Article Per Year (5 Year)
p-Index From 2019 - 2024
0.23
P-Index
This Author published in this journals
All Journal Techno (Jurnal Fakultas Teknik, Universitas Muhammadiyah Purwokerto) Semesta Teknika Jurnal Ilmiah Dinamika Rekayasa (DINAREK) Indonesian Journal of Community Services Makara Journal of Technology Techno Jurnal Rekayasa elektrika Jurnal INFOTEL
Hesti Susilawati
Program Studi Teknik Elektro, Fakultas Sains Dan Teknik, Universitas Jenderal Soedirman, Purwokerto 53122
Humanities Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mechanical Engineering Social Sciences

Published : 14 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search
Journal : Makara Journal of Technology

 1 
Static Hand Gesture Recognition of Indonesian Sign Language System Based on Backpropagation Neural Networks Asriani, Farida; Susilawati, Hesti
Makara Journal of Technology Vol. 14, No. 2
Publisher : UI Scholars Hub

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Static Hand Gesture Recognition of Indonesian Sign Language System Based on Backpropagation Neural Networks. The main objective of this research is to perform pattern recognition of static hand gesture in Indonesian sign language. Basically, pattern recognition of static hand gesture in the form of image had three phases include: 1) segmentation of the image that will be recognizable form of the hands and face, 2) feature extraction and 3) pattern classification. In this research, we used images data of 15 classes of words static. Segmentation is performed using HSV with a threshold filter based on skin color. Feature extraction performed with the Haar wavelet decomposition filter to level 2. Classification is done by applying the back propagation system of neural network architecture with 4096 neurons in input layer, 75 neurons in hidden layer and 15 neurons in output layer. The system was tested by using 225 data validation and accuracy achieved was 69%.
Co-Authors Akhmad Khahlil Gibran Alfin Hikmaturokhman Azis Wisnu Widhi Nugraha Azis Wisnu Widhi Nugraha Baharudin Yudha Permana Dimass Noly Mardhiko Dimass Noly Mardhiko Dwi Ary Asmoro Dwi Ary Asmoro, Dwi Ary Farida Asriani FX Anjar Tri Laksono Gito Sugiyanto Hari Prasetijo Huzaely Latief Sunan Ilham Perdana Muhamad Ridwan Nur Chasanah Rahmat K. Noviandono Rahmat K. Noviandono, Rahmat K. Rani Aulia Imran Shinta Yunita Sari Suroso Suroso Tantiningrum Niken Taufik Faturohman Teguh Cahyono Tigar Putri Adhiana Widhiatmoko H.P. Widhiatmoko Herry Purnomo Widhiatmoko Widhiatmoko Widhiatmoko Widhiatmoko, Widhiatmoko