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All Journal Journal of Hypermedia & Technology-Enhanced Learning
Khairi Budayawan
Department of Electronics Engineering, Faculty of Engineering, Universitas Negeri Padang, Padang
Computer Science & IT Education Electrical & Electronics Engineering Engineering Social Sciences Other

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An Artificial Intelligence-Based Mobile Application for Early Detection of Dyslexia Using Recurrent Neural Network Muhamad Fathur Rahman; Resmi Darni; Dony Novaliendry; Khairi Budayawan
Journal of Hypermedia & Technology-Enhanced Learning Vol. 4 No. 1 (2026): Future Education
Publisher : Sagamedia Teknologi Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58536/j-hytel.217

Abstract

Dyslexia is a neurodevelopmental learning disorder that significantly affects children’s reading and writing skills despite normal intelligence, and delayed identification may lead to long-term academic and psychosocial consequences. Existing dyslexia screening methods rely heavily on expert-driven assessments that are time-consuming, subjective, and difficult to scale in non-clinical settings. Although recent studies have explored artificial intelligence (AI) approaches for dyslexia detection, many remain limited to single-modality data, offline analysis, or non-mobile implementations, restricting their practical applicability for early screening. This study aimed to develop an AI-based mobile application for early dyslexia detection by leveraging sequential text and speech data through a Recurrent Neural Network (RNN) architecture, specifically the Gated Recurrent Unit (GRU). A Research and Development (R&D) methodology was employed, encompassing requirements analysis, system design, GRU model training, mobile application development with Flutter, and system integration with a RESTful backend and a MySQL database. The GRU model was trained on preprocessed reading text and voice recordings to capture temporal patterns associated with dyslexia-related reading behaviors. Experimental results indicate that the proposed model achieved reliable classification performance in identifying dyslexia-related patterns, while the mobile application successfully delivered real-time screening results and maintained longitudinal assessment records. The findings demonstrate that integrating lightweight sequential deep learning models into mobile platforms offers a scalable and accessible solution for early dyslexia screening, supporting independent use by parents and educators outside clinical environments.
SiKS: An Integrated Web-Based Student Counseling Information System with Automated WhatsApp Notifications Welly Aryatama; Syukhri Samala; Khairi Budayawan; Delvi Asmara; Dean Collin Langeveldt
Journal of Hypermedia & Technology-Enhanced Learning Vol. 4 No. 3 (2026): AI Frontier
Publisher : Sagamedia Teknologi Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58536/j-hytel.264

Abstract

Fragmented school counseling records can obscure case histories, disrupt follow-up, and weaken communication with families. This study developed and formatively evaluated SiKS, an integrated web-based student counseling information system that connects counseling activities through role-based access and automated WhatsApp notifications. Requirements were elicited through interviews with the Vice-Principal for Student Affairs and a School Counselor at an Indonesian vocational high school, supplemented by analysis of the existing workflow. Developed using the Waterfall model, Laravel, and MySQL, the prototype links appointment requests, scheduling, counseling records, follow-up actions, parental summons, reporting, and notification logs across six user roles. Evaluation included 50 black-box scenarios derived from the requirements and user acceptance testing with 20 role-representative respondents after a guided walkthrough. Three validators reviewed the 15-item instrument, assigning 211 out of 225 possible points (93.78%). All 50 executed functional scenarios produced the expected outputs. User acceptance scores totaled 1,336 out of 1,500 points (89.07%), with item scores ranging from 82% to 96%. Interface attractiveness, suitability for school use, menu accessibility, and perceived confidentiality under role-based access received the strongest ratings; report clarity, printing and downloading, and notification services received comparatively lower ratings. The study presents a context-specific design that links case continuity, differentiated access, and event-triggered communication within a single counseling workflow. The findings support functional conformance under the tested conditions and favorable initial user perceptions. Developer-led testing, the single-school setting, and guided exposure limit inferences about independent use, sustained adoption, privacy protection, and counseling outcomes.
Co-Authors Dean Collin Langeveldt Delvi Asmara Dony Novaliendry Muhamad Fathur Rahman Resmi Darni Syukhri Samala Welly Aryatama