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All Journal Register: Jurnal Ilmiah Teknologi Sistem Informasi Jurnal Elkolind : Jurnal Elektronika dan Otomasi Industri
Andrie Asmara, Rosa
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering

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Pemanfaatan Algoritma Faster R-CNN ResNet-101 Untuk Deteksi Potongan Tubuh Manusia Hanun, Nabilah; Sarosa, Mochammad; Andrie Asmara, Rosa
Jurnal Elektronika dan Otomasi Industri Vol. 10 No. 1 (2023): Jurnal Elkolind Vol. 10, No. 1, 2023
Publisher : Program Studi Teknik Elektronika Politeknik Negeri Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33795/elkolind.v10i1.2754

Abstract

Deteksi potongan tubuh manusia adalah tugas yang sangat menantang. Deteksi potongan tubuh manusia dapat diterapkan dalam berbagai situasi seperti pencarian korban bencana alam, pencarian potongan tubuh yang tertutup dengan objek lain, korban kecelakaan, dan lain – lain sehingga dapat memudahkan para pengguna untuk mendeteksi potongan tubuh manusia. Potongan tubuh dalam penelitian ini dibedakan dalam dua kategori yaitu berdasarkan kelengkapan potongan tubuh manusia dan kelas yang dideteksi. Tujuan pada penelitian ini adalah untuk mengetahui kinerja Faster R-CNN dengan ResNet-101 dalam mendeteksi potongan tubuh manusia. Jumlah dataset yang digunakan sebanyak 100 citra gambar. Berdasarkan hasil penelitian, Faster R-CNN dengan ResNet-101 pada step 1000, 2000, dan 3000 menunjukkan bahwa hasil terbaik pada deteksi step 3000 dengan Precision 79,50%, Recall 68,80%, dan F1 score 73,76%. Pada penelitian ini, pemanfaatan algoritma Faster R-CNN dengan ResNet-101 mampu mendeteksi potongan tubuh Manusia namun kualitas piksel dan ketajaman pada citra gambar juga mempengaruhi hasil deteksi.
Detecting Objects Using Haar Cascade for Human Counting Implemented in OpenMV Mentari, Mustika; Andrie Asmara, Rosa; Arai, Kohei; Sakti Oktafiansyah, Haidar
Register: Jurnal Ilmiah Teknologi Sistem Informasi Vol 9 No 2 (2023): July
Publisher : Information Systems - Universitas Pesantren Tinggi Darul Ulum

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26594/register.v9i2.3175

Abstract

Sight is a fundamental sense for humans, and individuals with visual impairments often rely on assistance from others or tools that promote independence in performing various tasks. One crucial aspect of aiding visually impaired individuals involves the detection and counting of objects. This paper aims to develop a simulation tool designed to assist visually impaired individuals in detecting and counting human objects. The tool's implementation necessitates a synergy of both hardware and software components, with OpenMV serving as a central hardware device in this study. The research software was developed using the Haar Cascade Classifier algorithm. The research process commences with the acquisition of image data through the OpenMV camera. Subsequently, the image data undergoes several stages of processing, including the utilization of the Haar Cascade classifier method within the OpenMV framework. The resulting output consists of bounding boxes delineating the detection areas and the tally of identified human objects. The results of human object detection and counting using OpenMV exhibit an accuracy rate of 71%. Moreover, when applied to video footage, the OpenMV system yields a correct detection rate of 73% for counting human objects. In summary, this study presents a valuable tool that aids visually impaired individuals in the detection and counting of human objects, achieving commendable accuracy rates through the implementation of OpenMV and the Haar Cascade Classifier algorithm.
Co-Authors Hanun, Nabilah Kohei Arai Mustika Mentari Sakti Oktafiansyah, Haidar Sarosa, Mochammad