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All Journal Indonesian Journal of Data and Science Letters in Information Technology Education (LITE) International Journal of Robotics and Control Systems
Kurniawan, Wendy Cahya
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Mathematics

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Genially Interactive Media to Boost Interest in Learning Electrical Basics in Vocational Schools Aprillia, Irma Rizky; Kurniawan, Wendy Cahya
Letters in Information Technology Education (LITE) Vol 7, No 1 (2024)
Publisher : Universitas Negeri Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17977/um010v7i12024p20-25

Abstract

This development research aims to create interactive media based on Genially for the subject of “Fundamentals of Electrical Engineering.” The study aims to assess the product’s feasibility and analyze the learning interest of vocational high school (SMK) students after using this interactive learning tool. The research follows the Four-D model (Define, Design, Develop, Disseminate). The results indicate that the developed interactive learning media is highly suitable for use as a learning tool for 10th-grade vocational high school students. Based on the combined validity results of the product, a score of 92 per cent was obtained, categorizing it as highly feasible for use as a learning medium. Additionally, the implementation results showed a learning interest score of 89 per cent, categorized as very high, indicating that students have a high interest in Basic Electricity, specifically in the elements of electrical measuring instruments and testing tools, when using the Genially-based interactive learning media
Educational Game-Based Learning Media to Grow Learning Motivation in Computer and Basic Network Subjects in Vocational High Schools Faiz, Yoga Hanif; Kurniawan, Wendy Cahya
Letters in Information Technology Education (LITE) Vol 6, No 1 (2023)
Publisher : Universitas Negeri Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17977/um010v6i12023p27-30

Abstract

The use of technology in the learning process requires educators to be able to adapt to technology in the learning process. Based on the observations that have been made, in the Subject of Computers and the basic network of computer assembly shows that students are less motivated. To foster motivation to study computer assembly material, media was developed to discuss basic competencies regarding computer assembly tools. Researchers make Educational Game-Based Learning Media as Learning Media that adapts to digital technology. This media has many advantages, including being easy to operate, not limited by distance and space, not boring, and easy to understand. The purpose of this development research is to develop and determine the feasibility of Educational Game-based learning media to foster motivation to learn basic subjects of Computers and Networks in TKJ. The research and development method used is Design Thinking with the following stages: 1) Empathize; 2) Define; 3) idea; 4) Prototype; 5) Test. The types of instruments used are game test questionnaires and student learning motivation tests. The type of data in this study is quantitative and quantitative with data collection methods using questionnaires. The results of the research and development carried out produced educational game learning media for computer subjects and basic network computer assembly materials. Feasibility analysis on material validation 96.45 percent, media expert validation 96.46 percent, product trial 84.23 percent, and learning motivation test 84.38 percent. Based on the results of the feasibility analysis of Educational Game learning media it is said to be "Valid" or Decent Enough. The results of the analysis of learning motivation are said to be high and can foster learning motivation. It can be concluded that Educational Game learning media can be used as a complement to the learning process and can foster student learning motivation.
Hand Keypoint-Based CNN for SIBI Sign Language Recognition Handayani, Anik Nur; Amaliya, Sholikhatul; Akbar, Muhammad Iqbal; Wiryawan, Muhammad Zaki; Liang, Yeoh Wen; Kurniawan, Wendy Cahya
International Journal of Robotics and Control Systems Vol 5, No 2 (2025)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v5i2.1745

Abstract

SIBI is less widely adopted, and the lack of an efficient recognition system limits its accessibility. SIBI gestures often involve subtle hand movements and complex finger configurations, requiring precise feature extraction and classification techniques. This study addresses these issues using a Hand Keypoint-based Convolutional Neural Network (HK-CNN) for SIBI classification. The research utilizes Kinect 2.0 for precise data collection, enabling accurate hand keypoint detection and preprocessing. The optimal data acquisition distance between 50 and 60 cm from the camera is considered to obtain clear and detailed images. The methodology includes four key stages: data collection, preprocessing (keypoint extraction and image filtering), classification using HK-CNN with ResNet-50, EfficientNet, and InceptionV3, and performance evaluation. Experimental results demonstrate that EfficientNet achieves the highest accuracy of 99.1% in the 60:40 data split scenario, with superior precision and recall, making it ideal for real-time applications. ResNet-50 also performs well with 99.3% accuracy in the 20:80 split but requires longer computation time, while InceptionV3 is less efficient for real-time applications. Compared to traditional CNN methods, HK-CNN significantly enhances accuracy and efficiency. In conclusion, this study provides a robust and adaptable solution for SIBI recognition, facilitating inclusivity in education, public services, and workplace communication. Future research should expand dataset diversity and explore dynamic gesture recognition for further improvements.
Yoga Posture Recognition and Classification Using YOLOv5 Maqbullah, Afwatul; Handayani, Anik Nur; Kurniawan, Wendy Cahya
Indonesian Journal of Data and Science Vol. 6 No. 2 (2025): Indonesian Journal of Data and Science
Publisher : yocto brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijodas.v6i2.228

Abstract

Yoga, a centuries-old health practice from India, has gained global recognition for its benefits to physical, mental, and emotional well-being. However, incorrect execution of yoga poses can lead to injuries or diminished results. This research develops an automated system for recognizing and classifying yoga postures using YOLOv5, a state-of-the-art deep learning algorithm. YOLOv5, part of the YOLO (You Only Look Once) series, is designed for real-time object detection and offers enhanced performance through features like anchor-free detection and adaptive training strategies. The study collects a dataset of 1,000 images across 20 yoga pose categories, followed by manual annotation and training using transfer learning. Validation results show strong performance, achieving an accuracy of 90% with precision and recall scores of 0.942 and 0.941, respectively, and mAP@50 and mAP@50-95 values of 0.976 and 0.866. Despite challenges with certain poses showing lower accuracy due to variations in posture and dataset limitations, the model demonstrates robustness in detecting and classifying yoga postures effectively. This system has potential applications in artificial intelligence-driven yoga education, enabling practitioners to train independently with real-time feedback
Co-Authors Amaliya, Sholikhatul Anik Nur Handayani Aprillia, Irma Rizky Faiz, Yoga Hanif Liang, Yeoh Wen Maqbullah, Afwatul Muhammad Iqbal Akbar Wiryawan, Muhammad Zaki