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All Journal Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI)
Yusnita Putri
School Of Electrical Engineering, Telkom University
Computer Science & IT Electrical & Electronics Engineering

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Handwritten Digits Detection Using Convolutional Neural Network Effendi , Doni Oktavian Ibnu; Saidah, Sofia; Putri , Yusnita
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 11 No. 2 (2025): June
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v11i2.30238

Abstract

Numbers are a collection of many lines and curves and play a vital role in everyday life. Each person has unique characteristics in handwriting, making handwritten digit detection a challenging task. This paper presents an approach for detecting handwritten digits using deep learning algorithms, particularly the Convolutional Neural Network (CNN)-based YOLOv8 family models. The main objective is to compare various YOLOv8 variants (YOLOv8n, YOLOv8s, YOLOv8m, YOLOv8l, and YOLOv8x) and determine the most optimal one in detecting handwritten digits.  Experimental results show that the YOLOv8x variant achieves the highest performance, with a mean Average Precision (mAP) of 96.9%, a recall of 100%, a precision of 99.8%, and an F1-score of 99.9%. The research contributions are achieving high accuracy in handwritten digit detection using the YOLOv8x model and utilizing a custom primary dataset of 3,000 handwritten digits for training and evaluation, which adds novelty and real-world relevance to the study.
Designing UI/UX on Adaptive Skills Learning Application for Autistic Children Using Design Thinking Method and Applied Behavior Analysis Theory Putri, Yusnita; Saidah, Sofia
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 10 No. 4 (2024): December
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v10i4.28560

Abstract

This paper introduces he design of SemaiSelaras, an adaptive learning application tailored for children with Autism Spectrum Disorder (ASD), utilizing the Applied Behavior Analysis (ABA) theory and developed through the Design Thinking (DT) methodology. The application aims to address challenges faced by children with ASD in acquiring essential adaptive living skills. While prior studies have explored applications employing the DT methodology, this research uniquely focuses on integrating ABA theory to better meet the specific needs of users. The user-centered and iterative nature of DT ensured the application was designed to effectively address these requirements. The ABA approach, which breaks learning materials into manageable steps, supports children with ASD in gradually mastering life skills. SemaiSelaras integrates advanced technologies such as Optical Character Recognition (OCR), digital storyboard, audio discrimination learning, and video-based learning. The research contribution emphasizes the role of ICT in supporting accessibility and inclusion, helping children with ASD develop essential life skills. Usability testing was conducted using the System Usability Scale (SUS) and the SemaiSelaras prototype achieved an average score of 86.5, reflecting an excellent rating and demonstrating a high level of acceptance and usability for the application.
Co-Authors Effendi , Doni Oktavian Ibnu SOFIA SAIDAH