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Contact Name
Anwar Fattah
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anwar.fattah@uniba-bpn.ac.id
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+6282157007397
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anwar.fattah@uniba-bpn.ac.id
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Program Studi Teknik Elektro Fakultas Teknologi industri, Universitas Balikpapan Jl.Pupuk Raya, Kelurahan Gn Bahagia, Kecamatan Balikpapan Selatan, Balikpapan Tel./Fax: 0542-764205/ 7127865
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Jurnal Teknik Elektro Uniba (JTE Uniba)
Published by Universitas Balikpapan
ISSN : 25286498     EISSN : 25490842     DOI : https://doi.org/10.36277/jteuniba
Core Subject : Science, Engineering,
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Energy Industrial & Manufacturing Engineering
JTE UNIBA (Jurnal Teknik Elektro UNIBA) E-ISSN 2549-0842 dan 2528-6498 (media cetak), adalah jurnal ilmiah yang mempublikasikan hasil penelitian di bidang inovasi teknologi terapan dengan cakupan: Teknik Mesin, Teknik Elektro, Teknik Elektronika, teknik Kontrol dan Instrumentasi ,Teknik Informatika, dan Telekomunikasi
Arjuna Subject : Teknik (semua kategori) - Teknik Listrik dan Elektro
Articles 1 Documents
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Search results for , issue "Vol 4 No 1 (2019): Vol 4 No 1 (2019): JTE UNIBA (Jurnal Teknik Elektro Uniba)" : 1 Documents clear
IDENTIFIKASI CITRA DAUN DENGAN MENGGUNAKAN METODE DEEP LEARNING CONVOLUTIONAL NEURAL NETWORK (CNN) saiful rahman, aswadul fitri; b, a asni; kurniawan, septian dwi
Jurnal Teknik Elektro Uniba (JTE Uniba) Vol 4 No 1 (2019): Vol 4 No 1 (2019): JTE UNIBA (Jurnal Teknik Elektro Uniba)
Publisher : lembaga Penelitian Universitas Balikpapan

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (748.761 KB) | DOI: 10.36277/jteuniba.v4i1.55

Abstract

In the world of programming, deep learning methods of convolutional neural networks (CNN) may be very rarely heard, because this method was only popular around 2015, and this time I will conduct an experiment using this method, where I will conduct experiments on identification systems leaf image whose application is almost similar to a face recognition system, the leaf image identification system itself consists of detection and classification stages. Both of these stages are done so quickly by humans but it takes a long time for the computer. The application also uses MATLAB 2018a software with the CNN method we can find out the image data classification and can do the process of identifying images properly.

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