Optimized fuzzy neural network for Jatropha Curcas plant disease identification

Fajri, Diny Melsye Nurul; Saragih, Triando Hamonangan; Hamdianah, Andi; Mahmudy, Wayan Firdaus; Anggodo, Yusuf Priyo

doi:10.1109/siet.2017.8304152

Cited by 7 publications

(9 citation statements)

References 16 publications

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“…The best method for STD classification is K-NN with the highest accurary that is 90%. The accuracy results are quite good, but in the future the authors will conduct research to improve the accuracy of classification results by optimizing parameters of K-NN method that has been done in our previous research for other classification problems [29], [30]. In addition, in the next research will also test the amount of training data and testing data in order to obtain accurate classification results.…”

Section: Discussionmentioning

confidence: 99%

Performance Analysis of Data Mining Methods for Sexually Transmitted Disease Classification

Yuliastuti¹,

Alfiyatin²,

Rizki³

et al. 2018

IJECE

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According to health reports of Malang city, many people are exposed to sexually transmitted diseases and most sufferers are not aware of the symptoms. Malang city being known as a city of education so that every year the population number increases, it is at risk of increasing the spread of sexually transmitted diseases virus. This problem is important to be solved to treat earlier sufferers sexually transmitted diseases virus in order to reduce the burden of patient spending. In this research, authors conduct data mining methods to classifying sexually transmitted diseases. From the experiment result shows that K-NN is the best method for solve this problem with 90% accuracy.

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Section: Discussionmentioning

confidence: 99%

Performance Analysis of Data Mining Methods for Sexually Transmitted Disease Classification

Yuliastuti¹,

Alfiyatin²,

Rizki³

et al. 2018

IJECE

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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

“…Simulated Annealing diambil sebagai optimisasi fungsi keanggotaan fuzzy untuk meningkatkan hasil akurasi. Berdasarkan penelitian sebelumnya oleh Fajri et al[7], Simulated Annealing mampu meningkatkan hasil akurasi dengan mengoptimasi fungsi keanggotaan. Oleh karena itu penulis mengambil topik fungsi penelitian optimasi keanggotaan fuzzy menggunakan Simulated Annealing pada identifikasi penyakit gigi menggunakan FIS Tsukamoto.…”

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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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“…There are few methods that can be used for identification, such as Dempster-Shafer [3], Fuzzy Neural Network [4], Optimized Fuzzy Neural Network [5], Extreme Learning Machine [6] and Naive Bayes [7] which work based on certainty and probabilistic method. Dhempster-Shafer method use belief value for making decision.…”

Section: Introductionmentioning

confidence: 99%

Comparative Study of Decision Tree, K-Nearest Neighbor, and Modified K-Nearest Neighbor on Jatropha Curcas Plant Disease Identification

Saragih

Fajri

Rakhmandasari

2020

KINETIK

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Jatropha Curcas is a very useful plant that can be used as a bio fuel for diesel engines replacing the coal. In Indonesia, there are few plantation that plant Jatropha Curcas. But there is so limited farmers that understand in detail about the disease of Jatropha Curcas and it may cause a big loss during harvesting when the disease occured with no further action. An expert system can help the farmers to identify the lant diseases of Jatropha Curcas. The objective of this research is to compare several identification and classification methods, such as Decision Tree, K-Nearest Neighbor and its modification. The comparison is based on the accuracy. Modified K-Nearest Neighbor method given the best accuracy result that is 67.74%.

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Optimized fuzzy neural network for Jatropha Curcas plant disease identification

Cited by 7 publications

References 16 publications

Performance Analysis of Data Mining Methods for Sexually Transmitted Disease Classification

Performance Analysis of Data Mining Methods for Sexually Transmitted Disease Classification

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

Comparative Study of Decision Tree, K-Nearest Neighbor, and Modified K-Nearest Neighbor on Jatropha Curcas Plant Disease Identification

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