Student Orientation Recommender System using TOPSIS and AHP

Abstract

 The process of traditional school guidance is carried out by specialists. They make a study of the student files based on the marks of the first year and the second year of the baccalaureate. Considering the progressive number of students and also the lack of time to make the decision, as the selection of the specialty has a great effect on the academic course of the students, we have realized a system of specialty recommendation, to computerize the orientation process and save time. But the major problem is that the students do not care about this process and pay no attention to it despite its importance. As well as the software which makes the orientation is chargeable. To order these specialties to take the best specialty. We have arrived at a problem of multi-criteria which makes it impossible to make a decision with these criteria. Because these criteria do not have the same importance and also are not
compatible, as there are criteria that must be maximum and other criteria must be minimal. To solve this problem, two systems of orientation and academic reorientation of students have been implemented. In both systems, the SMOTE method has been used to balance the learning data in the preprocessing phase. Then in the treatment phase, we sorted the specialties using in the first system, a hybridization of TOPSIS method and the information gain to find the weights of the criteria used, and in the second system, we used a hybridization of the AHP method and information gain. The results obtained indicate that before balancing data using the SMOTE method, the total accuracy of TOPSIS (84.20%) is higher than the total accuracy of AHP (83.71%). After applying balancing data using the SMOTE method, the total accuracy has increased. The total accuracy of TOPSIS (91.35%) is also higher than the total accuracy of AHP (90.83%). For the complexity of the two methods, it is related to the number of criteria and the number of alternatives. If the
number of criteria is more than 10 criteria, the complexity of TOPSIS is less than the complexity of AHP, and vice versa. The complexity of the two methods also depends on the number of alternatives, if the number of alternatives exceeds 10, the TOPSIS method becomes more complex than the AHP method. In general, the system based on TOPSIS method and the information gain is more precise than the system based on the AHP method and the information gain. But the complexity of the AHP method is less than the complexity of the TOPSIS method.