bridge linking theory and practice and to contribute to increased student motivation. This medium is a promising alternative learning strategy, especially for vocational schools with limited facilities.
Co-Authors Aji Prasetya Wibawa Anik Nur Handayani Dwi Palupi Kartika Candra Kirana Kohei Arai Muhammad Aris Ichwanto Nur Eva