Popular Content Prediction Based on Web Visitor Data With Data Mining Approach
Iqbal Dzulfiqar Iskandar, N Ch Basjaruddin, Deddy Supriadi, Ratningsih Ratningsih, Dini Silvi Purnia, Tio Satrio Wibisono · Journal of Physics Conference Series · 2020
Abstract 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