Skripsi

PREDIKSI KELULUSAN MAHASISWA TEPAT WAKTU DENGAN METODE RANDOM FOREST BERDASARKAN ALGORITMA K-MEANS PADA PASCASARJANA UNIVERSITAS SRIWIJAYA

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Penilaian

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On-time graduation is a crucial indicator for assessing the quality of higher education institutions. At the Postgraduate School of Sriwijaya University, delayed graduations remain a serious concern. This research aims to predict on-time student graduation by combining the random forest method with the K-means algorithm. The K-means algorithm is used to cluster students based on specific parameters or scopes, such as GPA, IPS, number of SKS, semester, age, and employment status. The results of this clustering are then used as input to build a predictive model using the random forest method. This research employs a data mining approach, involving stages of data collection, preprocessing, clustering, modeling, and evaluation using a confusion matrix and ROC curve. Experimental results show that this combined method provides high predictive accuracy in determining on-time student graduation, achieving an accuracy of 97.93%. This research is expected to serve as a basis for strategic decision-making by the university to enhance educational efficiency and improve the on-time graduation rate.

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Availability

Inventory Code	Barcode	Call Number	Location	Status
2507003846	T177526	T1775262025	Central Library (Reference)	Available but not for loan - Not for Loan

Detail Information

Series Title

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Call Number

T1775262025

Publisher

Indralaya : Program Magister Ilmu Komputer, Fakultas Ilmu Komputer Universitas Sriwijaya., 2025

Collation

xii, 87 hlm.; ilus.; tab.; 29 cm.

Language

Indonesia

ISBN/ISSN

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Classification

004.07

Content Type

Text

Media Type

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Carrier Type

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Edition

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Subject(s)

Ilmu Komputer

Specific Detail Info

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Statement of Responsibility

MI

Other version/related

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