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All Journal PROCESSOR Jurnal Ilmiah Sistem Informasi, Teknologi Informasi dan Sistem Komputer JOURNAL V-TECH (VISION TECHNOLOGY) JURNAL PENELITIAN PENDIDIKAN BAHASA DAN SASTRA Jurnal Pengabdian kepada Masyarakat Aspirasi : Publikasi Hasil Pengabdian dan Kegiatan Masyarakat Journal of Innovative and Creativity The Indonesian Journal of Computer Science Jurnal Pengabdian Masyarakat Unama Jurnal Agrisep
agus Nugroho
Unknown Affiliation
Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Languange, Linguistic, Communication & Media Mathematics Public Health Social Sciences Other

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Journal : The Indonesian Journal of Computer Science

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Optimasi Klasifikasi Gestur Tangan Menggunakan Metode CNN Dengan Implementasi Strategi Landmark Berbasis Warna Komplementer Agus Nugroho; Jasmir; M. Riza Pahlevi. B, S; Roby Setiawan
The Indonesian Journal of Computer Science Vol. 14 No. 2 (2025): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v14i2.4645

Abstract

The growth of hand gesture recognition technology has positively impacted various sectors. However, classification errors often occur due to the similarity of gesture shapes, which are challenging for models to differentiate. This study aims to develop a classification method based on Convolutional Neural Network (CNN) using a landmark modification approach with complementary colors. This approach applies significant color contrast to enhance the model’s ability to extract unique features from similar hand gestures. The dataset used includes gestures with color modifications on landmarks using an HSV-based color wheel to create maximum contrast. The data is then processed through bounding box creation, resizing, and transfer learning using the Teachable Machine architecture. The study results show a significant improvement in classification accuracy for models with landmark modifications compared to those without. Metrics analysis, including precision, recall, and F1-score, confirms that this approach effectively reduces classification errors caused by similar hand gestures.
Co-Authors Abdul Haris Ade Syoufa Aditya Tarikh Maulana Simbolon Amroni Desy Hertinsyana Devi Ratnasari Djoko Priambodo Dodi Sandra Eko AripWinanto Fajri Jakfar Gendoet Indarto Wibisono Haniva Mulyani Heriyanto Wibowo Ibnu Sani Wijaya indra indra Indra Indra Irfan Zikri Irina Mildawani Irmayani Irmayani Jasmir M. Fahmi Arsyad M. Riza Pahlevi. B, S M.Riza Pahlevi Meisak, Despita Mesquito Martins De Jesus Muhamad Alvin Aufar Pancawati Dewi Putri Athaya Robby Setiawan ROBY SETIAWAN Seilla Desilawati Setiawan Assegaf Sinung Try Nugroho Yudi Novianto