Article Per Year (5 Year)
p-Index From 2021 - 2026
1.513
P-Index
This Author published in this journals
All Journal Sinkron : Jurnal dan Penelitian Teknik Informatika Informatika Jurnal Informasi dan Teknologi Jatilima : Jurnal Multimedia Dan Teknologi Informasi Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer) Jurnal Ipteks Terapan : research of applied science and education Journal of Student Development Information System (JoSDIS) Internet of Things and Artificial Intelligence Journal Marsipature Hutanabe: Jurnal Pengabdian Kepada Masyarakat
Hendra, Yomei
Informatika, Universitas Adzkia
Aerospace Engineering Agriculture, Biological Sciences & Forestry Arts Humanities Astronomy Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Electrical & Electronics Engineering Energy Engineering Health Professions Immunology & microbiology Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Materials Science & Nanotechnology Mathematics Medicine & Pharmacology Neuroscience Nursing Physics Public Health Veterinary Other

Published : 11 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 11 Documents
Search

 1   2 
Integrasi Model Pembelajaran Mesin dalam Game Menggunakan Gerakan Tangan Yomei Hendra; Putri Sakinah; Fajar Maulana; Kiki Hariani Manurung
Jurnal Informatika Vol 12, No 3 (2024): INFORMATIKA
Publisher : Fakultas Sains & Teknologi, Universitas Labuhanbatu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36987/informatika.v12i3.6826

Abstract

This study develops a Tetris game controlled through hand gestures using a machine learning model. The primary objective of this research is to create an interactive and responsive gaming experience by utilizing hand gesture detection as the main control mechanism. A hand gesture dataset was collected from videos segmented into individual frames, which were then analyzed using MediaPipe to detect and label gestures. The machine learning model employs a Convolutional Neural Network (CNN) trained to recognize hand gesture patterns and translate them into commands within the game. After implementation, an evaluation was conducted by distributing questionnaires to 18 Informatics students at Adzkia University to assess the system's comfort and responsiveness. The questionnaire results showed a high satisfaction level, with an average score of 84.56, covering evaluations of control ease, gesture detection accuracy, and system responsiveness. The average score for ease of use reached 85, indicating that the majority of users found the gesture-based controls comfortable. This study demonstrates that applying machine learning models in gesture-based control games can provide a more interactive and responsive experience, with potential applications in other interactive technologies.
Co-Authors Ainil Mardiah Aldo Eko Syaputra Aly, Moustafa H Asyahri Hadi Nasyuha Ayun, Aisyah Qurrata Budy Satria Fajar Maulana Hamsar , Ali Hamsar, Ali Hariani Manurung, Kiki Hayati , Nova Hayati, Nova Husna, Radhiatul Kiki Hariani Manurung Manurung, Kiki Hariani Maulana , Fajar Maulana, Fajar Mufid, Muhammad Fauzan Muhammad Imam Akbar Muhammad Thoriq Putri Sakinah Rahman, Zumardi SAKINAH, PUTRI Thoriq, Muhammad Yofhanda Septi Eirlangga