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All Journal Tekno : Jurnal Teknologi Elektro dan Kejuruan Jurnal Ilmu Komputer dan Informasi Proceeding of the Electrical Engineering Computer Science and Informatics Edu Komputika Journal Knowledge Engineering and Data Science JTERA (Jurnal Teknologi Rekayasa) Pendas : Jurnah Ilmiah Pendidikan Dasar JAVA Journal of Electrical and Electronics Engineering Buletin Ilmiah Sarjana Teknik Elektro Generation Journal Belantika Pendidikan Jurnal Graha Pengabdian Jurnal Pengabdian kepada Masyarakat Journal of Disruptive Learning Innovation (JODLI) JURNAL PENGABDIAN PENDIDIKAN MASYARAKAT (JPPM) Wawasan : Jurnal Ilmu Manajemen, Ekonomi dan Kewirausahaan PAPUMA: Journal of Comunnity Services Edu Komputika Journal
Kartika Candra Kirana
Universitas Negeri Malang
Religion Agriculture, Biological Sciences & Forestry Humanities Biochemistry, Genetics & Molecular Biology Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Mathematics Mechanical Engineering Public Health Social Sciences Other

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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.
Epistemological and Axiological Analysis of ResNet18-Based Dysgraphia Classification Kartika Candra Kirana; Anik Nur Handayani; Syaad Patmanthara; Nur Eva
Generation Journal Vol 10 No 1 (2026): Generation Journal
Publisher : Universitas Nusantara PGRI Kediri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29407/gj.v10i1.27419

Abstract

Based on an ontological perspective, there is a gap in feature representation and in binary dysgraphia classification using ResNet18, an area that has not been explored simultaneously. Thus, our contribution is an analysis of research on dysgraphia classification using ResNet18 that employs epistemological and axiological approaches. ResNet18 was chosen as the backbone of the proposed framework because it has shortcut connections that can degrade residues into useless features. As a representation of new knowledge, ResNet18 was pre-trained on ImageNet. Classification was tested on challenging word assignments, comprising 145 dysgraphia images and 188 non-dysgraphia images. Epoch trials were conducted to find the best architecture. The results showed that ResNet18 at epoch 10 achieved the best performance in binary classification, with a recall of up to 93.55%. This indicates that ResNet18 is sensitive to recognizing dysgraphia classes. Challenges outlined in this study serve as a foundation for further research.
Ant Colony Optimization for Resistor Color Code Detection Wibawanto, Slamet; Kirana, Kartika Candra; Ramadhan, Hani
Knowledge Engineering and Data Science
Publisher : citeus

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

In the early stages of learning resistors, introducing color-based values is needed. Moreover, some combinations require a resistor trip analysis to identify. Unfortunately, a resistor body color is considered a local solution, which often confuses resistor coloration. Ant Colony Optimization (ACO) is a heuristic algorithm that can recognize problems with traveling a group of ants. ACO is proposed to select commercial matrix values to be computed without preventing local solutions. In this study, each explores the matrix based on pheromones and heuristic information to generate local solutions. Global solutions are selected based on their high degree of similarity with other local solutions. The first stage of testing focuses on exploring variations of parameter values. Applying the best parameters resulted in 85% accuracy and 43 seconds for 20 resistor images. This method is expected to prevent local solutions without wasteful computation of the matrix.
Co-Authors A, Devani Afiyatul Abdulrahman, Salah Abdullah Khalil Achmad Hamdan Adam Achmad Rachmawan Adinugroho, M Ludy Aji Prasetya Wibawa Akbar, Asna Isyarotul Andrian Rachmat Anik Nur Handayani Ashar, Muhammad Axel Gandy Arthayuda Azhar Ahmad Smaragdina Baihaqi, Ahmad Fist Cal Bayyinah, Nur Begananda, Hilham Bagus Budi Rahmadya Budi, Lalu Agung Purnama Cahya Bintang Wira Winata Cahyani, Amanda Dea Cahyani, Gita Ayu Cahyono, Gigih Prasetyo Choirul Anam Devita, Riri Nada Dimas Prasetyo Buseri Eko Tristyo Purwanto Ellvina Pramitadewi Wahyunigtyas F Ti Ayyu Sayyidul Laily Fadhlullah, Aufar Faiq Fajar Ananda Saputra Febri Liantoni Filsafalasafi, Alfi Firmansyah, Muhammad Ferdian Gigih Prasetyo Cahyono Gushardana, Raffi Taufiq Handayani, Dwining Handayani, Nia Okta Hani Ramadhan Hani Ramadhan, Hani Haq, Yaritza Hary Suswanto Hermanto, Yon Ade Lose Heru Wahyu Herwanto Hibatullah, Rizaldi Naufal Imro’aturrozaniyah Imro’aturrozaniyah Indri Astuti Ivan reynaldi Putra Kohei Arai Krisma Anuarin Hidayat Lalu Agung Purnama Budi Latif, Rafika Mas’udah, Ajeng Ramadhani Lailul Michell Brella Tamarizta Mochammad Bagus Priyantono Mohamad Firzon Ainur Mustika, M. Choirul Nadifah Adya Ilham Nandha Mustika Sari Nasharuddin Mas Nidhom, Ahmad Mursyidun Ningrum, Gres Dyah Kusuma Nur Eva Nur Hidayah Nur Hidayat, Wahyu Nurjihan Najma Zahera Perdana, Sigit Prasetya, Januari Adi Priyantono, Mochammad Bagus Purwanto, Eko Tristyo Rachmawan, Adam Achmad Raffi Taufiq Gushardana Raffi Taufiq Gushardana S, Arsy Aisyah Malika R. Saputra, M. Adi Sari, Nandha Mustika Sari, Rahajeng Kartika Shofiea, Maulida Siregar, Galih Carlos Putra Siregar, Sherly Allsa Siswahyudianto Slamet Wibawanto Sujito Sujito Sujito Sujito Syaad Patmanthara Syahputra, M. Firman Aji Thoriq Bachtiar Yusuf Ekananda Thoriq Bachtiar Yusuf Ekananda Tri Hadiah Muliawati W, Putri Nurdiana Wahyu Nur Hidayat Wahyu, Kartika Wulandari, Rizka Safitri Yusuf Ekananda, Thoriq Bachtiar