IDENTIFIKASI DINI MAHASISWA BERPOTENSI DROP-OUT DENGAN METODE MACHINE LEARNING (STUDI KASUS: UNIVERSITAS PEMBANGUNAN NASIONAL "VETERAN" JAKARTA)

Salsabila Faiha Puteri, . (2024) IDENTIFIKASI DINI MAHASISWA BERPOTENSI DROP-OUT DENGAN METODE MACHINE LEARNING (STUDI KASUS: UNIVERSITAS PEMBANGUNAN NASIONAL "VETERAN" JAKARTA). Skripsi thesis, Universitas Pembangunan Nasional Veteran Jakarta.

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Abstract

One of the criteria for determining the success of an educational institution, particularly a university, is student performance. The better the student performance, the better the quality of the university. The number of drop-out students is a significant indicator in assessing the quality of a university. The earlier potential drop-out students are detected, the quicker interventions can be implemented, thus minimizing the occurrence of student drop-outs. Therefore, there is a need for a system to predict potential drop-out students at a university to improve both student quality and university quality. Efforts in early identification of potential drop-out students also help prevent a surge in drop-outs in subsequent academic years. Based on observations conducted at Universitas Pembangunan Nasional “Veteran” Jakarta, there were 565 drop-out students from 2015 to 2022. Consequently, this research aims to develop a system that can perform early identification of potential drop-out students at Universitas Pembangunan Nasional “Veteran” Jakarta. The study employs machine learning methods using the KNN and Naïve Bayes algorithms. Additionally, this research develops a dashboard system to provide informative visualizations of the early identification data of potential drop-out students.

Item Type: Thesis (Skripsi)
Additional Information: [No.Panggil: 2010512138] [Pembimbing 1: Bambang Saras Yulistiawan] [Pembimbing 2: M. Octaviano Pratama] [Penguji 1: Tjahjanto] [Penguji 2: Sarika]
Uncontrolled Keywords: drop out, machine learning, KNN, Naïve Bayes
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Q Science > QA Mathematics > QA76 Computer software
Divisions: Fakultas Ilmu Komputer > Program Studi Sistem Informasi (S1)
Depositing User: SALSABILA FAIHA PUTERI
Date Deposited: 19 Sep 2024 06:50
Last Modified: 19 Sep 2024 06:50
URI: http://repository.upnvj.ac.id/id/eprint/31729

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