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All Journal SELAPARANG: Jurnal Pengabdian Masyarakat Berkemajuan IKRA-ITH ABDIMAS Abdimas Galuh : Jurnal Pengabdian Kepada Masyarakat JIKA (Jurnal Informatika) Jurnal Sisfotek Global Jurnal Multidisiplin Madani (MUDIMA) Formosa Journal of Science and Technology (FJST) Jurnal Pengabdian Masyarakat Bestari (JPMB) Formosa Journal of Applied Sciences (FJAS) Teknik: Jurnal Teknik Mesin, Elektro dan Ilmu Komputer Universal Raharja Community (URNITY Journal) INFINITY : UKI TORAJA JOURNAL OF INFORMATION TECHNOLOGY Jurnal Elektronika dan Teknik Informatika Terapan SmartComp Modem : Jurnal Informatika dan Sains Teknologi Switch: Jurnal Sains dan Teknologi Informasi Merkurius: Jurnal Riset Sistem Informasi dan Teknik Informatika Neptunus: Jurnal Ilmu Komputer dan Teknologi Informasi
Prya Artha Widjaja
Matana University
Agriculture, Biological Sciences & Forestry Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Medicine & Pharmacology Nursing Public Health Social Sciences Other

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Journal : Formosa Journal of Science and Technology (FJST)

 1 
Determining Mango Plant Types Using YOLOv4 Prya Artha Widjaja; Jose Ryu Leonesta
Formosa Journal of Science and Technology Vol. 1 No. 8 (2022): December 2022
Publisher : PT FORMOSA CENDEKIA GLOBAL

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55927/fjst.v1i8.2155

Abstract

Mango plants consist of many types. This study tries to use leaf imagery to determine the type of mango plant. In this study, one of the object recognition methods in Deep Learning was used, namely YOLO (You Only Look Once) version 4. The types of mango used were manalagi, apple, golek and sweet and fragrant. The study used 457 pieces of data for model training and yielded an accuracy of around 95 percent.
Differentiate a Health and Sick of Mango Leaves Using YOLOv4 Prya Artha Widjaja; Jose Ryu Leonesta
Formosa Journal of Science and Technology Vol. 2 No. 7 (2023): July, 2023
Publisher : PT FORMOSA CENDEKIA GLOBAL

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55927/fjst.v2i7.4792

Abstract

Healthy plants will provide good results for farmers. As in humans, plants can also be affected by diseases that can result in death or crop failure. This study uses the object recognition method, namely YOLO (You Only Look Once) version 4 to determine whether a plant is categorized as sick or healthy. The data used in this research is secondary data. The results obtained were in line with expectations, namely being able to distinguish between healthy and diseased leaves.
Co-Authors Amelia, Sabrina Yose Anthony, Eveline Valencia Ardilla, Veronica Yose Ary Budi Warsito Ary Budi Warsito, Ary Budi Bakti Siregar Barus, Simon Prananta Celvin, Jullius Christian, Vincent Dennis Cristiano, Ezra Dhamma, Yonathan Arya F. Anthon Pangruruk Heven Jose Ryu Leonesta Julian, Felix Kevin kevin Laia, Nico Abel Leonesta, Jose Ryu Lianto, Vincentius Kelvin Liem, Kenneth Matius Monto Lungan Tangdililing Nico Abel Laia Nicolas Setiawan Novesa Erick Jong Oswaldo Steven Carlo Wijaya Paramitha Gunawan Robert Robert Sabrina Yose Amalia Sabrina Yose Amelia Steven Jonathan Roy Sujanto Sujanto Theo, Robert Veronica Yose Ardilla