Article Per Year (5 Year)
p-Index From 2019 - 2024
5.119
P-Index
This Author published in this journals
All Journal ELKHA : Jurnal Teknik Elektro Jurnal Simetris Jurnal Teknologi Informasi dan Ilmu Komputer Annual Research Seminar Jurnal Komputer Terapan Jusikom : Jurnal Sistem Komputer Musirawas JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI Jurnal Sisfokom (Sistem Informasi dan Komputer) Petir Jurdimas (Jurnal Pengabdian Kepada Masyarakat) Royal KOMPUTIKA - Jurnal Sistem Komputer Jurnal Informatika Global STRING (Satuan Tulisan Riset dan Inovasi Teknologi) jurnal teknik informatika dan sistem informasi Jurnal Saintekom : Sains, Teknologi, Komputer dan Manajemen Algoritme Jurnal Mahasiswa Teknik Informatika Informatics and Enginering Dedication MDP Student Conference
Muhammad Ezar Al Rivan
Universitas Multi Data Palembang
Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Mathematics Social Sciences Other

Published : 39 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 3 Documents
Search
Journal : jurnal teknik informatika dan sistem informasi

 1 
Pengenalan Alfabet Bahasa Isyarat Amerika Menggunakan Edge Oriented Histogram dan Image Matching Fareza, Ivan; Busdin, Rusdie; Al Rivan, Muhammad Ezar; Irsyad, Hafiz
Jurnal Teknik Informatika dan Sistem Informasi Vol 4 No 1 (2018): JuTISI
Publisher : Maranatha University Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (883.573 KB)

Abstract

Sign Language is a way to communicate to people with disabilities. American Sign Language (ASL) is one among other sign languages. Sign language image would be extracted using Edge Oriented Histogram (EOH). In Content-Based Image Retrieval, a feature from query image will be compared to database image to find out the best matching method so three matching methods will be used. The matching methods are Earth Mover Distance, Hausdorff Distance, and Sum of Absolute Difference. The smallest distance shows the strong similarity between query image and database image. The Sum of Absolute Difference is outperformed of other in case the most of relevant image can be retrieved. The order of methods to recognize alphabet (from the best one) is Sum of Absolute Difference following by Earth Mover Distance and Hausdorff Distance. Hausdorff Distance has smallest running time using 4 bin features. Earth Mover Distance has smallest running time using 6 bin features. Sum of Absolute Difference has smallest running time using 9 bin features, so the method can be recommended to recognize ASL.
Pengenalan Alfabet American Sign Language Menggunakan K-Nearest Neighbors Dengan Ekstraksi Fitur Histogram Of Oriented Gradients Al Rivan, Muhammad Ezar; Irsyad, Hafiz; Kevin, Kevin; Narta, Arta Tri
Jurnal Teknik Informatika dan Sistem Informasi Vol 5 No 3 (2019): JuTISI
Publisher : Maranatha University Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28932/jutisi.v5i3.1936

Abstract

Sign Language use to communicate to people with dissabilities. American Sign Language (ASL) one of popular sign language. Histogram of Oriented Gradient (HOG) can be use as feature extraction. Then feature stored in database. K-Nearest Neighbor use to measure distance between feature train and feature test. There are three distance use in this paper consist of Euclidean Distance, Manhattan Distance and Chebychev Distance. The best result are 0,99 when using Euclidean Distance and Manhattan Distance with k=3 dan k=5
Klasifikasi American Sign Language Menggunakan Ekstraksi Fitur Histogram of Oriented Gradients dan Jaringan Syaraf Tiruan Al Rivan, Muhammad Ezar; Noviardy, Mochammad Trinanda
Jurnal Teknik Informatika dan Sistem Informasi Vol 6 No 3 (2020): JuTISI
Publisher : Maranatha University Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28932/jutisi.v6i3.2844

Abstract

Sign languages have various types, one of which is American Sign Language (ASL). In this study, ASL images from the handshape alphabet were extracted using Histogram of Oriented Gradient (HOG) then these features were used for the classification of Artificial Neural Networks (ANN) with various training functions using 3 variations of multi-layer network architecture where ANN architecture consists of one hidden layer. Based on ANN training, trainbr test results have a higher success rate than other training functions. In architecture with 15 neurons in the hidden layer get an accuracy value of 99.29%, a precision of 91.84%, and a recall of 91.47%. The test results show that using the HOG feature and ANN classification method for ASL recognition gives a good level of accuracy, with an overall accuracy of 5 neurons 95.38%, 10 neurons 96.64%, and 15 neurons with 97.32%. Keywords— Artificial Neural Network; American Sign Language; Histogram of Oriented Gradient; Training Function
Co-Authors Ahmad Farisi Alvin Setiawan Alwyn Giovri Riyadi Andree Fendiawati Angella Octavia Arta Tri Narta Bhagaskara Bhagaskara Billy Gunawan Busdin, Rusdie Devella, Siska Dini Amputri Elisa Putri Suwanto Fareza, Ivan Felix Antony Fendy Novianto Lukman Gabriela Repca Sung Gasim Giovani Prakasa Gandi Hafiz Irsyad Hafiz Irsyad Hafiz Irsyad Irsyad, Hafiz Irvan Wijaya Kevin kevin Molavi Arman Monica Rizki Ayustin Muhammad Fachrurrozi Syawalludin Muhammad Restu Alviando Narta, Arta Tri Ningrum Larasati Noviardy, Mochammad Trinanda Nur Rachmat Orlando Orlando Randy Andreo Sonaru Reynald Dwika Prameswara Rheza Rijaya Ricky Yohannes Risha Ambar Wati Sehat Martinus Surya Benediktus Siti Nadra Steven Steven Suherman, Jessica Tegar Juangkara Tommy Saputra William Tanzil Winston Kennedy Yoannita Yoannita Yohannes Yohannes