Lateness arrives at work can be experienced by anyone, including teachers. Teachers who are late arriving at school have shown examples of bad behavior for students. It takes a study to determine the factors that cause a teacher to arrive late to school. Data Mining is selected to process the data that has been available. Processing uses 3 classification algorithms which are decision tree (C4.5, Random Tree, and Random Forest) algorithms. All three algorithms will be tested for known performance, where the best algorithm is determined by accuracy and AUC. The results of the research were obtained that Random Forest with pruning and pre-pruning is the best for accuracy value with 74.63% and also AUC value with 0.743. The teacher's delay in this study is often done by teachers who have a vehicle compared to those who do not have a vehicle.
The online game is a game which is currently booming and interest ranging from children, teens, to adults. Online games can create a sense of opium to the people who play it. Online games become a new problem for the students, because online games make learning impaired concentration. The learning achievements can be measured from the value of report cards. The challenge on this research can be carried out using a method of classification for predicting learning achievements using algorithms of classification i.e. Naïve Bayes, Random Forest, and C4.5. After the third comparison algorithm, then the prediction results obtained by learning achievements. Naïve Bayes algorithm proved that value the accuracy and value of the AUC 69.18% of 0.771 contains the classification, fair for the random forest algorithm accuracy 66.34% and AUC values of 0.738 contains the classification, fair as for algorithm C4.5 65.65% accuracy and value of the AUC of 0.686 including into poor classification. From these results it can be concluded that the naïve bayes algorithm has higher accuracy compared with the random forest algorithm and C4.5, visible difference in accuracy between the naïve bayes with random forest of 2,84%, whereas the difference between the naïve bayes with C4.5 of 3,53%. Naïve bayes algorithm is thus able to predict achievement students can study better.
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