Multiclass classification of n-butanol concentrations with k-nearest neighbor algorithm and support vector machine in an electronic nose

2012 ◽  
Vol 166-167 ◽  
pp. 721-725 ◽  
Author(s):  
Selda Güney ◽  
Ayten Atasoy
Keyword(s):  
Support Vector Machine ◽  
Electronic Nose ◽  
Nearest Neighbor ◽  
Multiclass Classification ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Nearest Neighbor Algorithm ◽  
K Nearest Neighbor Algorithm

KOMPARASI ALGORITMA KLASIFIKASI PADA ANALISIS REVIEW HOTEL

2018 ◽  
Vol 14 (2) ◽  
pp. 261
Author(s):  
Lila Dini Utami
Keyword(s):  
Support Vector Machine ◽  
Nearest Neighbor ◽  
Naive Bayes ◽  
Service Providers ◽  
Naïve Bayes ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Nearest Neighbor Algorithm ◽  
K Nearest Neighbor Algorithm ◽  
Auc Value

At this time the freedom to express opinions in oral and written forms about everything is very easy. This activity can be used to make decisions by some business people. Especially by service providers, such as hotels. This will be very useful in the development of the hotel business itself. But the review data must be processed using the right algorithm. So this study was conducted to find out which algorithms are more feasible to use to get the highest accuracy. The methods used are Naïve Bayes (NB), Support Vector Machine (SVM), and k-Nearest Neighbor (k-NN). From the process that has been done, the results of Naïve Bayes accuracy are 71.50% with the AUC value is 0.500, Support Vector Machine is 72.50% with the AUC value is 0.936 and the accuracy results if using the k-Nearest Neighbor algorithm is 75.00% with the AUC value is 0.500. The use of the k-Nearest Neighbor algorithm can help in making more appropriate decisions for hotel reviews at this time.

scholarly journals Business Intelligence using the K-Nearest Neighbor Algorithm to Analyze Customer Behavior in Online Crowdfunding Systems

2020 ◽  
Vol 202 ◽  
pp. 16005
Author(s):  
Chashif Syadzali ◽  
Suryono Suryono ◽  
Jatmiko Endro Suseno
Keyword(s):  
Business Intelligence ◽  
Nearest Neighbor ◽  
Customer Behavior ◽  
Training Data ◽  
Business Strategies ◽  
Intelligence Analysis ◽  
K Nearest Neighbor ◽  
Nearest Neighbor Algorithm ◽  
K Nearest Neighbor Algorithm

Customer behavior classification can be useful to assist companies in conducting business intelligence analysis. Data mining techniques can classify customer behavior using the K-Nearest Neighbor algorithm based on the customer's life cycle consisting of prospect, responder, active and former. Data used to classify include age, gender, number of donations, donation retention and number of user visits. The calculation results from 2,114 data in the classification of each customer’s category are namely active by 1.18%, prospect by 8.99%, responder by 4.26% and former by 85.57%. System accuracy using a range of K from K = 1 to K = 20 produces that the highest accuracy is 94.3731% at a value of K = 4. The results of the training data that produce a classification of user behavior can be used as a Business Intelligence analysis that is useful for companies in determining business strategies by knowing the target of optimal market.

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