scholarly journals ANALISIS CREDIT SCORING TERHADAP STATUS PEMBAYARAN BARANG ELEKTRONIK DAN FURNITURE MENGGUNAKAN BOOTSTRAP AGGREGATING K-NEAREST NEIGHBOR

2021 ◽  
Vol 15 (4) ◽  
pp. 735-744
Author(s):  
Putri Sri Astuti ◽  
Memi Nor Hayati ◽  
Rito Goejantoro
Keyword(s):  
Length Of Stay ◽  
Nearest Neighbor ◽  
Credit Scoring ◽  
Classification Algorithms ◽  
K Nearest Neighbor ◽  
Nearest Neighbor Algorithm ◽  
Independent Variables ◽  
Bootstrap Aggregating ◽  
K Nearest Neighbor Algorithm ◽  
C 73

Classification is the process of grouping objects that have the same characteristics into several categories. This study applies a combination of classification algorithms, namely Bootstrap Aggregating K-Nearest Neighbor in credit scoring analysis. The aim is to classify the credit payment status of electronic goods and furniture at PT KB Finansia Multi Finance in 2020 and determine the level of accuracy produced. Credit payment status is grouped into 2 categories, namely smoothly and not smoothly. There are 7 independent variables that are used to describe the characteristics of the debtor, namely age, number of dependents, length of stay, years of service, income, amount of payment, and payment period. The application of the classification algorithm at the credit scoring analysis is expected to assist creditors in making decisions to accept or reject credit applications from prospective debtors. The results showed that the accuracy obtained from the Bootstrap Aggregating K-Nearest Neighbor algorithm with a proportion of 90:10, m=80%, C=73, and K=5 was the best, which was 92.308%.

scholarly journals On Some Fuzzy Classification Algorithms and the AEC Model

2020 ◽  
Vol 19 ◽  
Keyword(s):  
Pattern Recognition ◽  
Decision Rule ◽  
Nearest Neighbor ◽  
Fuzzy Classification ◽  
Classification Algorithms ◽  
K Nearest Neighbor ◽  
Nearest Neighbor Algorithm ◽  
Recognition Algorithms ◽  
K Nearest Neighbor Algorithm

In the paper some fuzzy classification algorithms based upon a nearest neighbor decision rule areconsidered in terms of the pattern recognition algorithms which are based on the computation of estimates (theso-called AEC model). It is shown that the fuzzy K nearest neighbor algorithm can be assigned to the AECclass. In turn, it is found that some standard AEC algorithms, which depend on a number of numericalparameters, can be used as fuzzy classification algorithms. Yet among them there exist algorithms extremalwith respect to these parameters. Such algorithms provide maximum values of the associated performancemeasures.

scholarly journals Recent trends in big data using hadoop

2019 ◽  
Vol 8 (1) ◽  
pp. 39
Author(s):  
Chetna Kaushal ◽  
Deepika Koundal
Keyword(s):  
Data Mining ◽  
Social Media ◽  
Big Data ◽  
Nearest Neighbor ◽  
Classification Algorithms ◽  
K Nearest Neighbor ◽  
Nearest Neighbor Algorithm ◽  
Clustering Techniques ◽  
Recent Trends ◽  
K Nearest Neighbor Algorithm

<span>Big data refers to huge set of data which is very common these days due to the increase of internet utilities. Data generated from social media is a very common example for the same. This paper depicts the summary on big data and ways in which it has been utilized in all aspects. Data mining is radically a mode of deriving the indispensable knowledge from extensively vast fractions of data which is quite challenging to be interpreted by conventional methods. The paper mainly focuses on the issues related to the clustering techniques in big data. For the classification purpose of the big data, the existing classification algorithms are concisely acknowledged and after that, k-nearest neighbor algorithm is discreetly chosen among them and described along with an example. </span>

scholarly journals Physiological Stress Prediction using Machine Learning Classifiers

2019 ◽  
Vol 9 (1) ◽  
pp. 675-677
Keyword(s):  
Machine Learning ◽  
Nearest Neighbor ◽  
Physiological Stress ◽  
Classification Algorithms ◽  
K Nearest Neighbor ◽  
Nearest Neighbor Algorithm ◽  
Machine Learning Classifiers ◽  
Learning Classifiers ◽  
Stress Prediction ◽  
K Nearest Neighbor Algorithm

The aim of this study is to predict the stress of a person using Machine Learning classifiers. This system classifies the stress of a person as either High or Low. There are various classification algorithms present, out of which 9 classification algorithms have been chosen for this study. The algorithms implemented are K-Nearest Neighbor classifier, Support Vector Machine with an RBF kernel, Decision Tree algorithm, Random Forest algorithm, Bagging Classifier, Adaboost algorithm, Voting classifier, Logistic Regression and MLP classifier. The different algorithms are applied on the same dataset. The dataset is obtained from a GitHub repository labelled Stress classifier with AutoML. The different accuracies of each algorithm are found, and the classification algorithm with the best accuracy is determined. On comparison, it was found that the K-Nearest Neighbor algorithm has the best accuracy with an accuracy rate of 79.3% for physiological stress prediction. While other algorithms had varying accuracies, K-Nearest Neighbor algorithm was the most consistent.

Handwritten Balinesse Character Recognition using K-Nearest Neighbor

2018 ◽  
Author(s):  
I Wayan Agus Surya Darma
Keyword(s):  
Feature Extraction ◽  
Success Rate ◽  
Character Recognition ◽  
Nearest Neighbor ◽  
Recognition System ◽  
Extraction Process ◽  
K Nearest Neighbor ◽  
Nearest Neighbor Algorithm ◽  
K Nearest Neighbor Algorithm ◽  
Character Feature

Balinese character recognition is a technique to recognize feature or pattern of Balinese character. Feature of Balinese character is generated through feature extraction process. This research using handwritten Balinese character. Feature extraction is a process to obtain the feature of character. In this research, feature extraction process generated semantic and direction feature of handwritten Balinese character. Recognition is using K-Nearest Neighbor algorithm to recognize 81 handwritten Balinese character. The feature of Balinese character images tester are compared with reference features. Result of the recognition system with K=3 and reference=10 is achieved a success rate of 97,53%.

scholarly journals Detection Model on Fatigue Driving Behaviors Based on the Operating Parameters of Freight Vehicles

2021 ◽  
Vol 11 (15) ◽  
pp. 7132
Author(s):  
Jianfeng Xi ◽  
Shiqing Wang ◽  
Tongqiang Ding ◽  
Jian Tian ◽  
Hui Shao ◽  
...  
Keyword(s):  
Traffic Accidents ◽  
Nearest Neighbor ◽  
K Nearest Neighbor ◽  
Nearest Neighbor Algorithm ◽  
Operating Data ◽  
Detection Model ◽  
Driving Behaviors ◽  
K Nearest Neighbor Algorithm ◽  
Fatigue Driving ◽  
Freight Vehicles

Whether in developing or developed countries, traffic accidents caused by freight vehicles are responsible for more than 10% of deaths of all traffic accidents. Fatigue driving is one of the main causes of freight vehicle accidents. Existing fatigue driving studies mostly use vehicle operating data from experiments or simulation data, exposing certain drawbacks in the validity and reliability of the models used. This study collected a large quantity of real driving data to extract sample data under different fatigue degrees. The parameters of vehicle operating data were selected based on significant driver fatigue degrees. The k-nearest neighbor algorithm was used to establish the detection model of fatigue driving behaviors, taking into account influence of the number of training samples and other parameters in the accuracy of fatigue driving behavior detection. With the collected operating data of 50 freight vehicles in the past month, the fatigue driving behavior detection models based on the k-nearest neighbor algorithm and the commonly used BP neural network proposed in this paper were tested, respectively. The analysis results showed that the accuracy of both models are 75.9%, but the fatigue driving detection model based on the k-nearest neighbor algorithm is more reliable.

Sign in / Sign up

Export Citation Format

Share Document