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All Journal Knowledge Engineering and Data Science Indonesian Journal of Electrical Engineering and Computer Science
Diny Melsye Nurul Fajri
Brawijaya University
Computer Science & IT Engineering

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Detection of Disease and Pest of Kenaf Plant Based on Image Recognition with VGGNet19 Diny Melsye Nurul Fajri; Wayan Firdaus Mahmudy; Titiek Yulianti
Knowledge Engineering and Data Science Vol 4, No 1 (2021)
Publisher : Universitas Negeri Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17977/um018v4i12021p55-68

Abstract

One of the advantages of Kenaf fiber as an environmental management product that is currently in the center of attention is the use of Kenaf fiber for luxury car interiors with environmentally friendly plastic materials. The opportunity to export Kenaf fiber raw material will provide significant benefits, especially in the agricultural sector in Indonesia. However, there are problems in several areas of Kenaf's garden, namely plants that are attacked by diseases and pests, which cause reduced yields and even death. This problem is caused by the lack of expertise and working hours of extension workers as well as farmers' knowledge about Kenaf plants which have a terrible effect on Kenaf plants. The development of information technology can be overcome by imparting knowledge into machines known as artificial intelligence. In this study, the Convolutional Neural Network method was applied, which aims to identify symptoms and provide information about disease symptoms in Kenaf plants based on images so that early control of plant diseases can be carried out. Data processing trained directly from kenaf plantations obtained an accuracy of 57.56% for the first two classes of introduction to the VGGNet19 architecture and 25.37% for the four classes of the second introduction to the VGGNet19 architecture. The 5×5 block matrix input feature has been added in training to get maximum results.
Jatropha Curcas Disease Identification With Extreme Learning Machine Triando Hamonangan Saragih; Diny Melsye Nurul Fajri; Wayan Firdaus Mahmudy; Abdul Latief Abadi; Yusuf Priyo Anggodo
Indonesian Journal of Electrical Engineering and Computer Science Vol 12, No 2: November 2018
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v12.i2.pp883-888

Abstract

Jatropha is a plant that has many functions, but this plant can be attacked by various diseases. Expert systems can be applied in identifying so that can help both farmers and extension workers to identify the disease. one of method that can be used is Extreme Learning Machine. Extreme Learning Machine is a method of learning in Neural Network which has a one-time iteration concept in each process. In this study get a maximum accuracy of 66.67% with an average accuracy of 60.61%. This proves the identification using Extreme Learning Machine is better than the comparison method that has been done before.
Co-Authors Abdul Latief Abadi Saragih, Triando Hamonangan Titiek Yulianti Wayan Firdaus Mahmudy Yusuf Priyo Anggodo