Klasifikasi Buah Belimbing Manis dan Tidak Manis Berdasarkan Citra Red Green Blue Menggunakan Fuzzy Neural Network

BSTRACTClassical classification problems that can not be solved using the NN can be done using the FNN. Thedifference lies in the use of learning targets, which uses a degree of membership in the output. This studyaims to create a classification of star fruit to sweet and not sweet categories with non destructive methodusing fuzzy neural network. Red green and blue components of the image of the star fruit is used as an inputparameter. FNN 3-15-2 accuration obtained is 88.89% by using 15 neurons in the hidden layer, MSE9.13e-09 at epoch 16th. Keyword : classification, fuzzy neural network, starfruit, non-destructive grading, pattern recognition

Multi objective genetic algorithm for training three term backpropagation network

Multi Objective Evolutionary Algorithms has been applied for learning problem in Artificial Neural Networks to improve the generalization of the training and testing unseen data.This paper proposes the simultaneous optimization method for training Three Term Back Propagation Network (TTBPN) learning using Multi Objective Genetic Algorithm.The Non-dominated Sorting Genetic Algorithm II is applied to optimize the TTBPN structure by simultaneously reducing the error and complexity in terms of number of hidden nodes of the network for better accuracy in classification problem.This methodology is applied in two kinds of multiclasses data set obtained from the University of California at Irvine repository.The results obtained for training and testing on the datasets illustrate less network error and better classification accuracy, besides having simple architecture for the TTBPN

Implementasi Neural Network Backpropagation Untuk Identifikasi Tingkat Manis Buah Belimbing Berdasarkan Citra Rgb

  Star fruit classification is needed to maintain quality and improve competitiveness. Star fruit-based sweetness can be done destructively and non-destructively. Nondestructive can be done by measuring the correlation value of red, green, blue (RGB) star fruit image with Total Dissolved Solids (TPT) contained in starfruit. This study aims to develop an artificial intelligence system model to classify star fruit non-destructively based on the red-green-blue component using Neural Network (NN). The input parameter used is the red-green-blue component of the star fruit image which has been correlated to the TPT. The amount of sample data used is 99 pieces, which is 33 sweet starfruit image, 33 medium starfruit image and 33 image starfruit acid. A total of 81 data were used as training data and 18 data were used as test data. To obtain the best introductory results experiments were conducted using 6 variations of the number of neurons in the hidden layer. The classification into acid, medium and sweet fruit classes in this study obtained the best NN model using red, green and blue input parameters with 2 neurons in the hidden layer. The NN backpropatation 3-2-1 model provides an accuracy of 66.67% with 2 neurons in the hidden layer, MSE of 4.73e-06 on epoch 1.   Keyword : classification, neural network, starfruit, non-destructive grading, pattern recognition.   &nbsp

Identifikasi Tingkat Manis Buah Belimbing Berdasarkan Citra Red Green Blue Menggunakan Fuzzy Neural Network

  Fuzzy Neural Network (FNN) has a capability to classify a pattern within two different classes which a classical Neural Network (NN) is failed to do so. The fuzzy pattern classification use membership degree on output of neuron as learning target. This research aim is to develop an artificial intelligence system model for non-destructive classification of starfruit using Fuzzy Neural Network. The input parameter is the estimator parameter of starfruit sweet level of red, green and blue index color obtained from image processing. The best result of starfruit sweet level identification using FNN with three classification class target (sour, medium and sweet) is achieved with 25 neurons in hidden layer and 14th epoch with 100% accuracy.   Keyword : classification, fuzzy neural network, starfruit, non-destructive grading, pattern recognition.   &nbsp

Klasifikasi Kematangan Buah Manggis Ekspor dan Lokal Berdasarkan Warna dan Tekstur Menggunakan Fuzzy Neural Network

Fuzzy neural network (FNN) memiliki kemampuan untuk melakukan klasifikasi terhadap suatu pola yang berada di dalam dua kelas yang tidak dapat diklasifikasi menggunakan model klasifikasi klasik neural network (NN). Penelitian ini bertujuan mengembangkan model klasifikasi buah manggis segar secara non-destruktif dengan menggunakan FNN. FNN yang dipakai menggunakan derajat keanggotaan pada neuron output sebagai target pembelajaran. Parameter input yang digunakan adalah komponen warna hasil dari pengolahan citra yang mempunyai pengaruh terhadap tahap kematangan buah manggis dan tekstur. Hasil pemodelan FNN menjadi 2 kelas target klasifikasi (ekspor dan lokal) mendapatkan model terbaik dengan fitur penduga indeks warna merah, hijau, biru, value, a*, u*, v*, dan entropi dengan 5 neuron pada lapisan tersembunyi. Perbandingan persentase akurasi model FNN dan NN ialah 90:90, dengan perbandingan kemampuan pengenalan terhadap kelas ekspor dan lokal ialah 92:100 dan 89:75.Kata kunci: fuzzy neural network, klasifikasi, manggis, non-destructive grading, pengenalan pol

IDENTIFICATION OF MANGOSTEEN STAGE MATURITY ON COLOR BASED USING FUZZY NEURAL NETWORK

ABSTRACT   Fuzzy Neural Network (FNN) has a capability to classify a pattern located within two different classes where a classical Neural Network (NN) is failed to do so. The fuzzy pattern classification is using membership degree on output of neuron as learning target. The objective of this research was  to undertake non-destructive identification of fresh mangosteen stage maturity using Fuzzy Neural Network. Component of colour resulted in from image processing that influential against level of mangosteen’s maturity was used as input parameter. Percentage accuracy ratio of FNN model compare to NN for five, three, and two classification classes was 70:40, 86:65 and 90:90, respectively. The best result of FNN modeling was  achieved on  three class target classification (unripe, export and local) with green colour index, value, a* u*, v*, entropy, contrast, energy and homogeneity  as predicting  parameters and 15 neurons hidden layer. Comparisons of percentage capability of FNN against NN to identify the class were 100:0, 100:87, and 63:75. Keywords: classification, fuzzy neural network, mangosteen, non-destructive grading, pattern recognitio

IDENTIFICATION OF MANGOSTEEN STAGE MATURITY ON COLOR BASED USING FUZZY NEURAL NETWORK

ABSTRACT   Fuzzy Neural Network (FNN) has a capability to classify a pattern located within two different classes where a classical Neural Network (NN) is failed to do so. The fuzzy pattern classification is using membership degree on output of neuron as learning target. The objective of this research was  to undertake non-destructive identification of fresh mangosteen stage maturity using Fuzzy Neural Network. Component of colour resulted in from image processing that influential against level of mangosteen’s maturity was used as input parameter. Percentage accuracy ratio of FNN model compare to NN for five, three, and two classification classes was 70:40, 86:65 and 90:90, respectively. The best result of FNN modeling was  achieved on  three class target classification (unripe, export and local) with green colour index, value, a* u*, v*, entropy, contrast, energy and homogeneity  as predicting  parameters and 15 neurons hidden layer. Comparisons of percentage capability of FNN against NN to identify the class were 100:0, 100:87, and 63:75. Keywords: classification, fuzzy neural network, mangosteen, non-destructive grading, pattern recognitio

Three-term fuzzy back-propagation

The disadvantages of the fuzzy BP learning are its low speed of error convergence and the high possibility of trapping into local minima. In this paper, a fuzzy proportional factor is added to the fuzzy BP’s iteration scheme to enhance the convergence speed. The added factor makes the proposed method more dependant on the distance of actual outputs and desired ones. Thus in contrast with the conventional fuzzy BP, when the slop of error function is very close to zero, the algorithm does not necessarily return almost the same weights for the next iteration. According to the simulation’s results, the proposed method is superior to the fuzzy BP in terms of generated error