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All Journal Jurnal Simetris JUITA : Jurnal Informatika Jurnal CoreIT SELAPARANG: Jurnal Pengabdian Masyarakat Berkemajuan JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI Jurnal Teknologi Komputer dan Sistem Informasi jurnal teknik informatika dan sistem informasi Jutisi: Jurnal Ilmiah Teknik Informatika dan Sistem Informasi Algoritme Jurnal Mahasiswa Teknik Informatika Jurnal Ilmiah Teknologi dan Komputer (JITTER) MDP Student Conference
Devella, Siska
UNIVERSITAS MULTI DATA PALEMBANG
Agriculture, Biological Sciences & Forestry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Medicine & Pharmacology Nursing Public Health Social Sciences Other

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Journal : Jurnal Ilmiah Teknologi dan Komputer (JITTER)

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Pengenalan Penggunaan Helm Proyek Berstandar Pada Citra Foto Berdasarkan SIFT Dengan SVM Siska Devella; Albert Kusuma Wijaya
JITTER : Jurnal Ilmiah Teknologi dan Komputer Vol 3 No 2 (2022): Jurnal Jitter Vol. 3, No. 2, August 2022
Publisher : Program Studi Teknologi Informasi, Fakultas Teknik, Universitas Udayana

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (412.203 KB) | DOI: 10.24843/JTRTI.2022.v03.i02.p09

Abstract

One of the causes of the high number of work accidents in Indonesia is not using personal protective equipment. The project helmet is a personal protective equipment that serves to protect he head. However, the level of awareness of workers using helmets in this project is still lacking. his study aims to determine the accuracy level of introduction to the use of standard project helmets. Scale Invariant Feature Transform (SIFT) and Support Vector Machine (SVM) are feature extraction and classification methods used in this study. The data used is in the form of 90 photos which are divided equally into 3 types of images. Research shows that there are 170 out of 180 upper bodies that have been successfully detected. The kernels used are linear, gaussian and polynomial. By sing 119 data as training data and 51 data as test data, the highest accuracy results are obtained the linear kernel with an overall accuracy rate of 68.63%.
Co-Authors Adrianus Prabowo Al Rivan, M. Ezar Albert Kusuma Wijaya Celvine Adi Putra Derry Alamsyah Dewi Marcella Firda Novia Rahmawati Gracivo Elsion Victory Hadisaputra, William Julian Rusli Tee Baldi Kelvin Arianto Kevin Oktavius Meiriyama Meiriyama Meiriyama Meiriyama Muhammad Ezar Al Rivan Ningrum Larasati Nur Rachmat Pandrean, Ade Hendri Rial Prasthio Rusbandi Rusbandi Sandra Mareza Adelfi Saputra, Jordi Valentino Ruben Ho Yohannes Yohannes