Article Per Year (5 Year)
p-Index From 2021 - 2026
2.978
P-Index
This Author published in this journals
All Journal Katalogis Natural Science: Journal of Science and Technology Dimas: Jurnal Pemikiran Agama untuk Pemberdayaan Lentera Pendidikan : Jurnal Ilmu Tarbiyah dan Keguruan Jurnal Ilmiah Mahasiswa FEB The International Journal of Accounting and Business Society Gravitasi COMPETITIVENESS JOURNAL JURNAL NASIONAL TEKNIK ELEKTRO JOIV : International Journal on Informatics Visualization Journal of Information Technology and Computer Engineering JURNAL SILOGISME : Kajian Ilmu Matematika dan Pembelajarannya Jurnal Ilmiah Mandala Education (JIME) JISIP: Jurnal Ilmu Sosial dan Pendidikan JUPE : Jurnal Pendidikan Mandala Idaarah: Jurnal Manajemen Pendidikan Buletin Ilmiah Sarjana Teknik Elektro Al asma : Journal of Islamic Education Alauddin Journal of Mathematics Education (AJME) Prosiding Seminar Nasional Sisfotek (Sistem Informasi dan Teknologi Informasi)
Muhammad Rusydi H.
Jurusan Fisika, Universitas Tadulako, Kota Palu, Indonesia
Religion Arts Humanities Astronomy Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Physics Social Sciences Other

Published : 30 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search
Journal : Journal of Information Technology and Computer Engineering

 1 
Hand Gesture to Control Virtual Keyboard using Neural Network Anandika, Arrya; Rusydi, Muhammad Ilhamdi; Utami, Pepi Putri; Hadelina, Rizka; Sasaki, Minoru
JITCE (Journal of Information Technology and Computer Engineering) Vol. 7 No. 01 (2023)
Publisher : Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/jitce.7.01.40-48.2023

Abstract

Disability is one of a person's physical and mental conditions that can inhibit normal daily activities. One of the disabilities that can be found in disability is speech without fingers. Persons with disabilities have obstacles in communicating with people around both verbally and in writing. Communication tools to help people with disabilities without finger fingers continue to be developed, one of them is by creating a virtual keyboard using a Leap Motion sensor. The hand gestures are captured using the Leap Motion sensor so that the direction of the hand gesture in the form of pitch, yaw, and roll is obtained. The direction values are grouped into normal, right, left, up, down, and rotating gestures to control the virtual keyboard. The amount of data used for gesture recognition in this study was 5400 data consisting of 3780 training data and 1620 test data. The results of data testing conducted using the Artificial Neural Network method obtained an accuracy value of 98.82%. This study also performed a virtual keyboard performance test directly by typing 20 types of characters conducted by 15 respondents three times. The average time needed by respondents in typing is 5.45 seconds per character.
ANN Models for Shoulder Pain Detection based on Human Facial Expression Covered by Mask Hadelina, Rizka; Rusydi, Muhammad Ilhamdi; Firza, Mutia; Samuel, Oluwarotimi Williams
JITCE (Journal of Information Technology and Computer Engineering) Vol. 7 No. 01 (2023)
Publisher : Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/jitce.7.01.49-55.2023

Abstract

Facial expressions are a method to communicate if someone feels pain. Moreover, coding facial movements to assess pain requires extensive training and is time-consuming for clinical practice. In addition, in Covid 19 pandemic, it was difficult to determine this expression due to the mask on the face. There for, it needs to develop a system that can detect the pain from facial expressions when a person is wearing a mask. There are 41 points used to form 19 geometrical features. It used 20.000 frames of 24 respondents from the dataset as secondary data . From these data, training, and testing were carried out using the ANN (Artificial Neural Network) method with a variation of the number of neurons in the hidden layer, i.e., 5, 10, 15, and 20 neurons. The results obtained from testing these data are the highest accuracy of 86% with the number of 20 hidden layers.
Co-Authors A. Sriyanti AA Sudharmawan, AA Achruh, Andi Ahmad Fadil Bagenda, Ahmad Fadil Anandika, Arrya Andi Dian Angriani, Andi Dian Ariadi Hazmi Aziz, Nurjannah Baharuddin Budi Sunaryo, Budi Eka Damayanti Firza, Mutia Fitriani Nur, Fitriani Gugus Irianto Gultom, Andre Paskah Hadelina, Rizka Hamdi, Khairil Hasan, Rezkianti Hidayat, Syarif - J, Jusriadi Jamidun, Jamidun Jordan, Adam Khidayati, Paridah Laksono, Pringgo Dwi Latuconsina, Nur Khalisah Matsushita, Kojiro Mattoliang, Lisnasari Andi Moh Dahlan Th Musa Muguro, Joseph Muhammad Ardiansyah Muhammad Fadil Mursalim, Ummi Kalsum Naharullah, Ahmad Sazky Nakajima, Rio Noverika Windasari Nur Miftahul Jannah Nur, Ahmad Hasan Nurhadi, Rahmad Novan Nurkhalisah Latuconsina, Nurkhalisah Nursalam P, Subagio Palembai, Suriadiningrat Ramadhani, Salisa 'Asyarina Ramlee, Ridza Azri Rosidi Sabhan Sabhan Samad, Rus'an Samuel, Oluwarotimi Williams Sandra Sandra Sasaki, Minoru SB, Kirbani Solly Aryza Sri Sulasteri, Sri Suhaimi, Muhammad Syaiful Amri Bin Suharti Suryanto, Wiwit Syamsuddin, Nur Syamsi Thamrin Tayeb, Thamrin Trisno Adi, Bambang Utami, Pepi Putri Utomo, Lilik Prihadi Yudea Yuhefizar Yuhefizar Zakia Zakia Zelly Rizky Fitriani