(SISFOKOM - S3) - Putri, Febriawan, Hasan [2024-02-15]

Hasan, Firman Noor (2024) (SISFOKOM - S3) - Putri, Febriawan, Hasan [2024-02-15]. SISFOKOM: Sistem Informasi dan Komputer), 13 (1). pp. 39-47. ISSN 2581-0588

[thumbnail of 1.  Cover.pdf]
Preview
Text
1. Cover.pdf

Download (375kB) | Preview
[thumbnail of 2.  Editorial Team.pdf]
Preview
Text
2. Editorial Team.pdf

Download (113kB) | Preview
[thumbnail of 3.  Daftar Isi.pdf]
Preview
Text
3. Daftar Isi.pdf

Download (309kB) | Preview
[thumbnail of 4.  Artikel.pdf]
Preview
Text
4. Artikel.pdf

Download (524kB) | Preview
[thumbnail of 5.  Bukti Hasil Turnitin.pdf]
Preview
Text
5. Bukti Hasil Turnitin.pdf

Download (3MB) | Preview
[thumbnail of 6.  LOA - Putri, Febriawan, Hasan.pdf]
Preview
Text
6. LOA - Putri, Febriawan, Hasan.pdf

Download (109kB) | Preview
Official URL: https://jurnal.atmaluhur.ac.id/index.php/sisfokom/...

Abstract

Graduating on time is what every student wants to accomplish in college. Students of Prof. Dr. Hamka Muhammadiyah University are one of those who have this dream. Based on 2020 graduates data from the Tracer Study, 60% said the university had a high enough impact on improving competence. This data indicates that university needs to evaluate improvement of academic quality. Often, students have difficulty finding information about important factors that support achieving timely graduation. A prediction analysis is needed to provide information about the student's graduation study period. For this analysis, data mining is implemented using the classification function of the decision tree (C4.5) algorithm with RapidMiner tools. The methodology for implementing data mining follows the stages of Knowledge Discovery In Database (KDD), beginning with data collection, preprocessing, transformation, data mining, and evaluation. The research findings consist of visualization and decision tree rules that reveal GPA as the most influential factor in determining a student's study period.There is other information, namely, students graduated on time (less than equal to 4 years) amounted to 170 or 54.5% and students did not graduate on time (more than 4 years) amounted to 142 or 45.6%. Testing the performance of decision tree (C4.5) utilizing confusion matrix through RapidMiner tools, resulted in accuracy reaching 83.87%, with precision of 87.50% and recall of 91.18%. Provides evidence that the decision tree algorithm (C4.5) has optimal performance to provide valuable information about predicting student graduation in order to increase student enrollment with the right study period.

Item Type: Article
Subjects: T Technology > T Technology (General)
Divisions: Fakultas Teknik > Teknik Informatika
Depositing User: Mr Firman Noor Hasan
Date Deposited: 20 Feb 2024 06:53
Last Modified: 20 Feb 2024 06:53
URI: http://repository.uhamka.ac.id/id/eprint/33307

Actions (login required)

View Item View Item