Learning Vector Quantization Method Research Articles

MEASUREMENT OF CLASSIFICATION PERFORMANCE WITH THE LEARNING VECTOR QUANTIZATION METHOD ON COVID-19 VACCINATION DATA AT THE PARUMPANAI HEALTH CENTER

In the midst of the COVID-19 pandemic, various countries are always trying their best to restore global stability. One effective way is the discovery of several vaccines to prevent transmission of the virus. Indonesia is one of the countries that is aggressively implementing the COVID-19 vaccination. The vaccination process which has been carried out from February 2021 until the end of 2021 has covered approximately 160 million people or 76.83% of the target set by the government. Vaccine recipients have criteria to be able to get vaccinated to avoid side effects or complications. So it is necessary to classify groups that can receive vaccines and also delay vaccination. This research aims to determine the performance of the learning vector quantization classification method. Learning vector quantization method classification produces 95% accuracy, 97% precision, and 96% sensitivity. From these performance measurements, it can be concluded that the learning vector quantization method is very good and can be used in the classification of COVID-19 vaccination recipients at the Parumpanai Public Health Center, East Luwu Regency.

Machine Learning Algorithms for Predicting Factitious Disorder Using the Learning Vector Quantization Method

Factitious disorder is classified as a mental problem because it is related to severe emotional disorders. A person who has factitious disorder intentionally produces symptoms of the disease for the purpose of receiving care and attention in a medical setting. People with Factitious disorder act with the aim of attracting the sympathy and attention of others. Diagnosing factitious disorder is very difficult. The reason is, the sufferer looks fine. The doctor must eliminate any physical and mental illness before confirming the diagnosis of Factitious disorder. Along with the development of machine learning technology. Incorporation of patient data and the use of machine learning technology can help detect the disease. The purpose of this study was to build a system to predict the likelihood of a person being exposed to factual or unrelated disorders with the inputs that the patient entered. The method for diagnosing factitious disorder uses the Learning Vector Quantization method whether a person is a sufferer or not. The data was obtained from the questionnaire using 14 parameters and managed to get data as much as 30 training data. This research resulted in a maximum epoch value of 1000, a learning rate value of 0.1, a learning rate multiplier of 0.1, a minimum learning rate of 0.0001 and a training data of 5. The accuracy result obtained is 70%.

Application of the Learning Vector Quantization Algorithm for Classification of Students with the Potential to Drop Out

Universities, as providers of academic education services, are required to provide an optimal educational process for students to produce a generation of quality human beings. Student learning success is seen as the success of universities in implementing the higher education process. One of the problems universities face in maintaining the quality of education is student dropout. The high dropout rate in universities can impact accreditation assessments. As a result, it will affect the level of public trust. The number of dropouts in higher education can be minimized from an early age by analyzing the factors that cause student dropouts using data on students who graduated and those who dropped out. This data can be used to determine student dropout patterns by classifying them using the artificial neural network learning vector quantization (LVQ) approach. The data used in this research was 4053, consisting of 3840 graduate student data and 213 dropout student data. This data is considered unbalanced, an unbalanced dataset can cause errors because the model tends to classify the majority class with a high classification and pays less attention to the minority class. So, it is necessary to apply oversampling techniques to overcome this problem. The research results show that the application of the LVQ method to unbalanced data produces an accuracy value of 95.53%, a precision value of 100%, a recall value of 15.02% and an f1-score of 0.26, while the application of the LVQ method to data that has undergone resampling resulting in an accuracy value of 94.66%, a precision value of 92.22%, a recall value of 97.55%, and an f1-score value of 0.95. The LVQ method can be used to classify dropout students with excellent results.

Measuring the Maturity Level of Oil Palm Fruit For CPO Production Based on Color With Using the LVQ Method

Palm oil is a very important commodity besides oil and gas which also has a fairly good export value. The palm oil produced must be supported by the quality standards set by SNI. The level of maturity when harvesting oil palm fruit greatly influences the quality of Crude Palm Oil (CPO) production, which is crude palm oil that has a reddish color obtained from extraction or from the pressing process of oil palm fruit flesh. In fact, in the field of oil palm fruit harvest, there are still many oil palm fruit that are not ripe enough and can even be said to be still raw, entering the CPO production process. Determination of the maturity level of oil palm fruit is generally determined based on the amount of loose fruit and color, so handling the harvest of oil palm fruit is an important activity in improving the quality of CPO. It is necessary to build a system capable of managing and processing palm fruit images to measure the maturity level of the palm fruit to be produced. To obtain the right level of accuracy, this research uses the Learning Vector Quantization (LVQ) method. LVQ is a method for conducting supervised competitive layer learning. From the results of trials conducted, it is proven that the system can measure very ripe oil palm fruit with HSV values (0.052209; 0.896021; 0.791114).

PERBANDINGAN METODE LVQ DAN BACKPROPAGATION UNTUK KLASIFIKASI STATUS GIZI ANAK DI KECAMATAN SANGKUP

The problem of children nutrition isi still a problem in various regions in Indonesia. Poor or poor nutrition of children is influenced by several factors, namely insufficient food intake and infectious diseases. Undernutrition or poor nutrition can be known from the nutritional status assessment obtained from classifying the nutrional status of children. Classification is a part of data mining that is often used to classify data based on certain data or variables. This study aims to compare the classification of the nutritional status of children using data mining with the learning vector quantization (LVQ) and backpropagation methods. Test were carried out using a comparasion ratio of training and testing data, namely 75% and 25%. From the research results, LVQ is superior with an accuracy of 95.12% and backpropagation of 80.49%.

Classification Appropriateness Recipient Help Non-Cash Food Using Learning Vector Quantization (LVQ) Method

Help Non-Cash Food is a program from the Government that is used to overcome poverty. The program is not functioning as well as it could because the procedure of receiving aid is not uniform, and individuals responsible for making choices are having trouble determining which families are qualified to receive the assistance. To overcome this problem, a classification system is needed to classify the eligibility of Non-Cash Food Assistance recipients so that the results are more efficient and accurate. This research uses the Learning Vector Quantization (LVQ) method with Python. This research aims to implement the LVQ method for the eligibility classification of non-cash food assistance recipients. System design is a stage that contains the process from start to finish of running this system which is described in the form of a flowchart, including system requirements that support this research, both software and hardware. In the process of analyzing the results and tests that are used as evaluation material in the process of finding a solution to a problem and making decisions in the process of planning activities, it is necessary to assess whether or not the LVQ approach is practicable to apply based on the findings of the research. In this study, 200 datasets were used with three epoch values and a learning rate of 0.1. The data set was randomly divided into a training portion of 80% and a testing portion of 20%. So that the results of this research using the LVQ method on the eligibility classification of recipients of Non-Cash Food Assistance obtain an accuracy of 97.5%.

Leaf Disease Detection

Abstract: Early disease identification is crucial for productive crop production in agriculture. illnesses such as bacterial spot, late blight, Septoria leaf spot, and yellow curved leaf the quality of the tomato harvest. Automatic classification techniques of plant diseases also assist in taking action once they are discovered diseased leaf symptoms Presented below is a Convolutional Learning Vector Quantization and Neural Network (CNN) model Method for detecting tomato leaf disease based on the (LVQ) algorithm and categorization. There are 500 tomato photos in the dataset. leaves that display four disease symptoms. We created a model of CNN for feature extraction and categorization automatically. Color Research on plant leaf diseases actively uses information. In our model, three channels based on RGB are subjected to filters.

Klasifikasi Jenis Mangga Berdasarkan Tekstur Tulang Daun Menggunakan Metode Learning Vector Quantization (LVQ)

Mango is a fruit plants that has the potential to be developed because it has a high level of genetic diversity. Mangoes vary in shape, size and color of the fruit, indicating a fairly wide genetic diversity. Of the many genetic diversity and types of society, there are still many who cannot distinguish them. This study builds an application to distinguish mango species based on the leaf bone structure where the feature extraction process uses the Learning Vector Quantization (LVQ) method. Then do the merging to produce a specific feature vector, then the classification calculation is carried out using the Euclidean Distance method to identify the type of mango fruit. The results of the study with the amount of training data as many as 6 images and testing data as much as 15 obtained system accuracy, with the calculation results, the remaining clusters are cluster 3 with a centroid value of R = 151.67 G = 145 and B = 153.33. From the test results with 2 scenarios, the mango golek type has low values, namely 50 and 60 because the mango golek type has less brightness than the other 2 types of mango.

Identification of Stroke with MRI Images Using the Learning Vector Quantization (LVQ) Method Based on Texture Features

Research on the Identification of Stroke with MRI Imagery Using the Learning Vector Quantization (LVQ) Method Based on Texture Features has been carried out. This study aims to determine the program's best parameters and the highest accuracy level of the stroke identification program. This research was conducted at Haji Sukolilo Hospital - Surabaya by obtaining 57 images of stroke patients and 15 images of regular patients. The study used the intelligence of stroke, tumor, and standard images to determine each category's image characteristics. After knowing the differences in each class, the next process is digital image processing, followed by feature extraction used is the Gray-Level Co-occurrence matrix (GLCM) with four parameters: contrast, correlation, diversity, and homogeneity. These four parameters are the best input parameters with an intelligence rate of 0.100 with a decrease in intelligence rate of 0.100, so the best accuracy value for training is 74.97%, and test data is 78.60%. Regarding the program's ability to correctly identify 11 data from 14 data tested, the program is feasible to be used as a second opinion.

Modeling and prediction of surface roughness at the drilling of SLM-Ti6Al4V parts manufactured with pre-hole with optimized ANN and ANFIS

In this study, modeling of the surface roughness (Ra) with artificial neural networks (ANN) in drilling the desired diameter of Ti6Al4V alloy produced by Selective Laser Melting (SLM) method is discussed. Drill type, cutting speed and feed were determined as evaluation parameters. Multilayer perceptron (MLP), Learning Vector Quantization (LVQ) and fuzzy-Mamdani methods were used in the study. In the estimation of Ra, a MAPE value of 0.206 % for MLP and 8 % for Mamdani was obtained. 100 % success was achieved in the classification made with LVQ. The ANN model accurately predicted Ra with excellent nonlinear mapping ability and an error of 4.9 %. It has been demonstrated that the presented models for drilling SLM-Ti6Al4V parts produced with unsupported front holes instead of drilling full holes can determine the appropriate drilling parameters for the desired Ra. The forecast results and presented models can be applied in drilling process planning for target industrial applications.

Development of Android-Based Speech Recognition Application Using Learning Vector Quantization Method in Optimizing Deaf Communication

Tunarungu merupakan keadaan seseorang yang memiliki kelainan pada fungsi pendengarannya. Kondisi ini bisa berlangsung hanya sementara maupun permanen. Ada dua jenis gangguan pendengaran yang membuat seseorang menjadi penyandang tunarungu, pertama tunarungu yang bersifat bawaan (sudah ada sejak lahir), kedua tunarungu yang terjadi setelah dilahirkan. Tunarungu yang bersifat bawaan kemungkinan besar disebabkan oleh suatu mutasi genetik, keturunan dari orang tua, maupun terpapar penyakit ketika masih di dalam kandungan. Sedangkan tunarungu yang sifatnya terjadi setelah lahir biasanya disebabkan oleh penerimaan gelombang suara yang keras dalam jangka panjang, usia, cedera, dan disebabkan oleh penyakit tertentu, seperti infeksi pada pendengaran. Komunikasi merupakan suatu kegiatan penyampaian pesan yang bertujuan terjalinnya hubungan antara komunikan dengan pemberi pesan. Permasalan yang muncul ketika bagaimana berkomunikasi dengan penyandang tunarungu, dimana komunikasi dilakukan dengan menggunakan bahasa isyarat. Sedangkan dalam penggunaan bahasa isyarat tidak semua orang bisa dan paham dalam pengunaannya. Maka peneliti mengembangkan aplikasi speech recognition dengan fitur speech to text untuk mengubah suara ke teks berbasis android dalam mempermudah berkomunikasi dengan para penyandang tunarungu, sehingga setiap orang dapat berkomunikasi walaupun tanpa memahami bahasa isyarat tertentu. Metode Learning Vector Quantization merupakan suatu metode untuk melakukan pembelajaran pada lapisan kompetitif yang terawasi, dimana metode LVQ digunakan untuk mengidentifikasi keakuratan dan ke murnian suara yang di tangkap. Pengambangan sistem tersebut menggunakan metode Rapid Application Development (RAD).

Application Of Learning Vector Quantization Algorithm In Educational Games Learning To Write Aksara Bali

Almost every region in the nusantara has its own regional script, one of which is in Bali the people know the Balinese script as a script commonly used by their ancestors in writing. In order to keep this Balinese script sustainable and not extinct, learning about this script is applied in schools in Balinese language subjects. In addition to applying learning about script in schools, it would be better if learning about Balinese script could be done in more interesting ways and concepts. One of them is by making an educational game for the introduction of Balinese characters. Which in this application the user will provide input in the form of Balinese script characters written on the application, which then the input will go through a preprocessing process then proceed with diagonal feature extraction, then the results of the feature extraction will go through a classification process using the Learning Vector Quantization method. . The result is a web-based application that can recognize Balinese script writing using the Learning Vector Quantization classification method with an accuracy rate of 58.6% and get a good response from each respondent who has tested the application.

Identifikasi Kualitas Kesegaran Susu Kambing Melalui Pengolahan Citra Digital Menggunakan Metode Learning Vector Quantization (LVQ)

Goat milk is milk produced by female goats after giving birth. Goat's milk contains many vitamins, minerals, electrolytes, chemical elements, enzymes, proteins, and fatty acids that are good for body health. The number of people's interest in goat's milk, makes goat's milk farmers to produce goat's milk in various ways for the sake of profit. For example, by reducing the level of purity and freshness of goat's milk by mixing other ingredients other than the original pure goat's milk. The identification process using imagery requires a method that can identify fresh and not fresh goat's milk. There are several methods that can be applied in digital image processing, one of which is using the Learning Vector Quantization (LVQ) method. LVQ is a single layer net with each input layer connected directly to the output neurons. Both are associated with a weight consisting of xi is the input, wii is the weight and yi is the output. Analysis of this calculation is used which becomes the initial value. Learning Rate (α) = 0.05, with a reduction of 0.1 * , and maximum epoch (MaxEpoch) = 1. The results of the analysis of the smallest distance on the 1st weight, so that the input image of the goat's milk test belongs to class 2. Thus, the image data of the goat's milk test is identified as mixed goat's milk.
 Keywords: Goat's Milk, Digital Image, Learning Vector Quantization

Identifikasi Kualitas Kesegaran Susu Kambing Melalui Pengolahan Citra Digital Menggunakan Metode Learning Vector Quantization (LVQ)

Goat milk is milk produced by female goats after giving birth. Goat's milk contains many vitamins, minerals, electrolytes, chemical elements, enzymes, proteins, and fatty acids that are good for body health. The number of people's interest in goat's milk, makes goat's milk farmers to produce goat's milk in various ways for the sake of profit. For example, by reducing the level of purity and freshness of goat's milk by mixing other ingredients other than the original pure goat's milk. The identification process using imagery requires a method that can identify fresh and not fresh goat's milk. There are several methods that can be applied in digital image processing, one of which is using the Learning Vector Quantization (LVQ) method. LVQ is a single layer net with each input layer connected directly to the output neurons. Both are associated with a weight consisting of xi is the input, wii is the weight and yi is the output. Analysis of this calculation is used which becomes the initial value. Learning Rate (α) = 0.05, with a reduction of 0.1 * , and maximum epoch (MaxEpoch) = 1. The results of the analysis of the smallest distance on the 1st weight, so that the input image of the goat's milk test belongs to class 2. Thus, the image data of the goat's milk test is identified as mixed goat's milk.
 Keywords: Goat's Milk, Digital Image, Learning Vector Quantization

Implementasi Learning Vector Quantization (LVQ) Dalam Mengidentifikasi Gula Aren Asli dengan Gula Aren Campuran

Palm sugar is one type of sugar that is often used by the community as a sweet taste for cooking, making food and drinks. Palm sugar is made from palm sap or juice from coconut trees, by boiling. To distinguish real and mixed palm sugar, by naked eye it is difficult to tell the difference. Moreover, many people do not understand or lack knowledge about the authenticity of palm sugar circulating in the market. So far, people who buy palm sugar only see the authenticity of palm sugar from its sweet taste or color. For this reason, it is necessary to identify using a digital image of the palm sugar, to determine the type of original palm sugar and mixed palm sugar. This is done so that the public gets information and knowledge so that they can be more observant and thorough in choosing and distinguishing palm sugar on the market by knowing the image characteristics of real palm sugar and mixed palm sugar. The Learning Vector Quantization (LVQ) method is a type of competitive-based network where from the output value given by the neurons in the output layer, only the winning neurons are considered. The winning neuron will undergo weight renewal. From the results of the analysis of calculations carried out with test data, the smallest distance data is obtained, namely at weight 1, so that the test image input on the palm sugar image is included in class 1 or original palm sugar. Thus, the palm sugar test image data is in accordance with the expected result data.
 Keywords : Palm Sugar, Digital Image Processing, Learning Vector Quantization

Implementasi Learning Vector Quantization (LVQ) Dalam Mengidentifikasi Gula Aren Asli dengan Gula Aren Campuran

Palm sugar is one type of sugar that is often used by the community as a sweet taste for cooking, making food and drinks. Palm sugar is made from palm sap or juice from coconut trees, by boiling. To distinguish real and mixed palm sugar, by naked eye it is difficult to tell the difference. Moreover, many people do not understand or lack knowledge about the authenticity of palm sugar circulating in the market. So far, people who buy palm sugar only see the authenticity of palm sugar from its sweet taste or color. For this reason, it is necessary to identify using a digital image of the palm sugar, to determine the type of original palm sugar and mixed palm sugar. This is done so that the public gets information and knowledge so that they can be more observant and thorough in choosing and distinguishing palm sugar on the market by knowing the image characteristics of real palm sugar and mixed palm sugar. The Learning Vector Quantization (LVQ) method is a type of competitive-based network where from the output value given by the neurons in the output layer, only the winning neurons are considered. The winning neuron will undergo weight renewal. From the results of the analysis of calculations carried out with test data, the smallest distance data is obtained, namely at weight 1, so that the test image input on the palm sugar image is included in class 1 or original palm sugar. Thus, the palm sugar test image data is in accordance with the expected result data.
 Keywords : Palm Sugar, Digital Image Processing, Learning Vector Quantization

MEMBANDINGAN METODE JARINGAN SYARAF TIRUAN BACKPROPAGATION DAN LEARNING VECTOR QUANTIZATION DENGAN OPENCV PADA PENGENALAN WAJAH

Face recognition is an area that is still being researched and improved for various purposes such as attendance, population data collection, security systems and others. Two methods that are often used for face recognition applications are artificial intelligence methods, especially back-propagation neural networks (ANN) and learning vector quantization. Both of these techniques are directed learning techniques that are widely used to identify distinctive patterns, namely grouping patterns into groups of patterns, making them ideal for use in facial recognition applications. In this application, preprocessing of the input image includes the detection process of scaling, grayscale, edged with the sobel and threshold methods, carried out before the image is processed in ANN. Meanwhile, the ANN approach used to identify faces involves the Backpropagation method and the Learning Vector Quantization method. The findings of this analysis are a comparison of the backpropagation neural network method and quantization of the learning vectors of face recognition used to assess variations, limitations, strengths and optimal results of the two techniques for use in facial recognition systems.
 

Identification of male and female nutmeg seeds based on the shape and texture features of leaf images using the learning vector quantization (LVQ)

One of the stages in the process of planting nutmeg seedlings on agricultural land is the identification of the sex of male and female nutmeg seedlings. The process of identifying nutmeg seedlings still uses manual methods, while the method is often inaccurate and inefficient. There fore, this research discusses the methods and results of identification of male and female nutmeg seedling leaves based on the combined feature extraction and leaf texture using learning vector quantization (LVQ). The form feature extraction method used consists of slimness, roundness, narrow factor, perimeter ratio and diameter, perimeter ratio at length while for texture is contrast, correlation, energy, and homogeneity. The results of the combined extraction of shape and texture features were applied as input vectors when classification using the LVQ method. Whereas LVQ aims at classification based on extraction results, by initializing LVQ parameters, namely learning rate and epoch. The change in the learning rate value on LVQ is very influential to get the percentage of the correctness of the data, the learning rate value must be between 0 to 1. The accuracy of LVQ predictions obtained in this study is 92.30%, with the best learning rate values from 0.001 to 0.09 and epoch 50.

Weather Forecast using Learning Vector Quantization Methods

Weather forecasting is one of the important factors in daily life, as it can affect the activities carried out by the community. The study was conducted to optimize weather forecasts using artificial neural network methods. The artificial neural network used is a learning vector quantization (LVQ) method, in which artificial neural networks based on previous research are suitable for prediction. The research is modeling weather forecast optimization using the LVQ method. Models with the best accuracy can be used in terms of weather forecasts. Based on the results of the training that has been done in this study produces the best accuracy on the LVQ method which is to produce 72%.

Identification of Formaldehyde Bananas using Learning Vector Quantization

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%.