scholarly journals Klasifikasi Kakao Berbasis e-nose dengan Metode Neuro Fuzzy

2018 ◽  
Vol 8 (1) ◽  
pp. 49
Author(s):  
Ikhsan Nur Rahman ◽  
Danang Lelono ◽  
Kuwat Triyana
Keyword(s):  
Feature Extraction ◽  
Electronic Nose ◽  
Extraction Process ◽  
Differential Method ◽  
Test Results ◽  
Optimal Parameters ◽  
Accuracy Rate ◽  
Gas Sensor Array ◽  
Neuro Fuzzy

During this time to clasify quality of cacao based on color and aroma involving human taster. But this cacao tester still has weaknesses such as subjective. Besides that, the standard chemical analytical methods requires a high cost and need expertise to analyzing it. Basically aroma of cacao is determined by volatile compounds such aldehid and alcohol. Electronic nose based on unselected gas sensor array has the ability to analyze samples with complex compositions that can be known characteristics and qualitative analysis of the samples. Stimulus aroma is transformed by electronic nose into fingerprint data then it is used by feature extraction process using the differential method. The results of feature extraction is used to process the neuro fuzzy training to obtain optimal parameters. The parameters have been optimized is then tested on cacao. Based on test results, neuro fuzzy can clasify samples with 95,21% accuracy rate so that the clasification of cacao quality with electronic nose using neuro fuzzy has been successfully carried out.

scholarly journals Identifikasi Tahu Berformalin dengan Electronic Nose Menggunakan Jaringan Syaraf Tiruan Backpropagation

2016 ◽  
Vol 6 (2) ◽  
pp. 211
Author(s):  
Wida Astuti ◽  
Danang Lenono ◽  
Faizah Faizah
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Volatile Compounds ◽  
Electronic Nose ◽  
Back Propagation ◽  
Extraction Process ◽  
Back Propagation Neural Network ◽  
Differential Method ◽  
Gas Sensor Array ◽  
Network Training

During this time to identify pure and formalin tofu based on color and aroma involving human taster. But this tofu tester still has weaknesses such as subjective. Besides that, the standard chemical analytical methods requires a high cost and need expertise to analyzing it. Basically aroma of tofu is determined by volatile compounds such as heksanal, ethanol, and 1-hexanol, while aroma of formalin tofu is determined by volatile compounds such as OH, CO, and hydrocarbon. Electronic nose based on unselected gas sensor array has the ability to analyze samples with complex compositions that can be known characteristics and qualitative analysis of the samples. Stimulus aroma is transformed by electronic nose into fingerprint data then it is used by feature extraction process using the differential method. The results of feature extraction is used to process the back propagation neural network training to obtain optimal parameters. The parameters have been optimized is then tested on a random tofus. Based on test results, ANN-BP can identify samples with 100% accuracy rate so that the identification of a pure tofu and tofu formalin with electronic nose using back propagation neural network analysis has been successfully carried out.

scholarly journals Feature Extraction and Classification of Citrus Juice by Using an Enhanced L-KSVD on Data Obtained from Electronic Nose

Sensors ◽  
2019 ◽  
Vol 19 (4) ◽  
pp. 916 ◽  
Author(s):  
Wen Cao ◽  
Chunmei Liu ◽  
Pengfei Jia
Keyword(s):  
Feature Extraction ◽  
Kernel Function ◽  
Electronic Nose ◽  
Classification Accuracy ◽  
Extraction Methods ◽  
Object Function ◽  
Optimal Value ◽  
Processed Products

Aroma plays a significant role in the quality of citrus fruits and processed products. The detection and analysis of citrus volatiles can be measured by an electronic nose (E-nose); in this paper, an E-nose is employed to classify the juice which is stored for different days. Feature extraction and classification are two important requirements for an E-nose. During the training process, a classifier can optimize its own parameters to achieve a better classification accuracy but cannot decide its input data which is treated by feature extraction methods, so the classification result is not always ideal. Label consistent KSVD (L-KSVD) is a novel technique which can extract the feature and classify the data at the same time, and such an operation can improve the classification accuracy. We propose an enhanced L-KSVD called E-LCKSVD for E-nose in this paper. During E-LCKSVD, we introduce a kernel function to the traditional L-KSVD and present a new initialization technique of its dictionary; finally, the weighted coefficients of different parts of its object function is studied, and enhanced quantum-behaved particle swarm optimization (EQPSO) is employed to optimize these coefficients. During the experimental section, we firstly find the classification accuracy of KSVD, and L-KSVD is improved with the help of the kernel function; this can prove that their ability of dealing nonlinear data is improved. Then, we compare the results of different dictionary initialization techniques and prove our proposed method is better. Finally, we find the optimal value of the weighted coefficients of the object function of E-LCKSVD that can make E-nose reach a better performance.

scholarly journals Analisis Speaker Recognition Menggunakan Metode Dynamic Time Warping (DTW) Berbasis Matlab

AVITEC ◽  
2019 ◽  
Vol 1 (1) ◽  
Author(s):  
Noor Fita Indri Prayoga
Keyword(s):  
Feature Extraction ◽  
Speaker Recognition ◽  
Dynamic Time Warping ◽  
Recognition Accuracy ◽  
Recognition System ◽  
Extraction Process ◽  
Test Results ◽  
Time Warping ◽  
Dynamic Time ◽  
The Voice

Voice is one of  way to communicate and express yourself. Speaker recognition is a process carried out by a device to recognize the speaker through the voice. This study designed a speaker recognition system that was able to identify speakers based on what was said by using dynamic time warping (DTW) method based in matlab. To design a speaker recognition system begins with the process of reference data and test data. Both processes have the same process, which starts with sound recording, preprocessing, and feature extraction. In this system, the Fast Fourier Transform (FFT) method is used to extract the features. The results of the feature extraction process from the two data will be compared using the DTW method. Calculations using DTW that produce the smallest value will be determined as the output. The test results show that the system can identify the voice with the best level of recognition accuracy of 90%, and the average recognition accuracy of 80%. The results were obtained from 50 tests, carried out by 5 people consisting of 3 men and 2 women, each speaker said a predetermined word

scholarly journals Garber Filter Based Hybridized Algorithm For Feature Extraction From A Fuzzy Fingerprint Image

2019 ◽  
Vol 9 (1) ◽  
pp. 7251-7257
Keyword(s):  
Feature Extraction ◽  
Detection System ◽  
Extraction Process ◽  
Image Feature ◽  
Fingerprint Image ◽  
Image Feature Extraction ◽  
Image Preprocessing ◽  
Cn Algorithm ◽  
Fingerprint Detection

Proper extraction of fingerprint functions is important for matching the fingerprint algorithms. Different pieces of fingerprint information, such as rigid orientation and frequency should be taken into consideration for good results. The quality of a fingerprint image is often required to improve the function extraction process. In this article we introduce a Hybridized Garber Filter Algorithm (HGFA) for Fuzzy Fingerprint Image Feature Extraction for effective fingerprint recognition.This paper describes a fingerprint detection system consisting of image preprocessing, filtration, extraction and recognition matching.Preprocessing of images includes normalization based on median value and variation. In order to prepare the fingerprint image further processing, Gabor filters are extracted. The Poincaré index with a partitioning technique is used for the identification of a particular point. The extraction of the ridge line is shown and also the minute extraction with CN algorithm

scholarly journals IMPLEMENTASI JARINGAN SARAF TIRUAN DENGAN METODE BACKPROPAGATION TERHADAP BIBIT TANAMAN KARET

2018 ◽  
Vol 1 (1) ◽  
pp. 1
Author(s):  
Rahmad Fauzi S.Pd., M.Kom.
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Quality Standards ◽  
Seed Color ◽  
Test Results ◽  
Accuracy Rate ◽  
Rubber Seed ◽  
Rubber Plantations ◽  
Artificial Neural

Availability of seeds is one of the critical success factors of increasing the productivity of rubber plantations, the empirical use of seeds as one component technology has made a great contribution in increasing the productivity of rubber plantations. To obtain plant materials of good quality, it is worth noting the procurement process as well as the quality standards of seeds produced, if all quality standards at every election seedlings to be planted, it is certain that the results will be planted in accordance with what had been planned as long as it is balanced with proper maintenance based technical. Artificial Neural Networks can be used to obtain information about the quality of rubber seedlings by using Backpropagation, observations and measurements of rubber seed 51 seeds were used as a sample, of 50 rubber seed of the 35 samples used as training data and 16 samples as test data, observations done by looking at the characteristics of rubber seed color, reflectivity, results marinade, long beans, broad beans and thick seeds. From the results of the training conducted by Artificial Neural Networks as many as 35 sample data by using architecture patterns 6 15 1 obtained accuracy rate of 94.29%, which means that the artificial neural network has been able to identify the quality of the rubber plant seeds, to prove the results of the training testing using a sample of 16 pieces of new data that has not been trained before, the test results showed the accuracy rate of 100%, of the test results can be concluded that the application of Artificial Neural Networks to identify quality rubber seedlings with architectural 6 15 1 more accurate compared to other architectures

scholarly journals The Multi Attribute Utility Theory (Death) Method In The Decision Of The Distributor Distributor Selection (Metode Multi Attribute Utility Theory (Maut) Dalam Keputusan Pemilihan Distributor Barang)

KOMTEKINFO ◽  
2020 ◽  
Vol 7 (2) ◽  
pp. 84-100
Author(s):  
Ritna Wahyuni ◽  
Sarjon Defit ◽  
Gunadi Widi Nurcahyo
Keyword(s):  
Decision Support ◽  
Utility Theory ◽  
Convenience Store ◽  
Test Results ◽  
Accuracy Rate ◽  
Sales Process ◽  
Multi Attribute Utility Theory ◽  
The Right ◽  
Selection Of

Distributors are intermediaries who distribute products from factories to retailers. While the distributor of goods is the distributor of goods from factories to shops that need these goods. Incorrect selection of distributors can interfere with the sales process at the store. To improve the quality and quality of a store, it requires the best distributor of goods. This study aims to determine the best distributor of goods. The method used is the Multi Attribute Utility Theory (MAUT) of distributor data at the Padang Luar Sundanese Convenience Store. The data processed in this study consisted of a number of distributor data selected by the Multipurpose Store. From some of the distributor data, the Decision Support System is very necessary in the selection of distributors who aim for the selection of appropriate alternative decisions. The selection of distributors uses 15 samples of distributor data and 5 criteria data that are used as the basis for selecting distributors, namely quality of goods, affordable prices, strategic locations, service responses, and giving bonuses. The results of testing on this method obtained an accuracy rate of 86.67% of the right distributors and in accordance with the realization of the UI data. So this research is very suitable in choosing the best distributor. From the test results, it has got the 5 best distributors by assigning a weight of 11.50 to the best distributor, so the criteria set by the All-Round Shop can be used as a reference in the selection of distributors of goods.

scholarly journals IMPLEMENTASI METODE CHAIN CODE UNTUK PENGENALAN RAMBU LALU LINTAS

2016 ◽  
Vol 12 (1) ◽  
Author(s):  
Ryan Agustian ◽  
Nugroho Agus H. ◽  
Junius Karel
Keyword(s):  
Feature Extraction ◽  
Euclidean Distance ◽  
Extraction Process ◽  
Test Results ◽  
Traffic Sign ◽  
Distance Method ◽  
Traffic Signs ◽  
Chain Code ◽  
Image Objects

Traffic sign is needed to give information to users so they can be aware in roads. There are many types of traffic signs and each has many forms and different from each other so users sometimes have difficuty in recognizing traffic signs. In this research, the signs used are signs based on Peraturan Menteri Perhubungan Republik Indonesia Nomor PM 13 Tahun 2014. Modified Chain Code method was implemented for feature extraction process and Euclidean Distance method is used to calculating the similarity. Testing is done with 5 types of tests i.e. resize image, objects truncated, added a few objects to image, added many objects to image and noise spots. The test results showed the accuracy of the image of traffic signs to be recognized is 92.5%.

scholarly journals A Multi-Mode Reasoning For Predicting Tobacco Baking Quality

2021 ◽  
Author(s):  
QingE Wu ◽  
Tao Zong ◽  
Hu Chen ◽  
Lintao Zhou ◽  
Yingbo Lu ◽  
...  
Keyword(s):  
Prediction Model ◽  
Prediction Accuracy ◽  
Quality Prediction ◽  
Test Results ◽  
Accuracy Rate ◽  
Baking Quality ◽  
Tobacco Production ◽  
Output Mapping ◽  
High Prediction

Abstract In order to reduce the number of defective products caused by the unreasonable baking time during the tobacco production process, this paper proposes a method for establishing a multi-model reasoning tobacco baking quality prediction model. Conduct data mining and analysis on the data of various indicators of the original tobacco, and screen out the data that have an impact on the quality of tobacco baking. In order to reduce the complexity of the model and eliminate the influence between different dimensions, the data are carried out and standardized processing. Next, the normalized data is explored for the multi-input and multi-output mapping relationship. Finally, a mapping matrix is given for the multi-input and multi-output mapping relationship so as to establish a tobacco baking quality prediction model. The test results show that the predicted value of this model is basically the actual value, and the prediction accuracy rate is more than 90%. It has a high prediction accuracy rate. The cured tobacco leaves are basically the same as the actual cured yellow expected value. This model provides a practical guide method for tobacco baking, which has certain practical value in actual tobacco baking.

scholarly journals Application System for Identification of Surakarta Traditional Batik Images (SABATARA)

SinkrOn ◽  
2019 ◽  
Vol 4 (1) ◽  
pp. 5
Author(s):  
Jani Kusanti ◽  
Ramadhian Agus T.S
Keyword(s):  
Image Data ◽  
Training Data ◽  
Test Results ◽  
Ministry Of Education ◽  
Test Image ◽  
Accuracy Rate ◽  
Application System ◽  
Occurrence Matrix ◽  
Contrast Stretching

Surakarta Batik is a traditional cloth in Indonesia that has been designated as an intangible cultural heritage by the Ministry of Education and Culture. The Surakarta Batik Pattern has characteristics and has a story in each style. The method used affects the accuracy of each pattern in the Surakarta batik image. Image data used for training data are 100 image data with a size of 256 x 256 pixels, with test image data used as many as 20 image data. Improving the quality of the image using contrast stretching, the output is processed to separate objects with the background using adaptive thresholding. The obtained object is added by the canny process and calculated using the Gray Level Co-Occurrence Matrix to obtain the characteristics of each image. The characteristics used are four variables (energy, contrast, homogeneity, and correlation). The resulting variable is used as input to the classification using backpropagation. The test results obtained an accuracy rate of 95%, with an error rate of 0.05%.

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