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 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%.

Klasifikasi Gerakan Jatuh Berbasis Accelerometer dan Gyroscope Menggunakan K-Nearest Neighbors

2020 ◽  
Vol 4 (2) ◽  
pp. 24-29
Author(s):  
Adlian Jefiza ◽  
Indra Daulay ◽  
Jhon Hericson Purba
Keyword(s):  
Real Time ◽  
Root Mean Square ◽  
Nearest Neighbors ◽  
Training Data ◽  
Mean Square ◽  
K Nearest Neighbors ◽  
Testing Data ◽  
Total Data

Permasalahan utama pada penelitian ini merujuk kepada semakin menurunnya daya tahan tubuh lanjut usia (lansia). Hal ini membutuhkan sistem monitoring aktivitas lansia secara real time. Untuk mendeteksi kegiatan para lansia, dirancang sebuah perangkat monitoring dengan accelerometer 3-sumbu dan gyroscope 3-sumbu. Data sensor diperoleh dari lima partisipan. Setiap partisipan melakukan lima gerakan yaitu terjatuh, duduk, tidur, rukuk dan sujud. Gerakan yang dipilih adalah gerakan yang menyerupai gerakan jatuh. Total data yang diperoleh dari partisipan adalah 75 data yang terbagi menjadi training data dan testing data. Penelitian ini menggunakan metode transformasi Wavelet untuk mengenali fitur dari gerakan. Untuk pengklasifikasian setiap gerakan, digunakan metode K-nearest neighbors (KNN). Hasil klasifikasi gerakan menggunakan lima kelas menghasilkan nilai root mean square sebesar 0.0074 dengan akurasi 100%.

scholarly journals Analysis of Personality Characteristic Using the Naïve Bayess Classifier Algorithm (Case Study Official Twitter of Basuki Tjahaja Purnama's and Anies Baswedan)

2019 ◽  
Vol 7 (2) ◽  
pp. 14
Author(s):  
Ireicca Agustiorini Harsehanto ◽  
M. Didik R. Wahyudi
Keyword(s):  
Social Media ◽  
Big Five ◽  
Personality Characteristic ◽  
Training Data ◽  
Big Five Personality ◽  
Personality Theory ◽  
Test Results ◽  
Bayes Classifier ◽  
Testing Data

Abstract - This research uses data from social media Twitter based on the results of tweets from user_timeline @basuki_btp and @aniesbaswedan. This study uses 2100 tweet data. Data that has been collected is then pre-processed first and labeled manually. The next process is classification using the Naïve Bayess Classifier Algorithm using the Big Five Personality Theory. Based on the test results using 500 tweet data as training data and 1600 tweet data as testing data. The classification results obtained by using the Naïve Bayes Classifier Method and grouped in the "Big Five" personality groups: Openness, Conscientiousness, Extraversion, Agreeableness and Neuroticism on tweet data in Indonesian.

scholarly journals Sistem Pengelompokan Siswa Berdasarkan Tingkat Kedisiplinan Menggunakan Metode Naïve Bayes Classifier

2021 ◽  
Vol 9 (2) ◽  
pp. 50
Author(s):  
Budi Hartanto ◽  
Sri Tomo
Keyword(s):  
Test Data ◽  
Educational Process ◽  
Training Data ◽  
Student Discipline ◽  
Test Results ◽  
Bayes Classifier ◽  
Bayes Method ◽  
Important Thing ◽  
Class Test

Discipline is a very important thing in the educational process. Discipline will succeed if it is applied to students correctly. Student discipline is that every student follows every rule and order that has been set by the school. At SMK Muhammadiyah 2 Sukoharjo student discipline. Declining discipline at SMK Muhammadiah 2 Sukoharjo is marked by the increase in points of violation from students. The purpose of this study was to apply the nave Bayes method in the classification of student discipline levels at SMK Muhammadiyah 2 Sukoharjo. With this information will be obtained that can be used for information on which students need to be given Counseling Guidance to provide direction and guidance to students. The attributes used are cases of fights, not attending apples, not carrying out picket, not entering without explanation, arriving late, noisy in class. Test results with 490 records with a portion of 75% training data and 25% test data. And produces an accuracy of 76%.

scholarly journals Klasifikasi Multi Output pada Harga Smartphone Menggunakan Learning Vector Quantization (LVQ) dan Backpropagation (BP)

2021 ◽  
Vol 6 (2) ◽  
pp. 14-19
Author(s):  
Dinita Rahmalia ◽  
Mohammad Syaiful Pradana ◽  
Teguh Herlambang
Keyword(s):  
Vector Quantization ◽  
Error Rate ◽  
Gradient Descent ◽  
Euclidean Distance ◽  
Learning Vector Quantization ◽  
Training Data ◽  
Price Range ◽  
Testing Data ◽  
Internal Storage ◽  
Simulation Results

There are many smartphones with various price sold in market. The price of smartphone is affected by some components such as weight, internal storage, memory (RAM), rear camera, front camera and brands. There are two methods for classifying price class of smartphone in market such as Learning Vector Quantization (LVQ) and Backpropagation (BP). From classifying price class of smartphone in market using LVQ and BP, there are the differences on the both of them. LVQ classifies price range of smartphone by euclidean distance of weight and data on its iteration. BP classifies price range of smartphone by gradient descent of target and output on its iteration. In multi output classification, one object may have multi output. Based on simulation results, BP gives the better accuracy and error rate in training data and testing data than LVQ.  

scholarly journals Stamping Tool Conditions Diagnosis: A Deep Metric Learning Approach

2021 ◽  
Vol 11 (15) ◽  
pp. 6959
Author(s):  
Zaky Dzulfikri ◽  
Pin-Wei Su ◽  
Chih-Yung Huang
Keyword(s):  
Neural Network ◽  
Metric Learning ◽  
Training Data ◽  
Probability Method ◽  
Learning Approach ◽  
Test Results ◽  
Complex Signals ◽  
Intelligent Monitoring ◽  
Deep Metric Learning

Stamping processes remain crucial in manufacturing processes; therefore, diagnosing the condition of stamping tools is critical. One of the challenges in diagnosing stamping tool conditions is that traditionally, the tools need to be visually checked, and the production processes thus need to be halted. With the development of Industry 4.0, intelligent monitoring systems have been developed by using accelerometers and algorithms to diagnose the wear classification of stamping tools. Although several deep learning models such as the convolutional neural network (CNN), auto encoder (AE), and recurrent neural network (RNN) models have demonstrated promising results for classifying complex signals including accelerometer signals, the practicality of those methods are restricted due to the flexibility of adding new classes and low accuracy when faced to low numbers of samples per class. In this study, we applied deep metric learning (DML) methods to overcome these problems. DML involves extracting meaningful features using feature extraction modules to map inputs into embedding features. We compared the probability method, the contrastive method, and a triplet network to determine which method was most suitable for our case. The experimental results revealed that, compared with other models, a triplet network can be more effectively trained with limited training data. The triplet network demonstrated the best test results of the compared methods in the noised test data. Finally, when tested using unseen class, the triplet network and the probability method demonstrated similar results.

scholarly journals KLASIFIKASI CITRA DIGITAL BUMBU DAN REMPAH DENGAN ALGORITMA CONVOLUTIONAL NEURAL NETWORK (CNN)

2020 ◽  
Vol 9 (3) ◽  
pp. 273-282
Author(s):  
Isna Wulandari ◽  
Hasbi Yasin ◽  
Tatik Widiharih
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Activation Function ◽  
Training Data ◽  
Young Generation ◽  
Testing Data ◽  
Filter Size ◽  
Hidden Layer ◽  
Herbs And Spices

The recognition of herbs and spices among young generation is still low. Based on research in SMK 9 Bandung, showed that there are 47% of students that did not recognize herbs and spices. The method that can be used to overcome this problem is automatic digital sorting of herbs and spices using Convolutional Neural Network (CNN) algorithm. In this study, there are 300 images of herbs and spices that will be classified into 3 categories. It’s ginseng, ginger and galangal. Data in each category is divided into two, training data and testing data with a ratio of 80%: 20%. CNN model used in classification of digital images of herbs and spices is a model with 2 convolutional layers, where the first convolutional layer has 10 filters and the second convolutional layer has 20 filters. Each filter has a kernel matrix with a size of 3x3. The filter size at the pooling layer is 3x3 and the number of neurons in the hidden layer is 10. The activation function at the convolutional layer and hidden layer is tanh, and the activation function at the output layer is softmax. In this model, the accuracy of training data is 0.9875 and the loss value is 0.0769. The accuracy of testing data is 0.85 and the loss value is 0.4773. Meanwhile, testing new data with 3 images for each category produces an accuracy of 88.89%. Keywords: image classification, herbs and spices, CNN. 

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 Classification of Bird Based on Face Types Using Gray Level Co-Occurrence Matrix (GLCM) Feature Extraction Based on the k-Nearest Neighbor (K-NN) Algorithm

2021 ◽  
Vol 6 (2) ◽  
pp. 111-119
Author(s):  
Daurat Sinaga ◽  
Feri Agustina ◽  
Noor Ageng Setiyanto ◽  
Suprayogi Suprayogi ◽  
Cahaya Jatmoko
Keyword(s):  
Feature Extraction ◽  
Nearest Neighbor ◽  
Correlation Energy ◽  
Training Data ◽  
Gray Level ◽  
K Nearest Neighbor ◽  
Testing Data ◽  
Occurrence Matrix

Indonesia is one of the countries with a large number of fauna wealth. Various types of fauna that exist are scattered throughout Indonesia. One type of fauna that is owned is a type of bird animal. Birds are often bred as pets because of their characteristic facial voice and body features. In this study, using the Gray Level Co-Occurrence Matrix (GLCM) based on the k-Nearest Neighbor (K-NN) algorithm. The data used in this study were 66 images which were divided into two, namely 55 training data and 11 testing data. The calculation of the feature value used in this study is based on the value of the GLCM feature extraction such as: contrast, correlation, energy, homogeneity and entropy which will later be calculated using the k-Nearest Neighbor (K-NN) algorithm and Eucliden Distance. From the results of the classification process using k-Nearest Neighbor (K-NN), it is found that the highest accuracy results lie at the value of K = 1 and at an degree of 0 ° of 54.54%.

scholarly journals PREDIKSI PENYAKIT TUBERCULOSIS PARU (TB PARU) MENGGUNAKAN METODE LEARNING VEKTOR QUANTIZATION (LVQ)

2018 ◽  
Vol 15 (1) ◽  
pp. 20-27
Author(s):  
A W Rahmadani ◽  
A I Jaya ◽  
N Nacong
Keyword(s):  
Pulmonary Disease ◽  
Vector Quantization ◽  
Medical Records ◽  
Disease Risk ◽  
Health Centers ◽  
Training Data ◽  
Shortness Of Breath ◽  
Pulmonary Tb ◽  
Testing Data ◽  
Weight Classification

Tuberculosis pulmonary (TB pulmonary) is a contagious disease that attacks the lungs that can spread through the air when a person active TB cough, sneeze or talk. This study aims to predict Tuberculosis pulmonary disease  using Learning Vector quantization based on data from the medical records of the health centers kamonji, Palu city. The study was conducted using 8 TB pulmonary disease risk factors which are age, gender, fever, long cough, cough, chest pain, shortness of breath, and decreased body weight. Classification is done by using 100 data consisting of 80 training data and 20 testing data. Results of the study showed that tested all the data correctly with rank of accuracy is 100%.

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