Sistem Peringatan Dini Keterlambatan Masa Studi Mahasiswa Menggunakan Metode Support Vector Machine

Students graduating late from college are a common problem in universities. The study of students at universities is generally designed to be completed in 3.5 to 4 years. If a student has not graduated past that time, he is considered late in completing his education. Lambung Mangkurat University, as the oldest university in Kalimantan, also experienced these problems. Therefore, an early warning system was build to predict students' possibility of being late in completing their studies. This study uses a sample of students from the Faculty of Engineering, the University of Lambung Mangkurat, to predict students who will be late graduating from Lambung Mangkurat University since semester 5. This system was to develop using a model built using the Support Vector Machine (SVM) method. Model training conducted using 755 data from Lambung Mangkurat University Faculty of Engineering students from 2010 to 2014. Then, the performance of the model tested using 234 student data from 2015 and 2016. The parameters used were the number of credits, gender, GPA on semester 1 to 4, and study programs. The test results show that the model has good performance to predict students who will be late in completing their studies with 88.2% accuracy

The Implementation of Restricted Boltzmann Machine in Choosing a Specialization for Informatics Students

Choosing a specialization was not an easy task for some students, especially for those who lacked confidence in their skill and ability. Specialization in tertiary education became the benchmark and key to success for students’ future careers. This study was conducted to provide the learning outcomes record, which showed the specialization classification for the Informatics students by using the data from the students of 2013-2015 who had graduated. The total data was 319 students. The classification method used for this study was the Restricted Boltzmann Machine (RBM). However, the data showed imbalanced class distribution because the number of each field differed greatly. Therefore, SMOTE was added to classify the imbalanced class. The accuracy obtained from the combination of RBM and SMOTE was 70% with a 0.4 mean squared error

The potential of individual factor towards graduate on time (GOT) among PHD students in University Utara Malaysia (UUM)

One major issued faced by higher learning institutions in many countries’ especially Malaysia is Graduate on Time (GOT), particularly among PhD students. This is followed by the concerns of university images and rankings. Past studies have shown that Graduate on Time (GOT) could be influenced by various factors. Therefore, this study investigated the relationship of individual factors such as personality type A and B, motivation, knowledge, skills, and abilities, and knowledge sharing behavior as a predictor of graduate on time. A total of four hypotheses were developed, and binary logistic regression was carried out to examine the effect. The sample consisted of 159 PhD students and students were selected starting from 3rd semester and above. This is because the outcome of graduation among 1st-semester students is not identifiable. Two of the hypotheses were supported, and the results showed that knowledge, skills, and abilities and knowledge sharing behavior have a significant effect on the outcome of graduate on time. This study aims to implement the proposed models that comprise several factors in predicting the outcome of students that will complete their PhD studies on the predetermined time. The analysis techniques used are Binary Logistic Regression Model, whereby a set of data were examined to determine the outcome. The results and findings in this study may contribute major insights into institutions and students themselves as the gaps concerning student’s personality traits as the causes of the decrease of graduation rates and how to handle and measure them. Moreover, the findings also imply that personality types seem to be a new predictor in research which lead to a person actions that may influence their completion of studies. Thus, stakeholders should join hands in providing a better solution to sustain the credibility of students and institutions as a whole