scholarly journals Penerapan algoritma C5.0 pada analisis faktor-faktor pengaruh kelulusan tepat waktu mahasiswa Teknik Informatika UMM

Repositor ◽  
2020 ◽  
Vol 1 (2) ◽  
pp. 131
Author(s):  
Vinna Rahmayanti ◽  
Yufis Azhar ◽  
Andriani Eka Pramudita
Keyword(s):  
High School ◽  
Feature Selection ◽  
Grade Point Average ◽  
Engineering Students ◽  
Senior High School ◽  
Credit System ◽  
Grade Point ◽  
Academic Credit ◽  
Entry Status ◽  
School Status

AbstrakKelulusan tepat waktu mahasiswa merupakan salah satu permasalahan yang sulit untuk diatasi oleh setiap pihak perguruan tinggi, begitu pula pada jurusan Teknik Informatika Universitas Muhammadiyah Malang. Permasalahan ini harus segera diatasi mengingat kualitas mahasiswa akan mempengaruhi sebuah akreditasi perguruan tinggi maupun jurusan. Oleh karena itu, perlu dilakukan analisis faktor-faktor pengaruh kelulusan tepat waktu mahasiswa Teknik Informatika UMM. Penelitian ini menggunakan algoritma C5.0 untuk melakukan seleksi fitur penting dan analisis regresi untuk melakukan estimasi peluang kelulusan tepat waktu mahasiswa. Variabel bebas yang digunakan adalah jenis kelamin, asal daerah, status masuk, SKS semester 4, SKS semester 6, IP semester 2, IP semester 4, IP semester 6, IPK semester 2, IPK semester 4, IPK semester 6, jenis SMA, status SMA, pendidikan orang tua, dan pekerjaan orang tua. Hasil implementasi algoritma C5.0 pada penelitian ini mampu melakukan seleksi fitur dengan menghasilkan 8 dari total keseluruhan 15 fitur dengan nilai akurasi yang lebih baik dibandingkan nilai akurasi yang menggunakan keseluruhan fitur. Serta, penelitian ini mampu memberikan model regresi dengan nilai akurasi sebesar 82%.Abstract Timely graduation of college students is one of the problems that is difficult to overcome by each college, as well as in the Department of Informatics, University of Muhammadiyah Malang. This problem must be resolved immediately, considering the quality of students will affect the accreditation of university and its majors. So, it is necessary to analyze the factors that influence the timely graduation of Informatics Engineering students in UMM. This study uses the C5.0 algorithm to do feature selection and regression analysis to estimate the opportunities of timely graduation. The independent variables used are gender, regional origin, entry status, academic credit system in 4th semester, academic credit system in 6th semester, grade point of 2nd semester, grade point of 4th semester, grade point of 6th semester, grade point average of 2nd semester, grade point average of 4th semester, grade point average of 6th semester, type of senior high school, status of senior high school, parent’s education, and parent’s job. The results of the implementation of the C5.0 algorithm in this study were able to do feature selection by producing 8 out of total 15 features with better accuracy than the value of accuracy using all features. And this study is able to provide a regression model with an accuracy value of 82%.

scholarly journals Supervised Learning Applied to Graduation Forecast of Industrial Engineering Students

2022 ◽  
Vol 11 (1) ◽  
pp. 325-337
Author(s):  
Natalia Gil ◽  
Marcelo Albuquerque ◽  
Gabriela de
Keyword(s):  
Machine Learning ◽  
High School ◽  
Logistic Regression ◽  
Supervised Learning ◽  
Grade Point Average ◽  
Engineering Students ◽  
Learning Algorithm ◽  
Industrial Engineering ◽  
Machine Learning Algorithm ◽  
Grade Point

<p style="text-align: justify;">The article aims to develop a machine-learning algorithm that can predict student’s graduation in the Industrial Engineering course at the Federal University of Amazonas based on their performance data. The methodology makes use of an information package of 364 students with an admission period between 2007 and 2019, considering characteristics that can affect directly or indirectly in the graduation of each one, being: type of high school, number of semesters taken, grade-point average, lockouts, dropouts and course terminations. The data treatment considered the manual removal of several characteristics that did not add value to the output of the algorithm, resulting in a package composed of 2184 instances. Thus, the logistic regression, MLP and XGBoost models developed and compared could predict a binary output of graduation or non-graduation to each student using 30% of the dataset to test and 70% to train, so that was possible to identify a relationship between the six attributes explored and achieve, with the best model, 94.15% of accuracy on its predictions.</p>

scholarly journals Predicting Freshman Grade Point Average From College Admissions Test Scores and State High School Test Scores

AERA Open ◽  
2016 ◽  
Vol 2 (4) ◽  
pp. 233285841667060 ◽  
Author(s):  
Daniel Koretz ◽  
Carol Yu ◽  
Preeya P. Mbekeani ◽  
Meredith Langi ◽  
Tasmin Dhaliwal ◽  
...  
Keyword(s):  
High School ◽  
Grade Point Average ◽  
Test Scores ◽  
College Admissions ◽  
Grade Point ◽  
Admissions Test

scholarly journals Differential associations of neurobehavioral traits and cognitive ability to academic achievement in higher education

2019 ◽  
Author(s):  
Graham Pluck
Keyword(s):  
Higher Education ◽  
Academic Achievement ◽  
Psychological Distress ◽  
Grade Point Average ◽  
Engineering Students ◽  
Executive Dysfunction ◽  
Grade Point ◽  
Strong Correlate ◽  
University Level ◽  
Current Psychological

BackgroundPeople vary between each other on several neurobehavioral traits, which may have implications for understanding academic achievement.MethodsUniversity-level Psychology or Engineering students were assessed for neurobehavioral traits, intelligence, and current psychological distress. Scores were compared with their grade point average (GPA) data.ResultsFactors associated with higher GPA differed markedly between groups. For Engineers, intelligence, but not neurobehavioral traits or psychological distress, was a strong correlate of grades. For Psychologists, grades were not correlated with intelligence but they were with the neurobehavioral traits of executive dysfunction, disinhibition, apathy, and positive schizotypy. However, only the latter two were associated independently of psychological distress. Additionally, higher mixed-handedness was associated with higher GPA in the combined sample.ConclusionsNeurological factors (i.e., neurobehavioral traits and intelligence), are differentially associated with university-level grades, depending on the major studied. However, mixed-handedness may prove to be a better general predictor of academic performance across disciplines.

Predicting success in teacher education: Revisiting the influence of high school GPA, admission, and standardized test scores on academic and licensure performance

2020 ◽  
Vol 10 (2) ◽  
pp. 1-17
Author(s):  
Apler J. Bansiong ◽  
Janet Lynn M. Balagtey
Keyword(s):  
High School ◽  
Teacher Education ◽  
Grade Point Average ◽  
Test Scores ◽  
Standardized Test ◽  
Standardized Test Scores ◽  
Degree Programs ◽  
Licensure Examination ◽  
Grade Point ◽  
Admission Variables

This predictive study explored the influence of three admission variables on the college grade point average (CGPA), and licensure examination ratings of the 2015 teacher education graduates in a state-run university in Northern Philippines. The admission variables were high school grade point average (HSGPA), admission test (IQ) scores, and standardized test (General Scholastic Aptitude - GSA) scores. The participants were from two degree programs – Bachelor in Elementary Education (BEE) and Bachelor in Secondary education (BSE). The results showed that the graduates’ overall HSGPA were in the proficient level, while their admission and standardized test scores were average. Meanwhile, their mean licensure examination ratings were satisfactory, with high (BEE – 80.29%) and very high (BSE – 93.33%) passing rates. In both degree programs, all entry variables were significantly correlated and linearly associated with the CGPAs and licensure examination ratings of the participants. These entry variables were also linearly associated with the specific area GPAs and licensure ratings, except in the specialization area (for BSE). Finally, in both degrees, CGPA and licensure examination ratings were best predicted by HSGPA and standardized test scores, respectively. The implications of these findings on admission policies are herein discussed.

Evaluation of TAPS Packages

2010 ◽  
pp. 128-147
Author(s):  
Manjit Singh Sidhu
Keyword(s):  
Problem Solving ◽  
Learning Styles ◽  
Grade Point Average ◽  
Engineering Students ◽  
Target Population ◽  
Grade Point ◽  
Slow Learners ◽  
Undergraduate Engineering ◽  
The Third ◽  
Engineering Mechanics

The evaluation was carried out to examine the distribution of learning styles (discussed in Chapter 2) of the third year undergraduate engineering students and suggest effective problem solving approaches that could increase the motivation and understanding of slow learners at UNITEN. For this study, a sample target population of 60 third year undergraduate engineering students who had taken the Engineering Mechanics subject was tested. These students were selected based on their second year grade point average (GPA) of less than 2.5 as this study emphasizes on slow learners.

Working on Grade Point Average, test Accuracy, and Attendance of High School Students

1996 ◽  
Vol 78 (1) ◽  
pp. 41-42 ◽  
Author(s):  
Grant Lenarduzzi ◽  
T. F. McLaughlin
Keyword(s):  
High School ◽  
High School Students ◽  
Grade Point Average ◽  
Test Scores ◽  
Grade Point Averages ◽  
Test Accuracy ◽  
Lower Grade ◽  
School Students ◽  
Grade Point ◽  
School Year

The present analysis examined grade point averages (GPA), subject-matter test scores, and attendance for 274 students enrolled in a high school at the beginning of the 1992–1993 school year by the number of hours worked per week in the previous year (1991–92) and in the current school year (1992–1993). The over-all outcomes indicated that working fewer than 10 hours per week had small adverse effects on each measure. Students working from 10 to 20 hours per week had lower grade point averages and attendance. Students working over 20 hours per week had depressed test scores and grade point averages and more absences than other students who worked less or did not work.

Sign in / Sign up

Export Citation Format

Share Document