scholarly journals Identification of Formaldehyde Bananas using Learning Vector Quantization

2021 ◽  
Vol 3 (2) ◽  
pp. 160
Author(s):  
Rahmat Musa ◽  
Mutaqin Akbar
Keyword(s):  
Vector Quantization ◽  
Visual Recognition ◽  
Texture Features ◽  
Learning Vector Quantization ◽  
Learning Rate ◽  
Training Data ◽  
Blue Color ◽  
Test Results ◽  
Accurate Identification ◽  
Sample Data

Bananas that ripen with chemical process or do not ripen naturally usually, this can be recognized by the presence of blackish patches on the surface of the skin. But visual recognition has its drawbacks, which is that it is difficult to recognize similarities between formalin bananas and natural bananas, resulting in a lack of accurate identification. In this study, a system was built that can determined formalin bananas and natural bananas through digital image identification using supervised classification. The image to be identification previously goes through the process of transforming RGB (Red Green Blue) color to Grayscale, and the process of extracting texture features using statically recognizable features through histograms, in the form of average, standard deviation, skewness, kurtosis, energy, entropy and smoothness. The extraction of texture features is classified with LVQ (Learning Vector Quantization) to determine formalin or natural bananas. The test was conducted with 122 banana imagery sample data, 100 imagery as training data consisting of 50 imagery for natural bananas and 50 imagery for bananas formalin, 22 imagery as test data. The test results showed LVQ method has the best percentage at Learning Rate 0.1, Decreased Learning Rate 0.75 and maximum epoch of 1000 with the smallest epoch of 7, obtained accuracy 90.90%, precision 84.61% and recall 100%.

scholarly journals Identification of Disease on Leaves Soybean using Modified Otsu and Learning Vector Quantization Neural Networks

Kursor ◽  
2018 ◽  
Vol 9 (3) ◽  
Author(s):  
Candra Dewi ◽  
Muhammad Sa’idul Umam ◽  
Imam Cholissodin
Keyword(s):  
Vector Quantization ◽  
Threshold Value ◽  
Learning Vector Quantization ◽  
Training Data ◽  
Blue Color ◽  
Phakopsora Pachyrhizi ◽  
Average Value ◽  
Maximum Value ◽  
Otsu Method ◽  
Soybean Leaf

Disease of the soybean crop is one of the obstacles to increase soybean production in Indonesia. Some of these diseases usually are found in the leaves and resulted to the crop become unhealthy. This study aims to identify disease on soybean leaf through leaves image by applying the Learning Vector Quantization (LVQ) algorithm. The identification begins with preprocessing using modified Otsu method to get part of the diseases on the leaves with a certain threshold value. The next process is to identify the type of disease using LVQ. This process uses the minimum value, the maximum value and the average value of the red, green and blue color of the image. The testing conducted in this study is to identify two diseases called Peronospora manshurica (Downy Mildew) and phakopsora pachyrhizi (Karat). The result of testing by using 60 training data and the value of all recommendations parameters obtained the highest accuracy of identification is 95% %, but the more stable accuracy is 90%. This result shows that the method perform quite well identification of two mentioned disease.

scholarly journals Cataract Disease Diagnosis System Using Artificial Neural Network Learning Vector Quantization Method

2020 ◽  
Vol 4 (2) ◽  
pp. 75-85
Author(s):  
Chrisani Waas ◽  
D. L. Rahakbauw ◽  
Yopi Andry Lesnussa
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Vector Quantization ◽  
Processing System ◽  
Disease Diagnosis ◽  
Learning Vector Quantization ◽  
Learning Rate ◽  
Training Data ◽  
Neural Network Learning ◽  
Artificial Neural

Artificial Neural Network (ANN) is an information processing system that has certain performance characteristics that are artificial representatives based on human neural networks. ANN method has been widely applied to help human performance, one of which is health. In this research, ANN will be used to diagnose cataracts, especially Congenital Cataracts, Juvenile Cataracts, Senile Cataracts and Traumatic Cataracts based on the symptoms of the disease. The ANN method used is the Learning Vector Quantization (LVQ) method. The data used in this research were 146 data taken from the medical record data of RSUD Dr. M. Haulussy, Ambon. The data consists of 109 data as training data and 37 data as testing data. By using learning rate (α) = 0.1, decrease in learning rate (dec α) = 0.0001 and maximum epoch (max epoch) = 5, the accuracy rate obtained is 100%.

scholarly journals Cervical Cell Classification using Learning Vector Quantization (LVQ) Based on Shape and Statistical Features

2019 ◽  
Vol 15 (02) ◽  
pp. 91
Author(s):  
Erlinda Metta Dewi ◽  
Endah Purwanti ◽  
Retna Apsari
Keyword(s):  
Vector Quantization ◽  
Optimal Parameter ◽  
Learning Vector Quantization ◽  
Learning Rate ◽  
Training Data ◽  
Statistical Features ◽  
Shape Features ◽  
Image Histogram ◽  
Cervical Cell ◽  
Cervical Cells

This research was conducted to design a system that is able to classify cervical cells into two classes, namely normal cells or abnormal cells. We use digital images of single cervical as research materials and Learning Vector Quantization (LVQ) as classification method.  Prior to classification, the nucleus areas of single cervical cell images were segmented and features were extracted. The features used in this study are 7 kinds of which consist of 2 types of feature, namely shape features and statistical features. The shape features used are area, perimeter, shape factor, and roundness of the nucleus, while the statistical features of the grayscale image histogram used are mean, standard deviation, and entropy. LVQ optimal parameter values based on the highest accuracy of training data, are learning rate 0.1 and learning rate reduction 0.5. The highest accuracy of system obtained from 45 testing data is 93.33%.

scholarly journals Perbandingan Metode Backpropagation dan Learning Vector Quantization (LVQ) Dalam Menggali Potensi Mahasiswa Baru di STMIK PalComTech

2019 ◽  
Vol 18 (2) ◽  
pp. 294-301
Author(s):  
Yarza Aprizal ◽  
Rabin Ibnu Zainal ◽  
Afriyudi Afriyudi
Keyword(s):  
Information Systems ◽  
Vector Quantization ◽  
Learning Vector Quantization ◽  
Learning Rate ◽  
Training Data ◽  
Rate Variation ◽  
New Students ◽  
Backpropagation Method ◽  
Input Variables ◽  
The Right

The Research aimst to compare backpropagation and Learning Vector Quantization (LVQ) methods in exploring the potential of new students at STMIK PalComTech. Comparisons in this study involve four input variables used which consist of four basic subjects of informatics engineering and information systems (math, basic programming, computer networks and management bases) which then make informatics techniques and information systems as outputs, to get the accuracy level high in this study, the researchers used several variations of parameters which eventually produced the best accuracy of the two methods. From 120 data tested using variations in test data and training data which are then processed using variations in the learning rate parameters and epochs. From the test results obtained the level of accuracy of pattern recognition in the backpropagation method is 99.17% with a learning rate variation of 0.1 and epoch 100, the learning vector quantization method has an accuracy rate of 96.67% with a variation of learning rate 1 and epoch 20 From the results of the comparison the Backpropagation method is superior in terms of accuracy so that it becomes the right method to use in exploring the potential of new students at STMIK PalComTech.

scholarly journals Learning Vector Quantization 3 (LVQ3) and Spatial Fuzzy C-Means (SFCM) for Beef and Pork Image Classification

2018 ◽  
Vol 1 (2) ◽  
pp. 60 ◽  
Author(s):  
Jasril Jasril ◽  
Suwanto Sanjaya
Keyword(s):  
Image Segmentation ◽  
Vector Quantization ◽  
Learning Rate ◽  
Training Data ◽  
Muslim Country ◽  
Test Results ◽  
Fuzzy C Means ◽  
Testing Data ◽  
Total Data

Base on some cases in Indonesia, meat sellers often mix beef and pork. Indonesia is a predominantly Muslim country. Pork is forbidden in Islam. In this research, the classification of beef and pork image was performed. Spatial Fuzzy C-Means is used for image segmentation. GLCM and HSV are used as a feature of segmentation results. LVQ3 is used as a method of classification. LVQ3 parameters tested were the variety of learning rate values and window values. The learning rate values used is 0.0001; 0.01; 0.1; 0.4; 0.7; 0.9 and the window values used is 0.0001; 0.4; 0.7. The training data used is 90% of the total data, and the testing data used is 10%. Maximum epoch used is 1000 iterations. Based on the test results, the highest accuracy was 91.67%.

scholarly journals Application of Learning Vector Quantization Method in Kinect Device AS a Base of the Development of Behaviour Detection System

2017 ◽  
Vol 1 ◽  
pp. 171-174
Author(s):  
Eko Arianto ◽  
Laifa Rahmawati
Keyword(s):  
Mental Disorder ◽  
Vector Quantization ◽  
Student Behavior ◽  
Detection System ◽  
Learning Vector Quantization ◽  
Learning Rate ◽  
Quantization Method ◽  
Special Schools ◽  
Natural Motion ◽  
Detection Technology

One of the lessons for mental disorder students in Special Schools is practicum lessons in the form of vocational education. This lesson uses equipment that requires prudence. Mental disorder students have characteristics that are low memory and move based on intuition. Teachers should pay extra attention especially to detect student behavior during the learning. This detection is needed for learning to take place smoothly and students are safe from the dangers around the practicum place. Teacher's feedback on the detection obtained in the form of a warning from the teacher. This study is expected to be useful for providing a special detection pattern for students to assist teachers by providing feedback in the form of warnings using natural motion detection technology. This research was conducted using Kinect as data input and data was processed using artificial neural network and Learning Vector Quantization method. The dangerous attitude used in the test is the attitude of standing at the time of drilling position. The data used by training is 126 data and do training using LVQ. At the LVQ training stage, the training was conducted with parameter of Learning Rate 0,05, maximum Iteration 44, reduction of learning rate 0.01, and Learning rate minimum 0,02.

scholarly journals Identifikasi Barcode pada Gambar yang Ditangkap Kamera Digital Menggunakan Metode JST

2013 ◽  
Vol 7 (2) ◽  
pp. 121
Author(s):  
Salman Aliaji ◽  
Agus Harjoko
Keyword(s):  
Vector Quantization ◽  
Digital Camera ◽  
Block Size ◽  
Modern Society ◽  
Learning Vector Quantization ◽  
Test Results ◽  
Training Process ◽  
Identification Process ◽  
Photo Identification ◽  
Time Required

AbstrakDewasa ini hampir setiap produk konsumen memiliki label barcode. Namun alat pembaca barcode jenis laser memiliki kelemahan karena tidak dapat mengenali barcode yang mengalami goresan atau noise. Namun telah dikembangkan teknik lain dengan memanfaatkan kamera digital untuk identifikasi barcode. JST telah banyak digunakan untuk identifikasi berbagai macam pola. Proses identifikasi barcode dalam JST terdiri dari proses training dan proses identifikasi. Proses training menggunakan metode LVQ (Learning Vector Quantization). Proses identifikasi terdiri dari beberapa tahap, yaitu akuisisi citra, preprocessing, locating barcode, proses pengujian dan verifikasi. Berdasarkan hasil pengujian metode LVQ dapat digunakan untuk identifikasi foto barcode dengan kinerja yang baik. Hasil pengujian menunjukkan tingkat akurasi sebesar 73,6 % dari 72 citra yang diuji dengan waktu rata-rata adalah 0.5 detik. Sementara waktu yang dibutuhkan untuk menemukan lokasi barcode adalah sekitar 6 detik menggunakan blok dengan ukuran 32x32 pixel. Kata kunci— Barcode, Learning Vector Quantization, Jaringan Syaraf Tiruan AbstrakIn today’s modern society, almost every consumer product has a barcode label. But the barcode reader with laser type has the disadvantage of not being able to recognize the barcode has a scratch or noise. However, other techniques have been developed by using a digital camera for barcode identification. ANN has been widely used for identification of various patterns. Barcode identification process consists of the ANN training process and the identification process. Training process using the LVQ (Learning Vector Quantization). Identification process consists of several stages: image acquisition, preprocessing, locating barcode, testing and verification process. Based on test results LVQ method can be used for photo identification barcode with good performance. The test results showed an accuracy of 73.6% rate of 72 images were tested with an average time is 0.5 seconds. While the time required to find the location of the barcode is about 6 seconds using a block size of 32x32 pixels. Keyword— Barcode, Learning Vector Quantization, Artificial Neural Network

scholarly journals PENERAPAN METODE LEARNING VECTOR QUANTIZATION UNTUK IDENTIFIKASI PENYAKIT PADI BERDASARKAN BENTUK BERCAK DAUN

2020 ◽  
Vol 6 (1) ◽  
pp. 28-35
Author(s):  
Ery Murniyasih ◽  
Luluk Suryani
Keyword(s):  
Vector Quantization ◽  
Error Rate ◽  
Learning Vector Quantization ◽  
Learning Rate ◽  
Rate Data ◽  
Target Error

Penelitian ini bertujuan : (1). Membuat suatu aplikasi untuk identifikasi jenis penyakit pada tanaman padi berdasarkan bentuk bercak daun padi.;(2). Menerapkan metode Learning Vector Quantization (LVQ) pada identifikasi penyakit tanaman padi. Pada tahapan learning dan testing pada LVQ citra diproses menjadi Grayscale, Thresholding, dan segmentasi. Di tahap pelatihan, metode LVQ digunakan untuk menentukan bobot, target error, max epoch, dan laju pelatihan (Learning rate). Data yang dijadikan sebagai input adalah citra identifikasi jenis penyakit tanaman padi berdasarkan bentuk bercak daun padi  yaitu dengan ukuran piksel 95x35 dan berekstensi BITMAP (.bmp). Standar keberhasilan sistem identifikasi ini adalah menghitung nilai Termination Error Rate dan tingkat keakuratan dalam identifikasi bentuk bercak daun. Dari simulasi ini diperoleh struktur Jaringan Syaraf Tiruan dengan jumlah nilai learning rate 0,02 dan jumlah epoch sebesar 5 kali. Sistem yang terbentuk mampu mengenali citra yang berisi bentuk bercak daun yang digunakan sebagai bobot dengan nilai keakuratan optimum yaitu 73,33% dengan komposisi penyakit bercak coklat (BC) 20 %, Blast  20 % dan cercak cercospora 33,33%.

scholarly journals Penerapan Metode Learning Vector Quantization2 (LVQ 2) Untuk Menentukan Gangguan Kehamilan Trimester I

2018 ◽  
Vol 15 (2) ◽  
pp. 144
Author(s):  
Elvia Budianita Budianita
Keyword(s):  
Vector Quantization ◽  
Learning Vector Quantization ◽  
Learning Rate

Trimester I adalah masa dimana 3 bulan pertama kehamilan yakni 0 sampai 12 minggu awal kehamilan. Pada masa ini tubuh ibu akan banyak mengalami perubahan seiring berkembangnya janin. Pada ibu-ibu hamil pada fase trimester I terkadang ditemukan beberapa gangguan kehamilan yaitu, Abortus, Anemia Kehamilan, Hiperemesis Gravidarum tingkat I, Hiperemesis Gravidarum tingkat II, Kehamilan Ektopik, dan Mola hidatidosa. Untuk membantu pasien dalam mengenali gangguan kehamilan pada trimester I ini maka peneliti berinisiatif merancang suatu sistem yang menerapkan konsep jaringan syaraf tiruan dengan metode LVQ 2 (Learning Vector Quantization) dalam mengenali gangguan kehamilan trimester I berdasarkan gejala gangguan kehamilan trimester I. Ada 41 gejala penyakit, dan 6 penyakit sebagai data masukan. Sistem akan mengklasifikasikan penyakit dengan proses pembelajaran dan pengujian ke dalam 6 jenis penyakit, berdasarkan pengujian metode LVQ2 cukup baik di terapkan dalam pengenalan pola gejala gangguan kehamilan, di buktikan dari hasil pengujian yang di lakukan menggunakan window 0.1, 0.3, 0.5, dan 0, data latih 90 dan data uji 18 didapat akurasi terbaik 100% dan rata-rata akurasi 97.68%  dengan nilai parameter pembelajaran algoritma learning rate = 0.02, 0.04, 0.06, pengurangan learning rate = 0.1, minimal learning rate = 0.01 dan nilai window (ε) =0.1, 0.3, 0.5, dan 0. Nilai w juga mempengaruhi akurasi. Kata Kunci:  Gangguan Kehamilan Trimester I, Learning Vector Quantization 2, Window

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