Claim Missing Document
Check
Articles

Found 2 Documents
Search

The Relationship Between Sports Hall Management Quality and Community Sports Participation Rahman, Muhamad Fathur; Setya Rahayu
Journal of Educational Management Research Vol. 4 No. 1 (2025)
Publisher : Al-Qalam Institue

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61987/jemr.v4i1.864

Abstract

This study aims to examine the relationship between the management quality of the Sports Hall (GOR) and the level of community participation in sports. A mixed-methods approach was employed with an explanatory sequential design. Quantitative data were collected through questionnaires, while qualitative data were gathered through semi-structured interviews. The findings revealed that the management quality was generally good, particularly in administrative and public service aspects. However, shortcomings were noted in facility maintenance and the disposal of unfit equipment. Community participation was relatively low, with a participation index of 0.47 for GOR-based activities and 0.60 for outdoor sports. The Spearman correlation test indicated a positive and significant relationship between management quality and sports participation (r = 0.295; p = 0.003). These results suggest that enhancing GOR management quality could foster greater community engagement in physical activity. The implications for education are significant, as improved sports facility management can contribute to creating more inclusive and accessible environments for students, promoting physical education and active lifestyles.
An Artificial Intelligence-Based Mobile Application for Early Detection of Dyslexia Using Recurrent Neural Network Rahman, Muhamad Fathur; Darni, Resmi; Novaliendry, Dony; Budayawan, Khari
Journal of Hypermedia & Technology-Enhanced Learning Vol. 4 No. 1 (2026): Journal of Hypermedia & Technology-Enhanced Learning—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.