Open AccessPopular Content Prediction Based on Web Visitor Data With Data Mining Approach
Author(s) -
Iqbal Dzulfiqar Iskandar,
Noor Cholis Basjaruddin,
Deddy Supriadi,
- - Ratningsih,
Dini Silvi Purnia,
Taufik Wibisono
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1641/1/012105
Subject(s) - visitor pattern , random forest , computer science , data mining , content (measure theory) , quality (philosophy) , web page , process (computing) , mathematics , world wide web , machine learning , mathematical analysis , philosophy , epistemology , programming language , operating system
A quality website has five parameters that must be considered are: information, security, convenience, comfort, quality of service. But of course, the fifth parameter does not always guarantee the amount through its Web page will increase, from that problem. So research is conducted to predict website content based on visitor data with a data mining approach, this research aims to improve the quality of content on target according to the interest of website visitors. Evaluation of random forest algorithm has the value the accuracy of classification of 71 percent by value of Kappa 0.712 whereas the k-NN algorithm has higher accuracy values of random Forest algorithm i.e. worth 84.88 percent and kappa values of the mean 0.847 the k-NN algorithm performs data processing process predictions against data of web content more effectively than the random forest