Klasifikasi Penerima Beasiswa Kopertis dengan Menggunakan Algoritma C.45

Every year college get scholarship coordinator private universities (kopertis), Scholarship devoted to student in a college namely scholarship an increase in academic performance (scholarship PPA ) and scholarship assistance learn student (scholarship BBM). The process filing scholarship ppa and bbm through two stages selection selection is the first stage selection in college to determine a candidate scholarship recipients that would be proposed to kopertis, the selection that both the stage in kopertis selection. Many a student who submitted the scholarships as well as surpassing kouta given resulted in the process of select recipients taken longer since select must be in accordance with the criteria so that the recipient scholarship right on target . Based on these problems need a the act of determining scholarship recipients proper. The purpose of this research is to make classifications students scholarship recipients with algorithm C4.5. The results of classifications evaluate and validated with confusion matrix and a curve ROC, the results classifications students scholarship recipients namely algorithm C4.5 with the level of accuracy of 86.88 %, So that it can be applied for the problem the determination of scholarship recipients

KLASIFIKASI PENERIMA BEASISWA KOPERTIS DENGAN MENGGUNAKAN ALGORITMA C.45

Every year college get scholarship coordinator private universities (kopertis), Scholarship devoted to student in a college namely scholarship an increase in academic performance (scholarship PPA ) and scholarship assistance learn student (scholarship BBM). The process filing scholarship ppa and bbm through two stages selection selection is the first stage selection in college to determine a candidate scholarship recipients that would be proposed to kopertis, the selection that both the stage in kopertis selection. Many a student who submitted the scholarships as well as surpassing kouta given resulted in the process of select recipients taken longer since select must be in accordance with the criteria so that the recipient scholarship right on target . Based on these problems need a the act of determining scholarship recipients proper. The purpose of this research is to make classifications students scholarship recipients with algorithm C4.5. The results of classifications evaluate and validated with confusion matrix and a curve ROC, the results classifications students scholarship recipients namely algorithm C4.5 with the level of accuracy of 86.88 %, So that it can be applied for the problem the determination of scholarship recipients

Identifying Patterns in Course-Taking that Predict Student Leaving: A Comparison of Different Predictive Algorithms

Higher education institutions continue to face the problem of student attrition, which in turn impacts graduation rates overall. This has numerous drawbacks not only at the university or student levels but has far-reaching influences on society itself (Schuh & Topf, 2012). Although much research has investigated various factors that contribute towards attrition, on average only 40.3% of college students are found to complete their degrees (ACT, 2008). Despite an attempt to better understand the role different kinds of predictors have towards student success (Lotkowski, Robbins, & Noeth, 2004), limited research has assessed to what extent course information adds incremental variability towards predictive modeling of student retention. Lewis and Terry (2016) have investigated the application of multi-level modeling toward such predictors, while data mining techniques have been used sparingly to support the use of differing predictors. For this study, a method of data mining relatively new to the field of educational literature is contrasted with a hierarchically-based statistical approach to support in determining whether any significant course patterns can lead to improved student retention outcomes. Results from the analysis may provide insight into models that contain greater predictive accuracy, with long-term benefits into course placement as more effective advising is applied. Over time, any improved placement is expected to yield positive effects for students and the university as a whole. Keywords: student retention, data mining, symbolic regression, logistic regression, hierarchical analysis, multilevel modeling, statistical techniques, exploratory analysis, area under curve, AU