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All Journal Voteteknika (Vocational Teknik Elektronika dan Informatika) JTEV (Jurnal Teknik Elektro dan Vokasional JURNAL PENGABDIAN KEPADA MASYARAKAT INFORMAL: Informatics Journal Jurnal Pengabdian UntukMu NegeRI Al Ishlah Jurnal Pendidikan Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING JURNAL PENDIDIKAN TAMBUSAI Journal on Education JTP - Jurnal Teknologi Pendidikan Jurnal Teknologi Informasi dan Pendidikan EDUTEC : Journal of Education And Technology JPKMI (Jurnal Pengabdian Kepada Masyarakat Indonesia) Jurnal Pengabdian kepada Masyarakat Nusantara Jurnal Vokasi Informatika (JAVIT) Journal for Lesson and Learning Studies Jurnal Media Pengabdian Kepada Masyarakat (JM-PKM) Rangkiang Mathematics Journal Jurnal Sains dan Informatika : Research of Science and Informatic Jurnal Pengabdian Pada Masyarakat JNPM (Jurnal Nasional Pendidikan Matematika) JPNM : Jurnal Pustaka Nusantara Multidisiplin Jurnal Teknik Komputer dan Informatika (JTeKI) Journal of Hypermedia & Technology-Enhanced Learning Jurnal Media Akademik (JMA) Journal of Innovative and Creativity Merkurius: Jurnal Riset Sistem Informasi dan Teknik Informatika
Resmi Darni
Universitas Negeri Padang
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Journal : Journal of Hypermedia

 1 
A Mobile Vocabulary Learning App for Engineering Contexts: Gamification, Self-Directed Learning, and User Progress Tracking Nursi, Anita; Darni, Resmi; Novaliendry, Dony
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.
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.
Co-Authors Agariadne Dwinggo Samala Ahmaddul Hadi, Ahmaddul Akhiru Ramadhan, Gian alfatakh, Muhamad amin Ambiyar, Ambiyar Amri, Alwi Dwi Ananda, Primayoga Andhika Herayono Annisa Wahyuni Asmara, Delvi Asrul Huda Batubara, Qothrun Nada Ma'ruf Bayu Ramadhani Fajri Budayawan, Khari Darwin, Widya Dedy Irfan Delsina Faiza Denny Kurniadi Desi Susanti Dio Alvendri Dol Frialdo Dony Novaliendry Edi Ismanto Efmi Maiyana Efrizon Efrizon Elfi Tasrif Elfina, Eni Elsa Sabrina Fadhli Ranuharja Fadrian, Yudha Fahlevi, Muhammad Farel Fitrani Dwina Geovanne Farell Ghina, Nadiyatul Hafizatunnisa Hansi Effendi Hasan Maksum Hidayati Hidayati Hilda Marliza Hilmi Asri Huda, Asrul Huriati Putri Ika Parma Dewi Iqbal Taufik Jusmardi Khalilul Afwan Kyranty Putri Zuhroh Lativa Mursyida Lativa Mursyida Legiman Slamet Lenggogeni Lenggogeni Luqyana Najla Mahesi Agni Zaus Marta, Rizkayeni Mazlini Adnan Melly Novalia Melri Deswina Mhd Arya Dhaifullah Minora Longgom Nasution Miranda Miranda Muchamad Subali Noto Muhammad Adri Muhammad Adri MUHAMMAD ANWAR Mursyida, Lativa Nabilla Yasmin Nadya Annisa Juventina Nita Sofia Nur Addina Nur Khairiyah Nursi, Anita Pipin Sumarni Pratama Benny Herlandi Putri Zuhroh, Kyranty Rachmah Nanda Octhavira Rahmad Alrian Rahmad Darmawan Rahman, Muhamad Fathur Rahmy Jasril, Ilmiyati Ramadhani Fajri, Bayu Risda Amini Rizkayeni Marta Rizky Ema Wulansari Rizky Nafarel Sidiq Robi Maulana Rudi, Muhamad Saputri, Sindi Saragih, Fadhillah Majid Silvia Gina Rahayu Alvi Sintia Okta Rina Sri Rizki Putri Primandari Suherman Suherman Taali, Taali Titi Sri Wahyuni Titin Sriwahyuni Triana Dewi, Olivia Usmeldi Usmeldi Vera Indriani Vera Irma Delianti Vitriani Vitrian Wahyu Permana Putra Wahyuni, Annisa Waskito Waskito, Waskito Winda Agustiarmi Yasdinul Huda Yasdinul Huda Yeka Hendriyani Yolanda Idha Fitri Yulyanti Harisman