A feature weighted support vector machine and K-nearest neighbor algorithm for stock market indices prediction

2017 ◽  
Vol 80 ◽  
pp. 340-355 ◽  
Author(s):  
Yingjun Chen ◽  
Yongtao Hao
Keyword(s):  
Support Vector Machine ◽  
Stock Market ◽  
Nearest Neighbor ◽  
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.

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