scholarly journals Analisis Penjualan Produk Paket Kuota Internet Dengan Metode K-Nearest Neighbor

2021 ◽  
Vol 6 (1) ◽  
pp. 111
Author(s):  
Dedi Handoko ◽  
Heru Satria Tambunan ◽  
Jaya Tata Hardinata
Keyword(s):  
Nearest Neighbor ◽  
Test Sample ◽  
K Nearest Neighbor ◽  
Nearest Neighbor Algorithm ◽  
The Past ◽  
Expected Result ◽  
Future Supply ◽  
K Nearest Neighbor Algorithm ◽  
One Year ◽  
A Company

PT. Akses Lintas Nusantara is a company engaged in the sale of Internet Quota Packages and package balances. This company has various data packages ranging from 6 GB, 11 GB, 21 GB and 32 GB with various price variations. Based on data on sales of internet quota package products for the past 1 (one) year, predictions for future sales are needed in order to facilitate the company in planning the provision of internet quota package stock. The K-Nearest Neighbor (KNN) algorithm is a method that is a supervised algorithm where the results of the new test sample are classified based on the majority of the categories on the KNN. To find out the sales of these products, the K-Nearest Neighbor algorithm is used. The expected result is to make it easier for companies to predict the future supply of internet quota packages in each region or region. The results of the research that have been carried out are prediction of Internet Quota Package Sales consisting of SP CL1, SPCL2, SPCL4 and SP CL8 with an Accuracy of 71.43%.

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.

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