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All Journal Journal of Information Technology and Computer Science JOURNAL OF ELECTRICAL AND SYSTEM CONTROL ENGINEERING INOVTEK POLBENG JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Jurnal Nasional Teknik Elektro dan Teknologi Informasi Jurnal Pengabdian Masyarakat eProceedings of Engineering Jurnal Rekayasa elektrika Telkatika: Jurnal Telekomunikasi Elektro Komputasi & Informatika Proceeding of Community Service and Engagement
Efri Suhartono
Telkom University
Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Public Health Other

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Journal : Journal of Information Technology and Computer Science

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Wood Species Identification using Convolutional Neural Network (CNN) Architectures on Macroscopic Images Oktaria, Anindita Safna; Prakasa, Esa; Suhartono, Efri
Journal of Information Technology and Computer Science Vol. 4 No. 3: Desember 2019
Publisher : Faculty of Computer Science (FILKOM) Brawijaya University

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1569.891 KB) | DOI: 10.25126/jitecs.201943155

Abstract

Indonesia is a country that is very rich in tree species that grow in forests. Wood growth in Indonesia consists of around 4000 species that have different names and characteristics. These differences can determine the quality and exact use of each type of wood. The procedure of standard identification is currently still carried out through visual observation by the wood anatomist. The wood identification process is very in need of the availability of wood anatomists, with a limited amount of wood anatomist will affect the result and the length of time to make an identification. This thesis uses an identification system that can classify wood based on species names with a macroscopic image of wood and the implementation of the Convolutional Neural Network (CNN) method as a classification algorithm. Supporting architecture used is AlexNet, ResNet, and GoogLeNet. Architecture is then compared to a simple CNN architecture that is made namely Kayu30Net. Kayu30Net architecture has a precision performance value reaching 84.6%, recall 83.9%, F1 score 83.1% and an accuracy of 71.6%. In the wood species classification system using CNN, it is obtained that AlexNet as the best architecture that refers to a precision value of 98.4%, recall 98.4%, F1 score 98.3% and an accuracy of 96.7%.
Co-Authors Agung Mujadid Ahmad Zaky Rafif Musthafa Ahmad Zaky Rafif Muthafa Akhmad Hambali Anam, Nasehun Andi Ade Yustika Annisa Puji Lestari Ardiansyah, Faiz Rizqullah Ardio Pratama Putra Arif Maulana Ashiddiqi , Muhamad Hazbi Aulia Nugraha Deasy Puspawati Dega Pradipta Ramadhan Dewa Mahardhika Pratama B Dwi Mustofa, Ihsan Eka Sugiarto Eka Wulandari Ekki Kurniawan Erixno, Oon Esa Prakasa, Esa Farah Pranidasari Faris Bayu Azanto Fauzan Adhima Hilmi Febriani Ruming Sari Firman Ag. Roni Gusmanda, Ilham Hani Khairunnisa Hasna Maharani Hazmi, Kiflan Mohammad I Nyoman Apraz Ramantryana I Putu Yowan Nugraha Suparta Irma Safitri Irvan Ragil Boesandi Jangkung Raharjo Jaspar Hasudungan Kharisma Bani Adam Kiflan Mohammad Hazmi Kurniawan Gustyanto Michael Binson Situmeang Muh Zidni Makarim Muh. Gazali Saleh Muhammad Hanif Abdurrahman Muhammad Rizqi Nor Kumalasari Caecar Pratiwi Nugroho , Haris Tri Nur Ibrahim Nur Ramadhani Nusyahya, Regisa Oktaria, Anindita Safna Pasa, Faris Alfaroby Pradipta , Dega Pratama Putra, Ardio Purnamasari , Rita R. Yunendah Nur Fu’adah Ramadhan , Ridwan Ramadhan, Muhammad Agung Ramadhana, Rexy Yusuf Ramadhani, Farah Rifki Rahman Nur Ikhsan Rissa Rahmania Rita Magdalena Rita Purnamasari Sandi, Irfan Maulana Kurnia Sari, Febriani Ruming Simamora , Haichel Anggy Paro Simanullang, Charlos Alvaredo Sjafril Darana SOFIA SAIDAH Suci Aulia Sugondo Hadiyoso Tri Hanuranto, Ahmad Trisucipto, Muhammad Umar Afnan Rahmatullah Wan Muhammad Faizal Wulan Dwi Suryandari Yoren, Muhammad Ikhlashul Yuni Rizki Linggasari YUNITA, TRASMA Zariaqwila , DTM Faiq