Article Per Year (5 Year)
p-Index From 2021 - 2026
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All Journal Journal of Hypermedia & Technology-Enhanced Learning
Dony Novaliendry
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): 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.
Web-Based Inventory Management System for Educational Training: Integrating EOQ and ARIMA for Data-Driven Learning Alkindi Syamsi; Dedy Irfan; Dony Novaliendry; Randi Proska Sandra
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.218

Abstract

Manual inventory recording and heuristic ordering practices remain common among Micro, Small, and Medium Enterprises (MSMEs), often leading to inaccurate demand estimation, excessive holding costs, and stockouts. This study develops and evaluates a web-based inventory information system that integrates Autoregressive Integrated Moving Average (ARIMA) forecasting with the Economic Order Quantity (EOQ) model to improve decision accuracy and cost efficiency. The system uses CodeIgniter 3 and MySQL and incorporates a Python-based time-series forecasting engine. Historical sales data were modeled using ARIMA, and the optimal specification was selected based on Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC). The ARIMA(1,1,1) model achieved a Mean Absolute Percentage Error (MAPE) of 8.47%, indicating high forecasting accuracy for operational planning. The forecasted annual demand was integrated into the EOQ framework to determine the optimal order quantity, Reorder Point (ROP), and probabilistic Safety Stock. A one-year cost simulation demonstrated that the EOQ-based policy reduced total inventory costs by 22.73% compared with the existing approach. Functional validation through Black-Box testing confirmed full compliance with specified requirements. These findings demonstrate that integrating predictive analytics with classical inventory optimization enhances operational efficiency and reduces total inventory cost. The system provides a practical, data-driven inventory management framework for MSMEs undergoing digital transformation.
A Mobile Vocabulary Learning App for Engineering Contexts: Gamification, Self-Directed Learning, and User Progress Tracking Anita Nursi; Resmi Darni; Dony Novaliendry
Journal of Hypermedia & Technology-Enhanced Learning Vol. 3 No. 3 (2025): Journal of Hypermedia & Technology-Enhanced Learning—Next Horizon
Publisher : Sagamedia Teknologi Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58536/j-hytel.193

Abstract

Engineering students frequently face challenges in mastering technical English vocabulary due to its context-dependent and specialized nature. To address this issue, this study developed a mobile application that facilitates self-directed learning of domain-specific vocabulary through gamified activities. The application was designed using the Flutter framework and integrated with Firebase for real-time data management and user tracking. It features two interactive games—Word Guess and Word Match—as well as text-to-speech functionality, a limited hint system, and visual progress tracking through mastery counts and progress bars. Vocabulary items are organized into three thematic categories: General Terms, Tools, and Instructions, each structured through JSON-based data management for scalability and ease of maintenance. Empirical evaluations involving expert review yielded a high validity score (M = 4.6/5), confirming the application’s pedagogical soundness and technical stability. The integration of gamification and progress visualization significantly enhanced learner motivation, autonomy, and engagement. This study contributes a replicable model for combining Flutter–Firebase development with instructional design principles to advance technology-enhanced language learning in engineering education.
Co-Authors Alkindi Syamsi Anita Nursi Dedy Irfan Khairi Budayawan Muhamad Fathur Rahman Resmi Darni Sandra, Randi Proska