Article Per Year (5 Year)
p-Index From 2021 - 2026
0.444
P-Index
This Author published in this journals
All Journal International Journal of Electrical and Computer Engineering JIPTEK : Jurnal Ilmiah Pendidikan Teknik dan Kejuruan
Rosihan Ariyuana, Rosihan
Sebelas Maret University
Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Education Electrical & Electronics Engineering Engineering Mechanical Engineering

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
Faster R-CNN implementation for hand sign recognition of the Indonesian sign language system (SIBI) Adhiatma, Paulus Lestyo; Prakisya, Nurcahya Pradana Taufik; Ariyuana, Rosihan
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i6.pp5759-5769

Abstract

The Indonesian sign language system (SIBI) is the authorized sign system in Indonesia that the deaf society uses to convey in Indonesian. However, its use still needs to be expanded and more widespread in the community, causing difficulties in communication for hard-of-hearing people. The product of deep learning technologies such as faster region-based convolutional neural network (Faster R-CNN) in object recognition has the potential to help improve communication between deaf people and the general public. This research will implement the Faster R-CNN algorithm with three different residual network (ResNet) architectures (50, 101, and 152) for SIBI recognition. The comparison of the faster R-CNN algorithm with different architectures is also conducted to identify the best architecture for SIBI recognition, and the results are evaluated using accuracy, precision, recall, and F1-score metrics from confusion matrix calculation and execution time. Faster R-CNN model with ResNet-50 architecture showed the best and most efficient performance with accuracy, recall, precision, and F1-score metrics of 96.15%, 95%, 93%, and 94%, respectively, and an execution time of 36.84 seconds in the testing process compared to models with ResNet-101 and ResNet-152 architectures.
Rekayasa Perangkat Lunak Aplikasi Presensi Mobile Menggunakan Metode Deep Learning Setiawan, Ragil; Prakisya, Nurcahya Pradana Taufik; Ariyuana, Rosihan
JIPTEK: Jurnal Ilmiah Pendidikan Teknik dan Kejuruan Vol 17, No 1 (2024): January
Publisher : Faculty of Teacher Training and Education Universitas Sebelas Maret Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20961/jiptek.v17i1.76556

Abstract

Facial recognition research has its challenges due to faces complexity, ranging from facial expressions and certain conditions that make facial recognition an exciting research experiment. Moreover, many-oriented applications of machine learning have moved to devices edge, and-based facial recognition is no exception mobile. Seeing the ongoing development of facial pattern recognition algorithms such as Viola Jones, Backpropagation, this research uses the MobileFaceNet  mobile CNN model which is currently popular to be implemented in the mobile-based facial recognition presence application at the Information and Computer Engineering Education (PTIK) FKIP UNS. The deep learning method is a method for understanding and classifying objects. In the developed application, a face is captured in an image. This research uses the help of the flutter framework and the Tensorflow Lite library to develop a presence application mobile facial recognition in real-time. This paper aims to determine the value of the memorization and generalization algorithms model of CNN MobileFaceNet  on the application.  A trial of the system has been carried out by involving 30 volunteers in the testing from 2016-2019 PTIK students by random sampling. Each test was carried out for 10 iterations. From the test results, the system memorization value is 84.5%. On the other hand, the generalization results get 70% in recognizing identical but not similar images correctly. In terms of memorization and generalization, these results are better than similar studies using backpropagation
Co-Authors Adhiatma, Paulus Lestyo Aris Budianto, Aris Dewanto Harjunowibowo Prakisya, Nurcahya Pradana Taufik Setiawan, Ragil Sri Hartati