k-Nearest Neighbor based Classification Algorithm for Phase to Phase Faults in 6-Phase Transmission Lines

2019 ◽  
Vol 3 (1) ◽  
pp. 10-13
Author(s):  
A Naresh Kumar ◽  
◽  
Ch Sanjay ◽  
M Chakravarthy ◽  
◽  
...  
Keyword(s):  
Transmission Lines ◽  
Nearest Neighbor ◽  
Classification Algorithm ◽  
K Nearest Neighbor ◽  
Phase Transmission

scholarly journals KNN classifier based approach for multi-class sentiment analysis of twitter data

2018 ◽  
Vol 7 (3) ◽  
pp. 1372
Author(s):  
Soudamini Hota ◽  
Sudhir Pathak
Keyword(s):  
Sentiment Analysis ◽  
Opinion Mining ◽  
Nearest Neighbor ◽  
Learning Algorithm ◽  
Online News ◽  
Classification Algorithm ◽  
Sentiment Classification ◽  
Supervised Machine Learning ◽  
K Nearest Neighbor ◽  
News Reports

‘Sentiment’ literally means ‘Emotions’. Sentiment analysis, synonymous to opinion mining, is a type of data mining that refers to the analy-sis of data obtained from microblogging sites, social media updates, online news reports, user reviews etc., in order to study the sentiments of the people towards an event, organization, product, brand, person etc. In this work, sentiment classification is done into multiple classes. The proposed methodology based on KNN classification algorithm shows an improvement over one of the existing methodologies which is based on SVM classification algorithm. The data used for analysis has been taken from Twitter, this being the most popular microblogging site. The source data has been extracted from Twitter using Python’s Tweepy. N-Gram modeling technique has been used for feature extraction and the supervised machine learning algorithm k-nearest neighbor has been used for sentiment classification. The performance of proposed and existing techniques is compared in terms of accuracy, precision and recall. It is analyzed and concluded that the proposed technique performs better in terms of all the standard evaluation parameters. 

scholarly journals A High Performace of Local Binary Pattern on Classify Javanese Character Classification

2018 ◽  
Vol 5 (1) ◽  
pp. 8 ◽  
Author(s):  
Ajib Susanto ◽  
Daurat Sinaga ◽  
Christy Atika Sari ◽  
Eko Hari Rachmawanto ◽  
De Rosal Ignatius Moses Setiadi
Keyword(s):  
Feature Extraction ◽  
Image Classification ◽  
Local Binary Pattern ◽  
Nearest Neighbor ◽  
Classification Algorithm ◽  
K Nearest Neighbor ◽  
Characteristic Extraction ◽  
Research Objects ◽  
Character Classification

The classification of Javanese character images is done with the aim of recognizing each character. The selected classification algorithm is K-Nearest Neighbor (KNN) at K = 1, 3, 5, 7, and 9. To improve KNN performance in Javanese character written by the author, and to prove that feature extraction is needed in the process image classification of Javanese character. In this study selected Local Binary Patter (LBP) as a feature extraction because there are research objects with a certain level of slope. The LBP parameters are used between [16 16], [32 32], [64 64], [128 128], and [256 256]. Experiments were performed on 80 training drawings and 40 test images. KNN values after combination with LBP characteristic extraction were 82.5% at K = 3 and LBP parameters [64 64].

scholarly journals COMPARISON OF MACHINE LEARNING CLASSIFICATION ALGORITHM ON HOTEL REVIEW SENTIMENT ANALYSIS (CASE STUDY: LUMINOR HOTEL PECENONGAN)

2020 ◽  
Vol 16 (1) ◽  
pp. 59-64
Author(s):  
Jaja Miharja ◽  
Jordy Lasmana Putra ◽  
Nur Hadianto
Keyword(s):  
Machine Learning ◽  
Nearest Neighbor ◽  
Classification Algorithm ◽  
K Nearest Neighbor ◽  
Business Decisions ◽  
Machine Learning Classification ◽  
Business People ◽  
Auc Value ◽  
The Right

Analysis of hotel review sentiment is very helpful to be used as a benchmark or reference for making hotel business decisions today. However, all the review information obtained must be processed first by using an algorithm. The purpose of this study is to compare the Classification Algorithm of Machine Learning to obtain information that has a better level of accuracy in the analysis of hotel reviews. The algorithm that will be used is k-NN (k-Nearest Neighbor) and NB (Naive Bayes). After doing the calculation, the following accuracy level is obtained: k-NN of 60,50% with an AUC value of 0.632 and NB of 85,25% with an AUC value of 0.658. These results can be determined by the right algorithm to assist in making accurate decisions by business people in the analysis of hotel reviews using the NB Algorithm.

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