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Dicky Pratama
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Journal : JuSiTik : Jurnal Sistem dan Teknologi Informasi Komunikasi

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Deteksi Ujung Jari menggunakan Faster-RCNN dengan Arsitektur Inception v2 pada Citra Derau Derry Alamsyah; Dicky Pratama
JuSiTik : Jurnal Sistem dan Teknologi Informasi Komunikasi Vol. 2 No. 1 (2018): Jurnal Sistem dan Teknologi Informasi Komunikasi
Publisher : Universitas Katolik Musi Charitas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32524/jusitik.v2i1.429

Abstract

Fingertip detection is a field on computers that has extensive space in field: NUI, robotics, etc. CNN is one method that is being used in object detection, with some CNN updates being faster - RCNN is able to detect objects very well. This study conducted the ability of Faster-RCNN in detecting fingertips with the Inception V2 architecture. Implementation is done on images that have noise and not. The results showed that image without noise has 91% accuracy, while for each noisy image: Gaussian, Salt and Pepper, Poisson and Speckle had an accuracy of 34%, 5%, 80% and 21%.
Co-Authors Derry Alamsyah