The Analysis and Application of Classification Methods For developing software intended for Students Admission Based on Aptitude and Interest Scouting System

• Main Author: SIBARONI (NIM : 23505025); Pembimbing: Dr.Ing. Ir. Benhard Sitohang, YULIANT
• Format: Theses
• Language: Indonesia
• Online Access: https://digilib.itb.ac.id/gdl/view/17686
• Institution: Institut Teknologi Bandung

The admission process for university students through aptitude and interest scouting can be done by predicting the possible GPA that students may achieve. This can be done by using data mining classification methods. The classifier is developed based the student’s historical data related to their achievement in their high school, the rank of their school and the major they choose. <br /> <br /> <br /> The classification methods further analyzed was decision tree and Bayesian network. These methods are often used in the solving problems in the area of prediction and their level of complexity and accuracy are similar. Specially related to decision tree, method of C4.5 is chosen because it has been widely used. Meanwhile, Naïve Bayes was chosen because the assumptions within this method are regarded to be more precise than those other of methods. <br /> <br /> <br /> Based on accuracy level, the result shows that there is no absolute superior method. The accuracy level of these methods has not been satisfying. In term of their process time, C4.5 needs shorter time than Naïve Bayes. However, the process time for both methods is applicable. For C4.5, there is no correlation between data dispersion and accuracy levels. For Naïve Bayes, it can be found that there is dependency between the attributes. Thus, Naïve Bayes is not suitable to process the data of this experiment. For the purpose software implementation, the level of accuracy of the methods needs to be increased. <br />