Prediksi Ketepatan Waktu Kelulusan Mahasiswa Universitas Airlangga Berdasarkan Performa Empat Semester Pertama

Heriqbaldi, Hemakesha Ramadhani (2024) Prediksi Ketepatan Waktu Kelulusan Mahasiswa Universitas Airlangga Berdasarkan Performa Empat Semester Pertama. Project Report. [s.n.], [s.l.]. (Unpublished)

Text 5025201209-Project_Report.pdf - Accepted Version Download (2MB)

Abstract

Kelulusan tepat waktu mahasiswa adalah faktor krusial bagi mahasiswa dan universitas. Ketepatan waktu kelulusan mahasiswa merupakan salah satu tolak ukur terpenting bagi universitas serta menjadi indikator penting dalam mengukur efektivitas program universitas. Tugas yang dikerjakan saat melakukan Kerja Praktik adalah pengembangan model prediksi ketepatan waktu kelulusan mahasiswa. Dimana model dapat memprediksi seorang mahasiswa dapat lulus tepat waktu atau tidak berdasarkan data empat semester pertama.
Model prediksi dibuat dengan menggunakan algoritma XGBoost dengan menggunakan beberapa fitur, yaitu terdiri dari data performa akademik mahasiswa selama empat semester pertama. Pengamatan pengaruh fitur terhadap model prediksi juga dilakukan menggunakan nilai SHAP.
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Students' on-time graduation is a crucial factor for both students and universities. Timeliness of student graduation is one of the most important benchmarks for universities and an important indicator in measuring the effectiveness of university programs. The task undertaken when doing Practical Work is the development of a prediction model for the timeliness of student graduation. Where the model can predict a student can graduate on time or not based on data from the first four semesters. The prediction model is made using the XGBoost algorithm using several features, which consist of student academic performance data for the first four semesters. Observation of the influence of features on the prediction model is also carried out using the SHAP value.

Item Type:	Monograph (Project Report)
Uncontrolled Keywords:	Python, Prediksi, XGBoost, Kelulusan Mahasiswa, Python, Prediction, XGBoost, Student Graduation
Subjects:	L Education > L Education (General) T Technology > T Technology (General) > T57.5 Data Processing
Divisions:	Faculty of Intelligent Electrical and Informatics Technology (ELECTICS) > Informatics Engineering > 55201-(S1) Undergraduate Thesis
Depositing User:	Heriqbaldi Hemakesha Ramadhani
Date Deposited:	30 Jul 2024 04:07
Last Modified:	30 Jul 2024 04:07
URI:	http://repository.its.ac.id/id/eprint/110170

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