Article Per Year (5 Year)
p-Index From 2021 - 2026
2.644
P-Index
This Author published in this journals
All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI) Bulletin of Electrical Engineering and Informatics Prosiding SNATIKA Vol 01 (2011) JOIV : International Journal on Informatics Visualization Sinkron : Jurnal dan Penelitian Teknik Informatika JMM (Jurnal Masyarakat Mandiri) International Journal of Natural Science and Engineering Indonesian Physical Review Jurnal Ekonomi Manajemen Sistem Informasi Jurnal Teknik Informatika (JUTIF) Journal of Applied Data Sciences Jurnal Masyarakat Indonesia The Indonesian Journal of Computer Science
Poltak Sihombing
FMIPA USU, Medan
Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Physics Other

Published : 19 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search
Journal : IAES International Journal of Artificial Intelligence (IJ-AI)

 1 
Smart agriculture model in detecting oil palm plantation diseases using a convolution neural network Gunawan, Gunawan; Zarlis, Muhammad; Sihombing, Poltak; Sutarman, Sutarman
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 3: September 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i3.pp3164-3171

Abstract

Planning models for sustainable crop care in the context of smart agriculture are complex issues as they involve many factors such as productivity, quality, growth sustainability, workforce use, and information technology use. In this study, we will create an optimized model using a convolution neural network (CNN) that can classify and monitor plant diseases. Part of the plant care system is to be aware of plant diseases and to be able to deal with them immediately. This study aims to acquire a new smart farming model for integrated crop care. The results of this research are findings in the form of a CNN model for classifying plant diseases detected from the leaves of the plants studied in oil palm. Testing using Google Colab obtains 100% accuracy and 99% accuracy using a teachable machine. The contributions of this paper create a new model in the field of informatics, especially in the field of intelligent agriculture based on information technology.
Co-Authors Abner H.Bajari Andri Andri Andri Budiman, Mohammad Ari Juliana Arjon Turnip Asrianda Asrianda Baehaqi Budhiarti Nababan, Erna Butar Butar, Kartika Dewi Darwin Darwin Efendi, Syahril Erna Budhiarti Nababan Fahmi Fahmi Fauzan Nurahmadi Ghazali, Alfin Gunawan Gunawan Handrizal Handrizal Hasugian , Paska Marto Herman Mawengkang Irpan Pardosi Jayawarsa, A.A. Ketut K. M. Nasution , Mahyuddin Manurung, Jonson Maya Silvi Lydia Mesran, Mesran Muhammad Zarlis, Muhammad Naemah Mubarakah Ng Poi Wong Opim Salim Sitompul Roni Yunis Sarif, Muhammad Irfan Sawaluddin Sawaluddin, Sawaluddin Siti Sarah Harahap Sutarman Sutarman Tulus Tulus Weber, Gerhard Wilhelm Yudhistira Adhitya Pratama