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All Journal Journal of Information Systems Engineering and Business Intelligence Indonesian Journal on Computing (Indo-JC) Al Ishlah Jurnal Pendidikan JURNAL MEDIA INFORMATIKA BUDIDARMA Jurnal Tekno Insentif JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Building of Informatics, Technology and Science Bubungan Tinggi: Jurnal Pengabdian Masyarakat BERNAS: Jurnal Pengabdian Kepada Masyarakat Charity : Jurnal Pengabdian Masyarakat Journal La Multiapp Instal : Jurnal Komputer Jurnal Nasional Teknik Elektro dan Teknologi Informasi eProceedings of Engineering Jurnal Pengabdian Masyarakat Bangsa Jurnal INFOTEL LOGIC: Jurnal Penelitian Informatika Proceeding of Community Service and Engagement
Indra Lukmana Sardi
Universitas Telkom
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Journal : Jurnal INFOTEL

 1 
Vegetation classification algorithm using convolutional neural network ResNet50 for vegetation mapping in Bandung district area Rina Pudji Astuti; Ema Rachmawati; Edwar Edwar; Simon Siregar; Indra Lukmana Sardi; Arfianto Fahmi; Yayan Agustian; Agus Cahya Ananda Yoga Putra; Faishal Daffa
JURNAL INFOTEL Vol 14 No 2 (2022): May 2022
Publisher : LPPM INSTITUT TEKNOLOGI TELKOM PURWOKERTO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/infotel.v14i2.756

Abstract

Bandung District is one of crop provider for West Java Province. About 31.158,22 ha is used for crop. However, some of them are not maintained well due to lack of vegetation map information. Local authority has tried to map the vegetation in their area by using free license satellite images, and aerial images from Unmanned Aerial Vehicle (UAV). Despite both images being able to provide large plantation area images, both are unable to classify the vegetation type in those images. Telkom University with Bandung Agriculture Regional Office (Dinas Pertanian Kabupaten Bandung) has conducted joint research to develop algorithm based on 50-layer residual neural network (ResNet50) to classify the vegetation type. The input is of this algorithm is primarily aerial images are captured from different type, height, and position of crops. Seven different ResNet50 configurations have been set and simulated to classify the crop images. The result is the configuration with resized images, employing triangular policy of cyclic learning rate with rate 1.10−7 – 1.10−4 comes out as the best setup with more than 95% accuracy and relatively low loss.
Co-Authors Abdillah, Mgs. M. Fakhri Adrianto, Rexcy Putra Agus Cahya Ananda Yoga Putra Agus Kusnayat Alfian Yulianto Angelina Prima Kurniati Angelino Sandy Kusuma Anisa Hendiawan Anisa Herdiani Arfianto Fahmi Arfive Gandhi Arie Kurniawan Arif Arif Bijaksana Aristyo Hadikusuma Azizi, Nur Fuad Bastomy Bastomy Dana S. Kusumo Dana Sulistiyo Kusumo Dana Sulistyo Kusumo Dana Sulistyo Kusumo Danang Junaedi Edwar Eko Darwiyanto Ema Rachmawati erna febriyanti Faishal Daffa Faris Muhammad Fathurrahman , Muhammad Wima Febrio Ibrahim Sebayang Fitriyani Fitriyani Gede Agung Ary Wisudiawan Giffarinda, Raihan Ekaputra Hadi Kurniawan Sidiq Hani Dika Riani Herdiani , Anisa I Gusti Ayu Chandra Devi Ibnu Asror Isman Kurniawan Jahtra Genio Muhammad Kridanto Surendro Kusumo, Dana Sulistyo Laksitowening, Kusuma Ayu Lestari, Marinda Endi Markus Bernard1 Moch Arif Bijaksana Mohammad Yasin Abdul Hafidh Mohd. Fauzy Monterico Adrian Muhammad Bambang Hidayanto Muhammad Fikri Ridhwan Muhammad Rezaldy Muhammad Shalahuddin Aldi Baihaqi Muhammad, Adika Nabila Nanda Widyastuti Nungki Selviandro Nurwibowo, Prigusti Arruum P, Shinta Yulia Prati Hutari Gani Prayogo, Ariq Dary Pristantya, Mayang Rais Hafiyyan Ramanti Dwi Indrapurasih Refanto, Moch Billy Ridho Hadad Rina Pudji Astuti Rizky Wahyu Kurniawati Rosa Reska Riskiana Saputra, Wahyu Nur Shaufiah Shaufiah . Shinta Yulia Puspitasari Shinta Yulia Puspitasari Sidiq, Hadi Kurniawan Simon Siregar Sri Martini Sri Widowati Stefanus Wisnu Adi Nugroho Winandri, Reyhan Rahma Yayan Agustian Yudhono, Efwandha Yustisia Susandi Zian Alfaen