relation: https://eprints.untirta.ac.id/45962/
title: APLIKASI EVALUASI PEMBELAJARAN MAHASISWA JURUSAN TEKNIK INDUSTRI UNIVERSITAS SULTAN AGENG TIRTAYASA BERBASIS MACHINE LEARNING
creator: Hamidah, Dhini
subject: Q Science (General)
subject: QA75 Electronic computers. Computer science
description: Student evaluation is an important stage in the process of assessing students and majors in higher education. Evaluation of students in higher education can be seen from the level at which students complete their studies. During the 3 academic years starting from 2019-2020 to 2021-2022 at the Department of Industrial Engineering, Sultan Ageng Tirtayasa University, there was a tendency for the rate of students graduating not on time to increase. Based on this, to reduce the upward trend, an application was designed as a student evaluation tool that is able to motivate students to plan academic and non-academic processes better so as to prevent delays in graduation which can be detrimental to both students and the university and maintain accreditation in the future. The aim of this research is to obtain an algorithm that produces the highest ROC-AUC score is Random Forest to design a student evaluation application for student graduation. This research uses the Google Collaborator editor's Python programming language with Machine Learning stages and the Streamlit framework. The results of the research are an algorithm with the highest ROC-AUC value in semester 7 is Random Forest where the algorithm for designing student evaluation applications is named "GoLulus".
date: 2025-02-05
type: Thesis
type: NonPeerReviewed
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identifier: https://eprints.untirta.ac.id/45962/20/Dhini%20Hamidah_3333190037_Fulltext.pdf
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identifier: https://eprints.untirta.ac.id/45962/21/Dhini%20Hamidah_3333190037_Lamp.pdf
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identifier: https://eprints.untirta.ac.id/45962/22/Dhini%20Hamidah_3333190037_Ref.pdf
identifier:   Hamidah, Dhini  (2025) APLIKASI EVALUASI PEMBELAJARAN MAHASISWA JURUSAN TEKNIK INDUSTRI UNIVERSITAS SULTAN AGENG TIRTAYASA BERBASIS MACHINE LEARNING.  S1 thesis, Fakultas Teknik Universitas Sultan Ageng Tirtayasa.