Jatropha Curcas Disease Identification With Extreme Learning Machine

Saragih, Triando Hamonangan; Fajri, Diny Melsye Nurul; Mahmudy, Wayan Firdaus; Abadi, Abdul Latief; Anggodo, Yusuf Priyo

doi:10.11591/ijeecs.v12.i2.pp883-888

Cited by 9 publications

(9 citation statements)

References 15 publications

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“…Berdasarkan hasil penelitian ini, dapat disimpulkan metode ini bisa menghasilkan akurasi yang lebih baik dibandingkan penelitian sebelumnya. Untuk penelitian selanjutnya, bisa menggunakan metode klasifikasi seperti Fuzzy KNN [16], Fuzzy Neural Network [7], [17] ataupun Extreme Learning Machine [18]- [20] untuk mendapatkan akurasi yang lebih baik lagi.…”

Section: Simpulanunclassified

Optimasi Fungsi Keanggotaan Fis Tsukamoto Menggunakan Simulated Annealing Untuk Identifikasi Penyakit Gigi

Saragih

Ramadhani

Mazdadi

et al. 2020

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Teeth are one of the tools in the framework related to the human stomach which fills as a food destroyer for simple processing. Diseases that attack teeth can withstand this action and cannot be distinguished quickly by young dental specialists. This problem can be solved by methods in the field of technology. The algorithm that can be used is FIS Tsukamoto in classification. Optimization of the membership function at FIS Tsukamoto is needed to improve accuracy. Optimization of FIS Tsukamoto membership function using Simulated Annealing produced the highest accuracy at 92.5% of the 100 test data.Keywords: Simulated Annealing; FIS Tsukamoto, Dental Disease, Optimization Gigi adalah salah satu alat dalam kerangka terkait perut manusia yang mengisi sebagai penghancur makanan untuk pemrosesan sederhana. Penyakit yang menyerang gigi dapat menahan tindakan ini dan tidak dapat dibedakan dengan cepat oleh dokter muda spesialis gigi. Masalah ini dapat diselesaikan dengan metode di bidang teknologi. Algoritma yang bisa digunakan yaitu FIS Tsukamoto dalam melakukan klasifikasi. Optimasi fungsi keanggotaan pada FIS Tsukamoto diperlukan untuk meningkatkan akurasi. Optimasi fungsi keanggotaan FIS Tsukamoto menggunakan Simulated Annealing menghasilkan akurasi paling tinggi yaitu 92,5% dari 100 data uji.Kata kunci: Simulated Annealing; FIS Tsukamoto, Penyakit Gigi, Optimisasi

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Section: Simpulanunclassified

Optimasi Fungsi Keanggotaan Fis Tsukamoto Menggunakan Simulated Annealing Untuk Identifikasi Penyakit Gigi

Saragih

Ramadhani

Mazdadi

et al. 2020

klik

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“…Making an expert system to solve these problems can be done in various ways, such as using Fuzzy Neural Network [5], Optimized Fuzzy Neural Network [6], Extreme Learning Machine [7], and Optimized Extreme Learning Machine [8]. Fuzzy Neural Network that has been done by Saragih et al provide an average accuracy of 11.2% using 30 symptoms and 9 diseases [5].…”

Section: Introductionmentioning

confidence: 99%

“…Then, Fajri et al made improvements by optimizing the Fuzzy Neural Network using Simulated Annealing so that it provides an average accuracy of 32.5%, better than previous research [6]. However, the computational complexity in the fuzzy defuzzification process is usually very high [9], so that in 2018, the Extreme Learning Machine algorithm was used by Saragih et al to identify Jatropha curcas disease with an average accuracy of 60.61% [7]. In the same year, Saragih et al performed an optimization on Extreme Learning Machine using Modified Simulated Annealing and produced a good average accuracy of 90,955%.…”

Section: Introductionmentioning

confidence: 99%

Jatropha Curcas Disease Identification using Random Forest

Saragih

Wijayaningrum

Haekal

2021

Jurnal Ilmiah Teknik Elektro Komputer Dan Informatika

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As one of the most versatile plants, Jatropha curcas is spread in various regions around the world because of the great benefits it provides. However, Jatropha curcas is easily attacked by viruses which then cause damage to the plant, such as yellowing leaves and secreting sap, making it necessary to identify Jatropha curcas disease to deal with the problem as early as possible so that the losses incurred are not too large. An expert system was built to be able to identify Jatropha curcas disease by adopting expert knowledge. The use of the Random Forest algorithm as one of the classification algorithms was applied in this study. By using a random forest, several disease prediction classes are generated by each decision tree that has been formed. The disease class with the most votes was used as the final result. In this study, the data used were 166 data with 9 diseases and 30 symptoms. The results showed that Random Forest outperformed other algorithms such as Fuzzy Neural Network and Extreme Learning Machine with an accuracy of 98.002% using the composition of training data and test data of 70:30.

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“…This machine-driven decision-making system is called machine learning [2]. One application of the machine learning method has been carried out by Saragih in the decision-making system for the classification of jatropha plant disease with an accuracy of 60.61% [3]. This result is categorized as quite good because it can achieve accuracy above 50%.…”

Section: Introductionmentioning

confidence: 99%

Detection of Disease and Pest of Kenaf Plant Based on Image Recognition with VGGNet19

Fajri

Mahmudy

Yulianti³

2021

Kno. Eng. Da. Sc.

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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.

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Jatropha Curcas Disease Identification With Extreme Learning Machine

Cited by 9 publications

References 15 publications

Optimasi Fungsi Keanggotaan Fis Tsukamoto Menggunakan Simulated Annealing Untuk Identifikasi Penyakit Gigi

Optimasi Fungsi Keanggotaan Fis Tsukamoto Menggunakan Simulated Annealing Untuk Identifikasi Penyakit Gigi

Jatropha Curcas Disease Identification using Random Forest

Detection of Disease and Pest of Kenaf Plant Based on Image Recognition with VGGNet19

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