Eigenvalue of Analytic Hierarchy Process as The Determinant for Class Target on Classification Algorithm

Data mining has two main concepts of data distribution, namely supervised learning and unsupervised learning. The most easily recognizable concepts from data distribution is related to the dataset, with and without target class. Analytic Hierarchy Process (AHP) technique that carries the concept of pairwise comparison able to answer the problem related to the dataset, which is to change unsupervised to be supervised by determining eigenvalue value of each attribute and sub attribute in AHP method. The case study conducted in this issue is related to determining the target classes used to predict the success of a student learning in UIN Suska Riau. The three main attributes are Procrastination, Total Credits (SKS) and Number of Repeated Courses, each having eigenvalues of 0.319; 0.189 and 0.171 which become the feedback in the determination of the Target Timely Graduation (TG) or Possibility of Timely Graduation (PTG). The biggest consistency ratio generated in the AHP case is 9.4% in the GPA attribute. This research recommends that further research should use datasets that have been arranged based on experimental combinations of the three main attributes above, then applied to the classification or prediction algorithm. So that it would obtain a decision of accuracy from data used against the real result on the field

Data Sharing Technique Modeling for Naive Bayes Classifier for Eligibility Classification of Recipient Students in the Smart Indonesia Program

The objective of Smart Indonesia Program (Program Indonesia Pintar: PIP) is to help school-aged people from poor / vulnerable / priority families to continue to receive education services to graduate from secondary education, both through formal and non-formal education channels. In its implementation, there are still many fraudulent in the proces of nominating proposal PIP funds and there are still many prospective students who should not receive PIP because they do not meet the technical guidelines provided by the Ministry of Education and Culture to determine the eligibility of prospective recipients of PIP funds can be done by schools and stakeholders, one of them by using classification techniques. One algorithm that is widely used in classification is the Naive Bayes Classifier (NBC) algorithm. In this study three data sharing techniques were used, namely Hold Out 70% training data and 30% testing data, K-Means Clustering, and also 10 Fold Cross Validation. Determination of the best data sharing technique will be determined by looking at the value of Accuracy, Precision, and Recall and also the value of Area Under Curve (AUC) which is illustrated by the Receiver Operating Characteristic (ROC) curve so that the NBC algorithm is generated with 10 Fold Cross Validation has a very good classification level with the values of accuracy, precision, and recall respectively at 97.40%; 100%; and 76.14%.