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All Journal Jurnal Buana Informatika JUTI: Jurnal Ilmiah Teknologi Informasi Jurnal Simantec Jurnal Ilmiah Kursor Jurnal Titra Agora Prosiding SNATIKA Vol 01 (2011) Jurnal Teknologi Informasi dan Ilmu Komputer SMATIKA Jurnal Dimensi Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Conference on Innovation and Application of Science and Technology (CIASTECH) Kurawal - Jurnal Teknologi, Informasi dan Industri Jurnal Maksipreneur: Manajemen, Koperasi, dan Entrepreneurship Jurnal Teknik Informatika (JUTIF) Jurnal Informatika Polinema (JIP)
Hendry Setiawan
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Agriculture, Biological Sciences & Forestry Arts Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Library & Information Science Social Sciences Other

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Journal : Jurnal Teknik Informatika (JUTIF)

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ORNAMENTAL PLANT IDENTIFICATION SYSTEM USING TRANSFER LEARNING ON CONVOLUTIONAL NEURAL NETWORK Prilianti, Kestrilia Rega; Oktariyanto, Vidian Vito; Setiawan, Hendry
Jurnal Teknik Informatika (Jutif) Vol. 5 No. 4 (2024): JUTIF Volume 5, Number 4, August 2024
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2024.5.4.1964

Abstract

There was a spike in the ornamental plants as a hobby while spending time at home during the COVID pandemic when people were restricted to activities outside the house. Unfortunately, along with this trend also came the serious issues associated with fake reports claiming that some ornamental plants were harmful to people's health. The public is more worried and perplexed by this situation, which also erodes their confidence in ornamental plants. This research aims to develop a real-time ornamental plant identification system as an educational medium for the public. To increase the system's accuracy, the transfer learning method is applied to the modified MobileNet CNN model. There are 9 species of popular ornamental plants in this identification system. From the experiments, it is known that the best accuracy has been achieved using the Adagrad optimization method (96% for training and 88% for testing data). The CNN model is then embedded in PLANTIS, an Android-based application prototype for ease of use purpose.
Co-Authors Antonius Duty Susilo, Antonius Duty Christianto O, Ivan Eka S. P., Kielvien Lourensius Febrian, Nicholas Fika Handani Gunawan, Dennys Gusfandi Gusfandi Hendra Kelana, Oesman Herditomo Herditomo Hesniati Hesniati Hilary, Antony Irawan, Paulus Lucky Tirma Irawan, Paulus Lucky Tirma Kamil Dhermawan, Abdurrachman Kelanata, Oesman Hendra Kendy Setiawan Hartono Kestrilia Rega Prilianti Kristian, Hans Kurniawan, Ardian Laulita, Nasar Buntu Leona, Ossie Leona, Ossie Lo Hanjaya Hanafi Lo Hanjaya Hanafi, Lo Hanjaya Lucky Tirma Irawan, Paulus Lydia Sasmita Mariana, Priska Meliyana Rahayu Yoanita Mimboro, Prasetyo Mochamad Subianto Oesman Hendra Kelana Oktariyanto, Vidian Vito Paulus Lucky Tirma Irawan Paulus Lucky Tirma Irawan Paulus Lucky Tirma Irawan Raharjo, Jani Romy Budhi Widodo Rosalin Veliana Rully Soelaiman Samuel Perkasa Samuel Samuel Sunday Noya Supusepa, Alfred Christian Tan, Dewi Fandelia Tjokro Atmodjo, Felix Adrian Toh Meng Loo Trya Sovi Kartikasari Wardojo, Kevin Putra Wibowo, Inovency Permata Windra Swastika