Journal of Enviromental Engineering and Sustainable Technology
Vol 3, No 1 (2016)

IDENTIFICATION OF PATCHOULI LEAVES QUALITY USING SELF ORGANIZING MAPS (SOM) ARTIFICIAL NEURAL NETWORK

Kartika Purwandari (Brawijaya University)
Candra Dewi (Brawijaya University)
Imam Cholissodin (Brawijaya University)



Article Info

Publish Date
02 Aug 2016

Abstract

One of the essential oil export commodities from Indonesia is patchouli oil. However, the price of patchouli todays is unstable caused by the low quality of the oils, which has high levels of acid and lower alcohol content. One part of patchouli that is widely used to obtain essential oils is the leaf. The better quality of leaves will produce oil with grade quality. The quality of the leaves can be identified by its physical characteristics. Leaves that have a good quality are small leaves, thick and slightly yellowish red color. This identification process can be done visually, but, it will be easier if it can be done automatically using computer applications. Therefore, this paper performs automatic identification of leaves utilizing image of patchouli leaves and artificial neural network algorithm Self Organizing Maps (SOM). Identification was done to distinguish the leaves with good quality and poor. From the test results using the initial learning rate 0.1, 0.3 deduction learning rate, the minimum rate learning 0.0001, 40 training data and testing the data 60 obtained an average accuracy of 82.82%.

Copyrights © 2016






Journal Info

Abbrev

JEEST

Publisher

Subject

Environmental Science

Description

JEEST is an interdisciplinary and refereed journal, addresses matters related to environmental engineering and sustainable technology. Its range of themes encompasses ecological studies, field research, empirical work and descriptive analyses on topics such as environmental systems, environmental ...