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All Journal Informatika Jurnal Sistim Informasi dan Teknologi Jurnal Informasi dan Teknologi Jurnal Sistem Informasi dan Sistem Komputer Jurnal Pustaka Data : Pusat Akses Kajian Database, Analisa Teknologi, dan Arsitektur Komputer Jurnal Sistim Informasi dan Teknologi Internet of Things and Artificial Intelligence Journal Bhandar: Harvesting Community Service in Asia
Manurung, Kiki Hariani
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Humanities Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Mechanical Engineering Social Sciences Other

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Journal : Informatika

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Integrasi Model Pembelajaran Mesin dalam Game Menggunakan Gerakan Tangan Hendra, Yomei; Sakinah, Putri; Maulana, Fajar; Manurung, Kiki Hariani
Jurnal Informatika Vol 12, No 3: 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 Ahmad Afif Aldo Eko Syaputra Alima Shofia Erlina Erlina Hafiz Hidayat Hayati, Nova Hendra, Yomei Jefri Rahmad Mulia Julius Santony Julius Santony Kaksim Maulana, Fajar Rahmad Mulia, Jefri SAKINAH, PUTRI Sopi Sapriadi Wendra, Yumai Weni Yulastri Yofhanda Septi Eirlangga Zulfa Zulfa