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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) ELINVO (Electronics, Informatics, and Vocational Education) Djtechno: Jurnal Teknologi Informasi
Herjuna Artanto
Universitas Negeri Yogyakarta
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering

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Emotions and gesture recognition using affective computing assessment with deep learning Herjuna Artanto; Fatchul Arifin
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 12, No 3: September 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v12.i3.pp1419-1427

Abstract

Emotions have an important role in education. Affective development, attitudes, and emotions in learning are measured using affective assessment. This method is the right way to determine the student’s affective development. However, the process did not run optimally because the teacher found it difficult to collect student’s affective data. This paper describes the development of a system that can assist teachers in carrying out affective assessment. The system was developed using a v-model that aligns the verification phase with the validation. The use of the system is carried out during learning activities. The emotion detection system detects through body gestures using PoseNet to generate emotional data for each student. The detection results are then processed and displayed on an information system in the form of a website for affective assessment. The accuracy of emotion detection got validation values of 84.4% and 80.95% after being tested at school. In addition, the acceptance test with the usability aspect of the system by the teacher got a score of 77.56% and a score of 79.85% by the students. Based on several tests carried out, this developed system can assist the process of implementing affective assessment. 
Performance Analysis of EMG Signal Classification Methods for Hand Gesture Recognition in Stroke Rehabilitation Winursito, Anggun; Arifin, Fatchul; Muslikhin, Muslikhin; Artanto, Herjuna; Caryn, Femilia Hardina
Elinvo (Electronics, Informatics, and Vocational Education) Vol. 8 No. 2 (2023): November 2023
Publisher : Department of Electronic and Informatic Engineering Education, Faculty of Engineering, UNY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21831/elinvo.v8i2.76811

Abstract

This study evaluates the performance of different classification methods in classifying healthy individuals and stroke patients. The hand gesture variations of the subjects were also analyzed based on electromyography (EMG) signals. Several classification methods were tested in this analysis to find out which method had the most suitable performance. The results showed that Decision Tree and Naive Bayes classifiers achieved the highest performance in classifying EMG signals from healthy individuals and stroke patients, with both methods showing high accuracy, precision, recall, and F1 score. Specifically, Decision Tree excelled in overall accuracy and recall, while Naive Bayes showed superior precision. For hand gesture recognition, SVM, KNN, and Random Forest classifiers showed similarly high performance, achieving accuracy, precision, recall, and F1 score above 82%. Naive Bayes also performed well, especially in precision, while Decision Tree performed poorly compared to other methods. This insight can form the basis for the development of more effective and personalized rehabilitation systems for stroke patients, by utilizing reliable and accurate EMG signal classification
SEMANTIC WEB IMPLEMENTATION FOR ENHANCING BUDGET TRANSPARENCY IN YOGYAKARTA CITY GOVERNMENT: AN ONTOLOGY AND RDF-BASED FRAMEWORK Rahman, Dzul Fadli; Setiyawan, Ramadhana; Artanto, Herjuna
Djtechno: Jurnal Teknologi Informasi Vol 6, No 3 (2025): Desember
Publisher : Universitas Dharmawangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46576/djtechno.v6i3.7448

Abstract

Penerapan teknologi Semantic Web dalam pengelolaan data pemerintah menjadi strategi penting untuk meningkatkan transparansi dan keterbacaan mesin terhadap data terbuka. Penelitian ini bertujuan untuk mentransformasikan data Anggaran Murni Kota Yogyakarta tahun 2022 dari format tabular ke dalam representasi semantik menggunakan Resource Description Framework (RDF) dan Web Ontology Language (OWL). Ontologi dikembangkan dengan mendefinisikan lima kelas utama, yaitu Anggaran, Daerah, KategoriAnggaran, KelompokAnggaran, dan JenisAnggaran, serta dilengkapi dengan object property dan data property yang menggambarkan relasi dan atribut antarentitas. Representasi RDF kemudian diuji menggunakan beberapa query SPARQL untuk mengekstraksi informasi, seperti total anggaran, surplus atau defisit, dan identifikasi entitas tanpa alokasi. Visualisasi struktur ontologi dilakukan menggunakan plugin OWLViz untuk memastikan konsistensi logis antar kelas dan properti. Hasil penelitian menunjukkan bahwa model ontologi yang dikembangkan mampu merepresentasikan struktur anggaran secara terstruktur, konsisten, dan mendukung kueri semantik. Penelitian ini memberikan kontribusi terhadap upaya keterbukaan data pemerintah daerah dan dapat dikembangkan lebih lanjut untuk integrasi lintas wilayah serta visualisasi interaktif berbasis Web Semantik.
Co-Authors Caryn, Femilia Hardina Fatchul Arifin Muslikhin Rahman, Dzul Fadli Setiyawan, Ramadhana Winursito, Anggun