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Alauddin, Mukhammad Wildan
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering

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Extreme Learning Machine Weight Optimization using Particle Swarm Optimization to Identify Sugar Cane Disease Alauddin, Mukhammad Wildan; Mahmudy, Wayan Firdaus; Abadi, Abdul Latief
Journal of Information Technology and Computer Science Vol. 4 No. 2: September 2019
Publisher : Faculty of Computer Science (FILKOM) Brawijaya University

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1135.372 KB) | DOI: 10.25126/jitecs.201942116

Abstract

Sugar cane disease is a major factor in reducing sugar cane yields. The low intensity of experts to go into the field to check the condition of sugar cane causes the handling of sugarcane disease tends to be slow. This problem can be solved by instilling expert intelligence on sugar cane into an expert system. In this study the method of classification of sugar cane disease was proposed using Extreme Learning Machine (ELM). However, ELM alone is not enough to classify multilabel and multiclass disease case data in this study. Therefore, it is proposed to optimize the weight of hidden neurons in ELM using Particle Swarm Optimization (PSO). The experimental results show that the classification using ELM alone can reach an accuracy rate of 71%. After the weight of hidden neurons from ELM was optimized, the accuracy rate became 79.92% or an increase of 8.92%.
Co-Authors Abdul Latief Abadi Wayan Firdaus Mahmudy