Article Per Year (5 Year)
p-Index From 2019 - 2024
0.23
P-Index
This Author published in this journals
All Journal JOURNAL OF ELECTRICAL AND SYSTEM CONTROL ENGINEERING
Vidya, Hurianti
Unknown Affiliation
Electrical & Electronics Engineering

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

 1 
TEA LEAVES GMB SERIES CLASIFFICATION USING CONVOLUTIONAL NEURAL NETWORK Rizal, Syamsul; Pratiwi, Nor Kumalasari Caecar; Ibrahim, Nur; Vidya, Hurianti; Saidah, Sofia; Fu'adah, R Yunendah Nur
JESCE (JOURNAL OF ELECTRICAL AND SYSTEM CONTROL ENGINEERING) Vol 3, No 2 (2020): Journal Of Electrical And System Control Engineering Februari
Publisher : Universitas Medan Area

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (860.876 KB) | DOI: 10.31289/jesce.v3i2.3320

Abstract

This study classifies GMB series tea leaves by using a convolutional neural network as a classification system. GMB series tea are the superior tea seeds in Indonesia. Gambung series, namely: GMB 1 to GMB 11, are planting material seeds that have been recommended by the Ministry of Agriculture. The potential of these tea series yield of 4,000 - 5,800 kg / ha of dried tea. The morphological similarity level of GMB 1 to GMB 11 is very high, because many elders from the clones are from the same crossing parents. During this time, the process of identifying GMB clones 1 through GMB 11 is done manually using the visual eye of an experts at PPTK Gambung. These experts are limited to be able to identify each tea series. This process is susceptible to errors in the reading of clone types, and is very dependent on the presence of the experts. If an error occurs in the process of identifying the type of clone, it will interfere with the nursery process. Errors in the selection of recommended clones will harm the process of a long period of time, because the economic age of tea plants can reach until 50 years. The potential loss of production due to misuse of plant material can reach 1,200 kg / ha per year. Against the background of these problems, it is very necessary to have a system to identify the GMB series clone. Continuous studies has been conducted to build an automation system for the identification and classification of GMB series tea clones. The system is designed using the Convolutional Neural Network (CNN) method. The results obtained from this system output in the form of accuracy with a value of 85%.
Co-Authors Nor Kumalasari Caecar Pratiwi Nur Ibrahim R. Yunendah Nur Fu’adah SOFIA SAIDAH Syamsul Rizal