K-nearest Neighbor Classification Method Research Articles

Comparison of K-Nearest Neighbor Classification Methods and Support Vector Machine in Predicting Students’ Study Period.

Comparison of K-Nearest Neighbor Classification Methods and Support Vector Machine in Predicting Students’ Study Period.

Comparison of Naive Bayes, K-Nearest Neighbor, and Support Vector Machine Classification Methods in Semi-Supervised Learning for Sentiment Analysis of Kereta Cepat Jakarta Bandung (KCJB)

Transportation technology has developed very rapidly in the 21st century; one of them is high-speed trains. Currently, the Indonesian government is implementing the construction of the Kereta Cepat Jakarta-Bandung (KCJB) project in collaboration with China. The construction of this fast train project has attracted various comments and opinions from the public on Twitter and social media. This research aims to compare the classification methods of Naïve Bayes, K-Nearest Neighbor (K-NN), and Support Vector Machine (SVM) in classifying sentiment in tweets about high-speed trains obtained by scraping Twitter. The comparison process was carried out using semi-supervised learning, and the results showed that the semi-supervised SVM model had the best performance with an average accuracy of 86%, followed by the semi-supervised Naïve Bayes model and semi-supervised K-NN with an average accuracy of 81% and 58% respectively. Overall, the prediction results from the three models conclude that there are more tweets with negative sentiment than tweets with positive and neutral sentiment.

Feature Selection using Information Gain on the K-Nearest Neighbor (KNN) and Modified K-Nearest Neighbor (MKNN) Methods for Chronic Kidney Disease Classification

Purpose: Kidneys has an important role in the human excretory system. Unhealthy kidneys can affect kidney function. It is important to know the symptoms of chronic kidney disease. One data mining technique that can be applied is the classification technique to determine whether a person has chronic kidney disease or not based on the symptoms (attributes) obtained from medical records. The symptoms of chronic kidney disease obtained amount to 24 symptoms or attributes,Methods/Study design/approach: In this research, the classification of chronic kidney disease is performed using the information gain feature selection method and the KNN and MKNN classification methods. The number of data used is 400 data with 2 classes, namely chronic kidney disease (CKD) and non-chronic kidney disease (non-CKD).Result/Findings: Based on the test results, it was found that the hemo (Hemoglobin) attribute has the highest information gain value, which is 0.6255. The best accuracy for the KNN classification method is 96.61%, and for the MKNN method, it is 98%. Novelty/Originality/Value: The purpose of information gain feature selection is to choose features or attributes that significantly influence chronic kidney disease. Keywords: Chronic Kidney Disease, Information Gain, KNN, MKNN

Style linear k-nearest neighbor classification method

Most studies on linear k-nearest neighbor methods assume independent and identically distributed training and testing samples. However, this is not the case in many practical applications, particularly when each class shares a homogeneous style. We propose a novel style linear k-nearest neighbor method to achieve two goals: (a) the linear k-nearest neighbor method should mine the stylistic data for explicit or implicit stylistic features while obtaining linear expressions and effectively transfer them to the predictor through matrix expressions; (b) the similarity of stylistic features between testing and training samples should be numerically quantified to enhance the generalizability of the predictor. To this end, firstly we introduce style matrices to express the style information of each class. In order to prevent the style matrices from degenerating into identity matrices, we introduce enhanced nodes to separate the manifold structure of the original data. The dual representation of matrix and enhanced nodes makes it easy to extract stylistic features from data. Furthermore, we introduce the style membership vector for the first time in the linear k-nearest neighbor method to calculate the style similarity of testing samples to each class, and determine the labels of testing samples easily and accurately through the style membership vector. We also propose an implementable alternating optimization strategy for the proposed method, which decomposes the complex optimization problem into independent subproblems for easy implementation. The experimental results demonstrate that the proposed method keeps comparable generalization capability on the total 9 ordinary datasets. And it outperforms the comparative methods, and even achieving 9.76 % improvement of average testing accuracy over the state-of-the-art weighted locally linear k-nearest neighbor method on the total 6 stylistic datasets.

Comparison Of K-Nearest Neighbor And CNN Classification Methods In Diabetic Data Sets

The number of diabetics worldwide is projected to increase by 204 million (48%), from 425 million in 2017 to 629 million in 2045. Indonesia ranks sixth out of ten countries with the most number of diabetics in the world or 10 million people. The majority of people with diabetes are between 20 and 64 years old, or 327 million people, compared to 123 million people between 65 and 99 years old. The incidence of diabetes increases by about 4.8% at the age of 55-64 years, and women (1.7%) suffer from diabetes more than men (1.4%). Therefore, the authors will create a program to determine the patient's diabetes. One approach is to use machine learning as a data mining classification technique. The author will do a classification comparison with the two methods, namely the KNN and CNN methods to provide the best results of the two methods for testing. So that the accuracy of the data from the diagnosis and photo images of the disease can be known to provide early treatment before the severity of the disease.

The Implementation of Color Feature Extraction and Gray Level Co-occurrence Matrix Combination in K-Nearest Neighbor Classification Method for Tomato Leaf Disease Identification

Purpose: Tomato plants are quite important commodities in Indonesia. With a complete and good content of substances, tomatoes become a product that is widely consumed by the public. However, much of the decline in crop production is caused by plant disruptive organisms such as viruses and bacteria. Early identification of plant diseases is expected to prevent the spread of diseases caused by these organisms.Design/methodology/approach: In this study the data used in machine training are data from kaggle sites. This study uses the K-Nearest Neighbor classification method with a combination method of extracting feature on RGB, HSV and GLCM images to obtain the best accuracy value.Findings/Results: Based on the test results among the combination methods of feature extraction in the process of identifying tomato leaf diseases which are classified into 7, namely testing units of RGB, HSV, GLCM followed by a combination of RGB HSV, RGB GLCM, HSV GLCM, and RGB HSV GLCM methods obtained a comparison value of 71.5%, 72.9%, 79%, 82.5%, 90.6%, 87.4% and 87.7%. Based on these data, it was concluded that with the combination of the RGB GLCM method obtained the best accuracy value in the identification of tomato leaf disease with an accuracy rate of 90.6%.Originality/value/state of the art: The use of the K-Nearest Neighbor classification method in this study combines the collection of selected characteristics so as to get a comparison of 7 combination groups between RGB, HSV, and GLCM.

Identifikasi Tingkat Kematangan Buah Pinang Menggunakan K-Nearest Neighbor Berdasarkan Fitur Tekstur dan Warna

This research builds a system for identifying the maturity level of areca fruit based on digital image processing using texture and color features through the Gray Level Co-Occurrence Matrix (GLCM) and Color moments. The initial stage of the research is image pre-processing so that it can be processed to the next stage, namely feature extraction. Texture feature extraction was performed using the Gray Level Co-Occurrence Matrix (GLCM), namely the correlation value and color feature extraction using Color moments, the mean value used in this study. Classification is done based on the features that have been extracted before. This study uses the K-Nearest Neighbor (KNN) classification method. Tests were carried out to determine the parameters that cause changes in the classification results with scenarios including determining the number of Neighbors in KNN. By using 1 Neighbors in the KNN classifier, the best accuracy is 86.36% in the process of identifying the maturity level of areca fruit.

Garlic Field Classification Using Machine Learning and Statistic Approaches

The level of garlic consumption in Indonesia increases as the population grows. This is because most of the ingredients of Indonesian food recipes contain garlic. However, local garlic production is not sufficient to fulfil the demand. Therefore, the Indonesian government imported garlic from other countries to fulfil the demand. To reduce the import capacity of garlic, the government made a regulation to increase the potential area for garlic cultivation in several priority locations in Indonesia, one of which is Sembalun District, East Lombok. To support government regulation, this study presents an application of machine learning and a statistic approach for the garlic field mapping method in Sembalun, Indonesia. This study comprises several steps including the Sentinel-1A images data acquisition, image preprocessing, machine learning and statistic model training, and model evaluation. k-nearest neighbor (k-NN) and maximum likelihood classification (MLC) methods are selected in this study. The performance of k-NN and MLC are compared to other garlic field classification results developed in previous studies using pixel-based and image-based classifications. The comparison results show that the k-NN classification is slightly better than the SVM classification and also that it outperformed the MLC method. In addition, MLC works faster than k-NN in learning the dataset and testing the models. The classification results can be used to estimate garlic production in the study area. The study concludes that the proposed methods are better than other classification models and the statistic approach. The future study will improve dataset quality to increase the model’s accuracy.

Analyze Important Features of PIMA Indian Database For Diabetes Prediction Using KNN

Diabetes is a chronic, non-communicable disease, and a long-term health condition that affects how the body uses glucose, the type of sugar that gives energy. In Indonesia, diabetes ranks as the sixth highest cause of death, following conditions related to childbirth. In 2021, Indonesia has a total of 19.5 million diabetes patients, making it the fifth-highest in the world. Some machine learning research has used data from the PIDD (PIMA Indian Diabetes Dataset) to predict diabetes. In this research, in addition to prediction accuracy, data complexity is also important. This research analyzes important features in the PIMA Indian database using the KNN (k-nearest neighbor) method for classification. The results show that using KNN with k=22 value results in the highest accuracy of 83.12%. The analysis also found that the important features required by the KNN method to achieve high accuracy from the PIMA Indian database, in order of importance, are glucose, age, insulin, blood pressure, Body Mass Index, pregnancy, skin thickness, and diabetes pedigree function. However, when used in the KNN classification method, the diabetes pedigree function feature was found to be unnecessary, not relevant, and can be reduced.

An Investigation of Data Mining Classification Methods in Classifying Students According to 2018 PISA Reading Scores

The purpose of this research was to determine classification accuracy of the factors affecting the success of students' reading skills based on PISA 2018 data by using Artificial Neural Networks, Decision Trees, K-Nearest Neighbor, and Naive Bayes data mining classification methods and to examine the general characteristics of success groups. In the research, 6890 student surveys of PISA 2018 were used. Firstly, missing data were examined and completed. Secondly, 24 index variables thought to affect the success of students' reading skills were determined by examining the related literature, PISA 2018 Technical Report, and PISA 2018 data. Thirdly, considering the sub-classification problem, the students were scaled in two categories as “Successful” and “Unsuccessful” according to the scores of PISA 2018 reading skills achievement test. Statistical analysis was conducted with SPSS MODELER program. At the end of the research, it was determined that Decision Trees C5.0 algorithm had the highest classification rate with 89.6%, the QUEST algorithm had the lowest classification rate with 75%, and four clusters were obtained proportionally close to each other in Two-Step Clustering analysis method to examine the general characteristics according to the success scores. It can be said that the data sets are suitable for clustering since the Silhouette Coefficient, which is calculated as 0.1 in clustering analyses, is greater than 0. It can be concluded that according to achievement scores, all data mining methods can be used to classify students since these models make accurate classification beyond chance.

Cancer Classification Based on the Features of Itemset Sequence Pattern of TP53 Protein Code Using Deep Miden - KNN

Cancer is a disease that is still difficult to identify up to today. One of the causes of cancer is genetic modification that because of mutations in p53 gene. Healthy cells have a p53 wild type protein (normal) that is able to manage DNA separation. If DNA mutates, it will be difficult to detect cancer because the composition of the protein has changed. Bioinformatics is a combination of biology and information engineering (TI) that is utilized to manage data. One of the applications of data mining in bioinformatics is the development of pharmaceutical and medical industries. Data mining classification can use variety of methods including K-Nearest Neighbor (KNN), C45, ID3, and several other methods. One of the most reliable data classification methods is KNN. In this study, the development used two algorithms. The first was with the modification of the k-fold method, which divided two data into training data and test data, in which test-1 data and test-2 data were made into slices. The second was by a method for selecting an itemset sequence pattern that had the largest Gain Information, either 2 itemsets, 3 itemsets, and so on (Deep Miden). The best accuracy result of 96.00% was obtained through the process of computation testing in the server based on variations in terms of the number of patterns of Deep Miden itemset sequences and several k values on KNN classification method.

Implementasi Metode K-Nearest Neighbor Dalam Mengklasifikasikan Jenis Suara Berdasarkan Jangkauan Vokal

Humans have voice characteristics with different vocal ranges, namely the male voice consists of Tenor, Baritone, and Bass, while the female voice consists of Soprano, Mezzosoprano, and Alto. Determining the voice range, especially for a singer, requires a vocal trainer or musical instrument that is quite difficult to access. Therefore, a sound classification system created based on vocal range using the Harmonic Product Spectrum (HPS) feature extraction method and the K-Nearest Neighbors (KNN) classification method uses k parameters from 1 to 40. The test gets the highest accuracy on parameter k=8, which is 88.88%, so that from the resulting accuracy to prove the K-Nearest Neighbor (KNN) method gives good results in classifying the type of voice.
 Keywords: Classification, Vocal range, Harmonic Product Spectrum, K-Nearest Neighbors

A Hybrid DenseNet201-SVM for Robust Weed and Potato Plant Classification

Potato plant growth needs to be protected from weeds that grow around it. Currently, the manual spraying of pesticides by farmers is not only precise on weeds but also on cultivated plants. Therefore, we need an intelligent system that can appropriately classify potato plants and weeds. The research contribution combines feature extraction and appropriate classification methods to obtain optimal accuracy. In addition, the small amount of data also contributes to this research. In this research, it is proposed to use a combination of feature extraction using deep learning techniques and classification using machine learning. We use the feature extraction method with the DenseNet201 model because this study's data is not too much. Complex vectors from DenseNet201 were reduced using Principal Component Analysis (PCA). Then we classified it with the Support Vector Machine (SVM) and K-Nearest Neighbor (KNN) classification methods. The experimental results show that the PCA method can reduce the complexity of high-dimensional features into 2 and 3 dimensions. The average of the best classification results using SVM was obtained with a 3-dimensional PCA configuration, but on the contrary, using KNN obtained the best results in a 2-dimensional PCA configuration. The results showed 100% accuracy on the DenseNet201-SVM hybrid. The SVM kernel configuration used is a linear kernel. The results of this study can be an insight into an accurate classification method for separating weeds and potatoes so that agricultural technology can apply this method for classification.

Efficient Parallel Processing of k-Nearest Neighbor Queries by Using a Centroid-based and Hierarchical Clustering Algorithm

The k-Nearest Neighbor method is one of the most popular techniques for both classification and regression purposes. Because of its operation, the application of this classification may be limited to problems with a certain number of instances, particularly, when run time is a consideration. However, the classification of large amounts of data has become a fundamental task in many real-world applications. It is logical to scale the k-Nearest Neighbor method to large scale datasets. This paper proposes a new k-Nearest Neighbor classification method (KNN-CCL) which uses a parallel centroid-based and hierarchical clustering algorithm to separate the sample of training dataset into multiple parts. The introduced clustering algorithm uses four stages of successive refinements and generates high quality clusters. The k-Nearest Neighbor approach subsequently makes use of them to predict the test datasets. Finally, sets of experiments are conducted on the UCI datasets. The experimental results confirm that the proposed k-Nearest Neighbor classification method performs well with regard to classification accuracy and performance.

The effects on classifier performance of 2D discrete wavelet transform analysis and whale optimization algorithm for recognition of power quality disturbances

Power Quality (PQ) is becoming more and more important day by day in the electric network. Signal processing, pattern recognition and machine learning are increasingly being studied for the automatic recognition of any disturbances that may occur during the generation, transmission, and distribution of electricity. There are three main steps to identify the PQ disturbances. These include the use of signal processing methods to calculate the features representing the disturbances, the selection of those that are more useful than these feature sets to prevent the creation of a complex classification model, the creating a classification model that recognizes multiple classes using the selected feature subsets. In this study, one-dimensional (1D) PQ disturbances signals are transformed into two-dimensional (2D) signals, 2D discrete wavelet transforms (2D-DWT) are used to extract the features. The features are extracted by using the wavelet families such as Daubechies, Biorthogonal, Symlets, Coiflets and Fejer-Korovkin in 2D-DWT to analyze PQ disturbances. Whale Optimization Algorithm (WOA) and k-nearest neighbor (KNN) classifier determine the feature subsets. Then, WOA and k nearest neighbor (KNN) classifier are used to determine the feature group. By using KNN and Support Vector Machines (SVM) classification methods, Classifier models that distinguish PQ disturbances are formed. The main aim of the study is to determine the features derived from 2D wavelet coefficients for different wavelet families and to determine which of them has a better classification performance to distinguish PQ disturbances signals. At the same time, different classification methods are simulated and a model which can classify PQ disturbances signals with high performance is created. Also, the generated models are analysed for their performance in terms of different noise levels (40 dB, 30 dB, 20 dB). The result of this simulation study shows that the model developed to classify PQ disturbances is superior to conventional models and other 2D signal processing methods in the literature. In addition, it was concluded that the proposed method can cope better with noisy signals by low computational complexity and higher classification rate.

Comparison of Support Vector Machine and K-Nearest Neighbors in Breast Cancer Classification

Cancer is one of the leading causes of death, and breast cancer is the second leading cause of cancer death in women. One method to realize the level of malignancy of breast cancer from an early age is by classifying the cancer malignancy using data mining. One of the widely used data mining methods with a good level of accuracy is the Support Vector Machine (SVM) and K-Nearest Neighbors (KNN). Evaluation techniques of percentage split and cross-validation were used to evaluate and compare the SVM and KNN classification models. The result was that the accuracy level of the SVM classification method was better than the KNN classification method when using the cross-validation technique, which is 95,7081%. Meanwhile, the KNN classification method was better than the SVM classification method when using the percentage split technique, which is 95,4220%. From the comparison results, it can be seen that the KNN and SVM methods work well in the classification of breast cancer.

Linked Data Fusion Based on Similarity Calculation and K-Nearest Neighbor

The development of semantic web technology supports the continuous development of linked data and its applications. In order to make effective use of the growing linked data on the web, multi-source data should be fused, which is a key step in multi-source large-scale data analysis and management. Currently, merging these data can be difficult, because various sources usually provide multiple conflict descriptions for entities in the same real world. To complete the fusion, we need to solve the problem of object conflict. This paper proposes a linked data fusion method based on similarity calculation and k-nearest neighbor. This method has two contributions. Firstly, a similarity calculation method of linked data is proposed, which can effectively integrate URI nodes and blank nodes in linked data; Secondly, a literal type node fusion strategy based on k-nearest neighbor classification method is proposed, which realizes the automation of fusion and has the independence of data source. The results show that compared with other methods, this method can improve the conciseness and consistency and precision by up to 12.9%, 30.6% and 12%.

Perbandingan Nilai K pada Klasifikasi Pneumonia Anak Balita Menggunakan K-Nearest Neighbor

Pneumonia is an infectious disease that attacks the lower respiratory tract and is one of the main causes of death in children under five. Pneumonia is easy to attack toddlers caused by various microorganisms that exist in the environment such as viruses, bacteria, fungi and micro bacteria. This study uses K-Nearest Neighbor (KNN) for the classification of pneumonia in patients based on the symptoms experienced. The KNN classification method is carried out by comparing the object distance between the test data and the overall object on the training data based on the patient's medical history data. The comparison of the percentage of the training data and the test data used is 90:10, 80:20, and 70:30 to calculate the value of the closest distance of the test data to the overall training data with the number of k used. The confusion matrix was used to measure the results of the Pneumonia classification test for toddlers with a combination of the amount of training data and test data on the number of k={1, 3, 5, 7, 9, 11}, the highest accuracy, precision, recall, and F-measure values were obtained. 0.86, 0.89, 1, and 0.91 for 90% training data, 10% test data with a value of k = 3. Keywords: Pneumonia, Toddlers, KNN, Confusion Matrix.

Chinese Language Literature Emotion Analysis Model Based on Unbalanced K-Nearest Neighbor Classification Method

The Chinese language is a nation’s symbol, the accumulation of a country’s magnificent culture, and the pearl precipitated in the long history’s washing. The Chinese language is rich and complex, and there are still many topics and issues that merit repeated exchanges and discussions in academic circles. This study proposes a classification method of emotion polarity based on reliability analysis in order to identify the tendency of literary emotion in Chinese language. Support vector machine (SVM), class center, and KNN (K-nearest neighbor) are included in the combined classifier, which effectively improves the accuracy and efficiency of emotion polarity classification. A Chinese literary emotion analysis model based on the method of UKNNC (unbalanced K-nearest neighbor classification) is proposed at the same time by analysing the characteristics of text structure and emotion expression. The experimental results show that, when compared to traditional machine methods, the UKNNC method can analyse text sentiment in fine-grained and multilevel ways, while also improving the accuracy of Chinese literary sentiment analysis.

Komparasi Metode K-Nearest Neighbor dan Random Forest Dalam Prediksi Akurasi Klasifikasi Pengobatan Penyakit Kutil

Warts are a skin health problem, usually characterized by small, rough bumps on the surface of the skin caused by a virus, known as the human papillomavirus (HPV). The common way of treating warts is with immunotherapy, which is the treatment of warts by strengthening the body's immune system. In the process of predicting and diagnosing warts, it can be done by applying Machine Learning. This study focuses on the comparison of the K-Nearest Neighbor classification method with Random Forest to see the level of accuracy in predicting the success of the treatment of warts. Data for Immunotheraphy was obtained from the UCI Machine Learning Repository with a total of 90 data records, 7 attributes and 1 attribute class. Based on the results of testing the K-Nearest Neighbor and Random Forest methods to see the accuracy of the prediction of the success of the data being tested, the results obtained are the accuracy of the KNN method of 90.00% and the Random Forest method with an accuracy of 85.50%. From the results obtained from the tests that have been carried out, it is known that the Random Forest method is a better method than K-Nearest Neighbor in predicting accuracy in the Immunotheraphy Dataset.