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All Journal International Journal of Electrical and Computer Engineering International Journal of Advances in Applied Sciences Seminar Nasional Aplikasi Teknologi Informasi (SNATI) Jurnal Ilmiah Informatika Komputer Jurnal Psikologi Jurnal Ilmiah Teknologi dan Rekayasa Prosiding KOMMIT Prosiding PESAT Majalah Ilmiah Matematika Komputer Majalah Ilmiah Ekonomi Komputer TELKOMNIKA (Telecommunication Computing Electronics and Control) JUTI: Jurnal Ilmiah Teknologi Informasi Jurnal Teknologi Informasi dan Ilmu Komputer JUITA : Jurnal Informatika Jurnal Ilmiah KOMPUTASI Sistemasi: Jurnal Sistem Informasi Computatio : Journal of Computer Science and Information Systems Jurnal Ilmiah FIFO Informatika Pertanian JURNAL MANAJEMEN INFORMATIKA (JUMIKA) Prosiding Seminar Nasional Darmajaya Petir JUTEKIN (Jurnal Manajemen Informatika) Energy : Jurnal Ilmiah Ilmu-Ilmu Teknik Journal of Information System, Applied, Management, Accounting and Research Jurnal Nasional Teknologi Komputer Jurnal Sistem Informasi International Journal Multidisciplinary Science
Dewi Agushinta R.
Gunadarma University
Religion Aerospace Engineering Agriculture, Biological Sciences & Forestry Humanities Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Social Sciences

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Journal : International Journal of Advances in Applied Sciences

 1 
Identification of mangrove tree species using deep learning method Paranita Asnur; Rifki Kosasih; Sarifuddin Madenda; Dewi A. Rahayu
International Journal of Advances in Applied Sciences Vol 12, No 2: June 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijaas.v12.i2.pp163-170

Abstract

Artificial intelligence can help classify plants to make identification easier for everyone. This technology can be used to classify mangrove trees. The degradation of mangrove forests has resulted in a 20% loss of biodiversity, an 80% loss of microbial decomposers, reduced C-organic soil, and fish spawning grounds, resulting in estimated losses in the ecological and economic fields for up to IDR 39 billion. The identification of different mangrove species is the first step in ensuring the preservation of these forests. Therefore, this research aimed to develop algorithms and a convolutional neural network (CNN) architecture to classify mangrove tree species with the highest possible accuracy using Python software. The architecture selection for this model includes a batch size of 32, an input image size of 128x128 pixels, four classes, four convolution layers, four rectified linear unit (ReLU) layers, 2x2 max-pooling, and two fully connected layers (FCL). The finding showed that the resulting accuracy from the test was 97.50%, while the validation test was 81.25%, applied to four types of mangrove leaves, including Avicenia marina, Avicenia officialis, Rizophora apiculata, and Soneratia caseolaris.
Identification of mangrove tree species using deep learning method Paranita Asnur; Rifki Kosasih; Sarifuddin Madenda; Dewi A. Rahayu
International Journal of Advances in Applied Sciences Vol 12, No 2: June 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijaas.v12.i2.pp163-170

Abstract

Artificial intelligence can help classify plants to make identification easier for everyone. This technology can be used to classify mangrove trees. The degradation of mangrove forests has resulted in a 20% loss of biodiversity, an 80% loss of microbial decomposers, reduced C-organic soil, and fish spawning grounds, resulting in estimated losses in the ecological and economic fields for up to IDR 39 billion. The identification of different mangrove species is the first step in ensuring the preservation of these forests. Therefore, this research aimed to develop algorithms and a convolutional neural network (CNN) architecture to classify mangrove tree species with the highest possible accuracy using Python software. The architecture selection for this model includes a batch size of 32, an input image size of 128x128 pixels, four classes, four convolution layers, four rectified linear unit (ReLU) layers, 2x2 max-pooling, and two fully connected layers (FCL). The finding showed that the resulting accuracy from the test was 97.50%, while the validation test was 81.25%, applied to four types of mangrove leaves, including Avicenia marina, Avicenia officialis, Rizophora apiculata, and Soneratia caseolaris.
Co-Authors -, Hustinawaty Achmad Benny Mutiara Adam Huda Nugraha Aditia Arga Pratama Agung Satria Ahmad Hidayat Akbar, Rizky Alif Ahmad Syamsudduha Andi Shahreza Harahap Anggari, Elevanita Anindito Yoga Pratama Anindito Yoga Pratama Anindito Yoga Pratama Anindito Yoga Pratama Antonius Angga Kurniawan Ardhani Reswari Yudistari Armita Widyasuri Ayu Hardianti Besty Ghina Cyntya Widyarsih Delvita Dita Putri Anggrayni Denisha Trihapningsari Dharma Tintri Ediraras Diana Tri Susetianigtias Dini Sundani Dyah Pratiwi Edy Nursanta Emirul Bahar Ety Sutanty Fajar Nugraha Ferina Ferdianti Gagah Lanang Ramadhan Grace Desi Geoloni Hafiz Ma'ruf Hanifah Aprilia Nur’aini Haniyah Haniyah Harya Iswara A.W. Henny Medyawati Henny Medyawati Henri Muel Herry Sussanto Hustinawaty Hustinawaty, Hustinawaty Ihsan Jatnika Ika Setiowati Suprihatin Indira Mahayani Irwan Bastian Jhordy Wong Johanna Sindya Widjaya Jonathan Hindharta Khoirul Islam Lia Ambarwati Lintang Yuniar Banowosari Lintang Yuniar Banowosari M. Abdul Mukhyi Mariono Reksoprodjo Martina Octavia Mega Maralisa Putri Metty Mustikasari Muhammad Edy Supriyadi Murniyati Murniyati Neneng Winarsih Regy Dwi Septian Remi Senjaya Remi Senjaya riamande jelita tambunan Rifki Kosasih Rindani, Fiena Rizka Yulianti Putri Rodiah Rodiah Rustam M. Ali Sandi Agung Sarifuddin Madenda Sigit Widiarto Soeltan Zaki Sova, Erma Sri Rahayu Puspita Sari sugrio dwi darmawan Suryadi H. S. Suryadi Harmanto Syifah Paujiah Vega Valentine Yahya Novi Andi Cuhwanto Yoga Yuniadi Yogi Oktopianto Yurista Vipriyanti Yusuf Triyuswoyo Yuti Dewita Arimbi