PENGEMBANGAN APLIKASI PENJADWALAN MATA KULIAH DENGAN MENGGUNAKAN ALGORITMA CONSTRAINT SATISFACTION

Scheduling is one of academic institution administration problems. Making an academic schedule might not difficult but it will consume much time. The schedule maker is also ought to redoing the same procedure at least for every semester. As a result, scientists have discovered some methods to automate and make the scheduling process simpler, one of them is constraint satisfaction method. Considering the ability of constraint satisfaction problem to solve scheduling problem, this research try to develop scheduling application with constraint satisfaction method under the object oriented paradigm. This application can be used for simplifying the scheduling process

Feature Selection on Pregnancy Risk Classification Using C5.0 Method

The maternal mortality rate in Indonesia is still relatively high. This is caused by several factors, including the ignorance of pregnant women about the risk status of pregnancy. Several methods are proposed for early detection of the risk of a mother's pregnancy. However, no one has highlighted what features are most influential in the process of classifying the risk of pregnancy. In this research, we use data of pregnant women in one of the health centers in Malang, Indonesia, as a dataset. The dataset has 107 features, therefore, feature selection is needed for the classification process. We propose to use the C5.0 method to select important features while classifying dataset into low, high, and very high risk of pregnancy. C5.0 was chosen because this method has a better pruning algorithm and requires relatively smaller memory compared to C4.5. Another classification method (SVM, Naive Bayes, and Nearest Neighbor) is then used to compare the accuracy values between datasets that use all features with datasets that only use the selected features. The test results show that feature selection can increase accuracy by up to 5%