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All Journal Voteteknika (Vocational Teknik Elektronika dan Informatika) Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Jurnal Penelitian Pendidikan IPA (JPPIPA) JURNAL PENDIDIKAN TAMBUSAI JETL (Journal Of Education, Teaching and Learning) Ensiklopedia of Journal JTP - Jurnal Teknologi Pendidikan JISIP: Jurnal Ilmu Sosial dan Pendidikan Jurnal Teknologi Informasi dan Pendidikan Suluah Bendang: Jurnal Ilmiah Pengabdian Kepada Masyarakat JURNAL PENDIDIKAN, SAINS DAN TEKNOLOGI Jurnal Pengabdian kepada Masyarakat Nusantara Jurnal Kependidikan: Jurnal Hasil Penelitian dan Kajian Kepustakaan di Bidang Pendidikan, Pengajaran dan Pembelajaran Jurnal SAINTIKOM (Jurnal Sains Manajemen Informatika dan Komputer) MANAJERIAL: Jurnal Inovasi Manajemen dan Supervisi Pendidikan COMMUNITY : Jurnal Pengabdian Kepada Masyarakat ACADEMIA: Jurnal Inovasi Riset Akademik Reflection Journal Priviet Social Sciences Journal Lentera: Multidisciplinary Studies Jurnal Teknik Komputer dan Informatika (JTeKI) Journal of Hypermedia & Technology-Enhanced Learning Journal of Innovative and Creativity HUMANIORASAINS Lentera: Multidisciplinary Studies Jejak digital: Jurnal Ilmiah Multidisiplin Jurnal Merah Putih Sekolah Dasar
Dony Novaliendry
Electronic Department, Engineering Faculty, Universitas Negeri Padang
Aerospace Engineering Agriculture, Biological Sciences & Forestry Arts Humanities Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mathematics Physics Public Health Social Sciences Other

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Implementasi Algoritma Fuzzy Tsukamoto Dalam Sistem Pendeteksi Gejala Social Anxiety Disorder Pada Mahasiswa Musfara Zahra Nadien; Muhammad Anwar; Dony Novaliendry; Randy Proska Sandra
Journal of Innovative and Creativity Vol. 5 No. 3 (2025)
Publisher : Fakultas Ilmu Pendidikan Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/joecy.v5i3.4578

Abstract

Social Anxiety Disorder (SAD) atau gangguan kecemasan sosial yang ditandai oleh rasa takut berlebihan dalam situasi sosial. Penelitian ini bertujuan untuk merancang dan mengimplementasikan sistem pendeteksi gejala Social Anxiety Disorder pada mahasiswa menggunakan algoritma Fuzzy Tsukamoto berbasis web. Sistem ini memanfaatkan data hasil kuesioner berdasarkan indikator gejala kecemasan sosial yang diadaptasi dari instrumen Hamilton Anxiety Rating Scale (HARS). Proses inferensi dilakukan melalui tahapan fuzzifikasi, pembentukan rule base, inferensi fuzzy, dan defuzzifikasi untuk menghasilkan nilai crisp yang menunjukkan tingkat kecemasan sosial. Metode pengembangan yang digunakan adalah Waterfall, yang meliputi tahap analisis kebutuhan, perancangan, implementasi, pengujian, dan pemeliharaan. Pengujian sistem dilakukan terhadap 20 responden mahasiswa. Hasil implementasi menunjukkan bahwa sistem mampu mengklasifikasikan tingkat kecemasan, yaitu tidak ada kecemasan, ringan, sedang, berat, dan sangat berat. Berdasarkan hasil validasi, diperoleh tingkat kesesuaian sebesar 90% antara hasil sistem dan hasil penilaian manual menggunakan metode HARS. Dengan demikian, sistem ini dapat digunakan sebagai alat bantu deteksi dini gangguan kecemasan sosial pada mahasiswa secara mandiri serta sebagai media pendukung pemantauan kesehatan mental di lingkungan kampus.
Development of Interactive Media to Improve CNC Machine Malfunction Handling Skills of TU 3A Based on Immersive Gamification Raihan Ramadhan; Rizky Ema Wulansari; Eko Indrawan; Dony Novaliendry; Dimas Aulia Saputra
Jurnal Penelitian Pendidikan IPA Vol 11 No 12 (2025): December
Publisher : Postgraduate, University of Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jppipa.v11i12.13088

Abstract

This research was conducted to design and produce an electronic module (e-module) for CNC Programming that integrates a project-based learning (PjBL) approach with an immersive gamification strategy, optimized for use on Android-based devices. The study employed a Research and Development (R&D) methodology, adopting the 4D framework consisting of the stages of define, design, develop, and disseminate. Data were obtained from expert validators, lecturers, and students to evaluate the e-module in terms of its validity, practicality, and effectiveness. The validation results indicated that the e-module achieved excellent validity, with average scores of 0.917 for media quality and 0.950 for material quality. The practicality evaluation yielded ratings of 96.94% from lecturers and 91.90% from students, both categorized as very practical. The effectiveness test produced an N-Gain value of 0.69, which reflects a moderate improvement in students’ learning performance. Furthermore, the t-test analysis (Sig. 0.001 < 0.05; t-count 3.806 > t-table value) demonstrated a statistically significant difference between the experimental and control groups. Overall, the developed PjBL-based e-module with immersive gamification features was found to be valid, practical, and effective, serving as an interactive learning medium that fosters student engagement and aligns with 21st-century educational demands.
A Aplikasi Web untuk Klasifikasi dan Deteksi Penyakit Daun Tomat Menggunakan Model CNN dan YOLO Novaliendry, Dony; Rizal, Fachri; Anwar, Muhammad; Irfan, Dedy
Jurnal Teknologi Informasi dan Pendidikan Vol. 19 No. 1 (2026): Jurnal Teknologi Informasi dan Pendidikan
Publisher : Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/jtip.v19i1.1073

Abstract

This study developed a web-based application for the classification and detection of tomato leaf diseases using Convolutional Neural Network (CNN) and You Only Look Once (YOLO) models. The research followed a Research and Development approach that consisted of requirement analysis, system design, implementation, model training, and testing. The CNN model was trained to classify tomato leaf images into specific disease categories, while the YOLO model was designed to detect and localize diseased areas in real time. Both models were integrated into a Flask-based web system to provide accessible and interactive functionality through standard web browsers. Testing results showed that the CNN model achieved an accuracy of 96.1%, effectively identifying disease types such as Early Blight and Bacterial Spot. The YOLO model reached a mean Average Precision (mAP) of 87.3% for real-time detection, successfully locating and labeling infected regions on tomato leaves. The integration of CNN and YOLO models demonstrated strong classification and detection performance, offering an efficient and scalable solution to support early disease diagnosis and digital transformation in precision agriculture.
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.
Web-Based Inventory Management System for Educational Training: Integrating EOQ and ARIMA for Data-Driven Learning Syamsi, Alkindi; Irfan, Dedy; Novaliendry, Dony; Sandra, Randi Proska
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.
Co-Authors Afifah Rizki, Putri Afiful Azkia Agariadne Dwinggo Samala Ahmaddul Hadi Ahmaddul Hadi, Ahmaddul Aieni , Nurul Al-Ayyubi, Andi Sultan Alfarouq, Ahmad Dzaki Amalinda, Ilsa Amanda, Adittya Ambiyar, Ambiyar Amelia Zahra Andhika Herayono Andhika Herayono Apriliyanti, Resti Ardika, Kiki Ariusni Ariusni Arrum Pramesti Asmar Yulastri Asmara, Delvi Astry Yulinda Elfani Aulia, Wahdatul Azhar, Viki Aziza, Dwi Ayu Budayawan , Khairi Budayawan, Khairi Budayawan, Khari Budi Prasetyo Cheng-Hong Yang, Cheng-Hong Chintia Ningsih, Nur Dedy Irfan Denny Kurniadi Dimas Aulia Saputra Eko Indrawan Elfina, Eni Elgi Janliza Putra elida elida Elmi, Hafiz Elsa Sabrina Erzitha, Ranny Farraz Hadyan Fathur Rahman Febby Apri Wenando Firdaus Firdaus Fitri, Ernarisa Ganefri . Geovanne Farell Gusti, Tika Sylviani Hadi Kurnia Saputra Hadi Kurnia Saputra Hadi, Ahmadul Hadyan, Farraz Hafilah Hamimi Hafilah Hamimi, Hafilah Hansi Effendi Hasan Maksum Hastria Effendi Hibatullah, Hulwa Zuhrahriani Huda, Yasdinul Ika Parma Dewi Ilahi, Akbar Insan Aljundi, Ihsanul Intan Maisa Tania Irma Yulia Basri Irnanda, Muhammad Fakhri Kartini, Ridha Kasmita Kasmita Kurnia Saputra, Hadi Kurniadi, Denny Leoparlin, Alfridho Lestari , Jumaita Levran, Abel Lovita, Vega Makmur, Brian Mardi Mardi Muhammad Adri Muhammad Adri Muhammad Anwar Muhammad Anwar Muhammad Farhan Mursyida, Lativa Musfara Zahra Nadien Nathasya Utami Hakim Naufal , Naufal Naufan Islami, Fattan Nazifa Dwi Putri Noviko, Heri Nursi, Anita Nurul Ikhsani, Syahdilla Pati Harau, Frans Surya Putra, Adrian Rama Putri, Selma Eka Rabbynawa, Glody Syah Rahadi, Muhammad Devin Rahadian Zainul Rahmadika, Sandi Rahman, Muhamad Fathur Rahmat Raihan Maulana Raihan Ramadhan Rakhel Cakra Sandika Ramadhani , Kharisa Ramadhani Fajri, Bayu Randy Proska Sandra Resmi Darni Riskia, Anita Ica Risna, Mega Rizal, Fachri Rizky Ema Wulansari Ronald Lorenzo Sandika, Rakhel Cakra Sandra, Randi Proska Sartika, Indah Saza Kurnia Utami Silvia Vaesar, Sahera Siptya Savira Rahmi Sisca Andriani Skoatiya, Najwa Sugiarto, Toto Syafiqoh, Iffat Syafrijon, Syafrijon Syamsi, Alkindi Syukhri, Syukhri Titi Titi Sriwahyuni Titin Sriwahyuni Usmeldi Usmeldi Venny Enrizal Vera Irma Delianti Viony Dwiyana Puteri Wahyuni, Novri Waskito Waskito, Waskito Yahmanita, Vanisa Tsuraya Yasdinul Huda Yeka Hendriyani Yolanda, Wira Yuli Pusparani Zafania, Puti