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

Jeffri Prayitno Bangkit Saputra, Retno Waluyo

Abstract


Academic evaluation and graduation of students are critical components of an academic information system's (AIS) effectiveness since they allow for the measurement of student learning progress. Additionally, the assessment stating whether the student passed or failed would benefit both the student and teacher by acting as a reference point for future performance suggestions and evaluations. Using Decision Tree C4.5, a comprehensive analysis of the student academic evaluation approach was conducted. Age, gender, public or private high school status, high school department, organization activity, age at high school admission, progress GPA (pGPA), and total GPA (tGPA) were all documented and evaluated from semester 1–4 utilizing three times the graduation criterion periods. The article's scope is confined to undergraduate programs. An accuracy algorithm (AC) with a performance accuracy of 79.60 percent, a true positive rate (TP) of 77.70 percent, and 91 percent quality training data achieved the highest performance accuracy value.


Article Metrics

Abstract: 246 Viewers PDF: 122 Viewers

Keywords


Data Mining; C4.5; Education; Graduation Prediction

Full Text:

PDF


Refbacks

  • There are currently no refbacks.



Barcode

Journal of Applied Data Sciences

ISSN : 2723-6471 (Online)
Organized by : Computer Science and Systems Information Technology, King Abdulaziz University, Kingdom of Saudi Arabia.
Website : http://bright-journal.org/JADS
Email : taqwa@amikompurwokerto.ac.id (principal contact)
    support@bright-journal.org (technical issues)

 This work is licensed under a Creative Commons Attribution-ShareAlike 4.0