scholarly journals Data Mining Implementation with Algorithm C4.5 for Predicting Graduation Rate College Student

2021 ◽  
Vol 2 (3) ◽  
pp. 74-83
Author(s):  
Jeffri Saputra
Keyword(s):  
Data Mining ◽  
College Student ◽  
Graduation Rate

scholarly journals Application of Data Mining Classification Method for Student Graduation Prediction Using K-Nearest Neighbor (K-NN) Algorithm

2018 ◽  
Vol 1 (1) ◽  
pp. 1-8
Author(s):  
Mohammad Imron ◽  
Satia Angga Kusumah
Keyword(s):  
Data Mining ◽  
Graduation Rate ◽  
Nearest Neighbor ◽  
High Accuracy ◽  
Classification Method ◽  
K Nearest Neighbor ◽  
Data Mining Technique ◽  
Study Program ◽  
Mining Technique ◽  
Student Graduation

The student graduation rate is one of the indicators to improve the accreditation of a course. It is needed to monitor and evaluate student graduation tendencies, timely or not. One of them is to predict the graduation rate by utilizing the data mining technique. Data Mining Classification method used is the algorithm K-Nearest Neighbor (K-NN). The data used comes from student data, student value data, and student graduation data for the year 2010-2012 with a total of 2,189 records. The attributes used are gender, school of origin, IP study program Semester 1-6. The results showed that the K-NN method produced a high accuracy of 89.04%.

scholarly journals ALGORITMA APRIORI UNTUK MENEMUKAN HUBUNGAN ANTARA JURUSAN SEKOLAH DENGAN TINGKAT KELULUSAN MAHASISWA

2019 ◽  
Vol 12 (1) ◽  
pp. 69-78
Author(s):  
Indah Puji Astuti
Keyword(s):  
High School ◽  
Data Mining ◽  
Graduation Rate ◽  
A Priori ◽  
Influential Factors ◽  
School Level ◽  
High School Level ◽  
Student Graduation ◽  
The University ◽  
Course Of Study

If a university or course of study to apply for accreditation, graduation rate is one of the influential factors. Educational period targeted in 4 years or 8 semesters of study period. But in reality there are still many students who pass beyond the study period. In this case the university or course of study can utilize students' self data to predict the student's graduation rate. One of them by using the concept of data mining. In this research the authors used an a priori algorithm to find the relationship between departments taken at high school level with the level of graduation students. The student's graduation rate is measured by length of study and GPA. The calculation is  using 2 ways that is by manual calculation and by using Tanagra software. Based on the results of the analysis from regular class A students of 2012/2013 which amounted to 23 data it can be found rule, if the majors taken at high school level is SMK, then the possibility of the student will graduate on time with a period of 4 years or less. The GPA that the student will achieve between 3.1 - 3.5. Keywords - Algoritma Apriori, Data mining, Student Graduation, Tanagra

On the Application of Data Mining Algorithm in College Student Management

2021 ◽  
pp. 248-257
Author(s):  
Xiaofei Sun ◽  
Yunan Zeng
Keyword(s):  
Data Mining ◽  
College Student ◽  
Data Mining Algorithm ◽  
Student Management ◽  
Mining Algorithm

Close Campus Friendships and College Student Success

2017 ◽  
Vol 21 (3) ◽  
pp. 270-285 ◽  
Author(s):  
Ryan H. Bronkema ◽  
Nicholas A. Bowman
Keyword(s):  
Academic Achievement ◽  
College Student ◽  
Student Success ◽  
Grade Point Average ◽  
Graduation Rate ◽  
Student Attrition ◽  
Multilevel Analyses ◽  
College Student Success ◽  
Grade Point ◽  
College Friendships

Friendships are widely considered to be an essential part of life in college and beyond. The existing literature on college friendships, academic achievement, and student attrition is mixed, which may occur as a result of varying ways of defining friendship. This study adds to an understanding of these dynamics by examining both the number of close campus friends as well as the emotional connection students have with these friends within a large, multi-institutional sample. Multilevel analyses found that both of these attributes of campus friendships are positively and significantly related to six-year graduation rate, whereas only the number of close campus friends significantly predicts college grade point average.

scholarly journals PREDIKSI POLA KELULUSAN MAHASISWA MENGGUNAKAN TEKNIK DATA MINING CLASSIFICATION EMERGING PATTERN

Petir ◽  
2019 ◽  
Vol 12 (1) ◽  
Author(s):  
Ida Farida ◽  
Spits Warnars Harco Leslie Hendric
Keyword(s):  
Data Mining ◽  
Teaching And Learning ◽  
Graduation Rate ◽  
Large Data ◽  
University Management ◽  
Study Program ◽  
Student Graduation ◽  
The University ◽  
Process Strategy ◽  
Emerging Pattern

Mercu Buana University is one of the private universities in Indonesia, especially in DKI Jakarta, which has a large number of students and a number of graduations. However, the University management has difficulty predicting a pattern and graduation rate from existing student data in each academic year. Most researchers use data mining techniques to find a regularity of patterns or relationships set on large data. In this paper, to predict patterns and analyze student graduation rates researchers use data mining by focusing on the classification process using emerging pattern algorithms on the timeliness of student studies. In this study the data used came from combined data between student master data and graduation data. The results of testing the data carried out by researchers in the data mining application produce graduation patterns with various variations according to the learning attributes used, namely gender, class, study program, lecture system and student GPA. By using the results of testing this study, it is expected that the resulting data can help the management of the University as a basis for analysis in planning the teaching and learning process strategy to increase the graduation rate on time and as a support for the management of Mercu Buana University

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