Model Klasifikasi Data Mining Sebagai Pertimbangan Pemilihan Peminatan Departemen untuk On Job Training (OJT) di Hotel - Dedi Joko Purnomo; Izza Ulumuddin Ahmad Asshofi; Cindy Citya Dima; Prima Setia Judha Pranatha (JITEKH, 2023)
Determining the choice of the department in the implementation of On Job Training (OJT) for students is always based on the needs and requests of the hotel or manually dividing the number of students who will carry out Job Training with the operational department in the hotel. Selection is often not in accordance with the potential of student expertise that has been achieved academically. Student academic data is often only archived, there is no utilization and efficient processing to obtain patterns, or habits that can be found as material for analysis in higher education institutions to improve the quality of students and campuses. To overcome the existing problems, the Udinus hospitality management study program utilizes student academic data to help determine department choices in the implementation of job training. The method used in this study is uses the Data Mining Classification Algorithm, namely Algorithm C4.5. The purpose of this research is to find out that the classification model created using the C4.5 Algorithm decision tree model can assist Study Programs in considering the selection of majors for student Job Training. This research is included in experimental research that uses data from students by carrying out the stages of classification methods such as literature review, data collection, data selection, data processing, and data testing. The result achieved is the application of the decision tree algorithm rule C4.5 by using data on student scores that can be used to assist in determining the selection of majors in the implementation of Job Training. The test results using the decision tree C4.5 algorithm obtained good results with an accuracy of up to 94.44% with a ratio of 80% training data and 20% test data, the ratio of the number of training data can affect the accuracy of values ​​in each experiment.