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KLASIFIKASI KATEGORI WAKTU KELULUSAN MAHASISWA MENGGUNAKAN DATA AKADEMIK SEBAGAI UPAYA PERINGATAN DINI BAGI MAHASISWA AKTIF MENGGUNAKAN ALGORITMA DECISION TREE, NAÏVE BAYES DAN SUPPORT VECTOR MACHINE STUDI KASUS : JURUSAN SISTEM INFORMASI UNIVERSITAS SRIWIJAYA

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Penilaian

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Graduating on time is one of the indicators of student and campus success. Students are expected to graduate on time in 4 years or less. In real practice, students are not always able to complete undergraduate education within four years, this needs to be evaluated, but there is no valid data to determine the cause of the graduation rate that is not on time Understanding students who graduate on time is very important for educators or educators. campus for early warning efforts that can lead to student success in the long term so that timely graduation can be improved. Using academic data, classification will be carried out to determine the category of student study period. The algorithms used are Decision Tree, Naïve Bayes and Support Vector Machine (SVM). Cross-Industry Standard Process for Data Mining (CRISP-DM) is a standard that has been developed that is used to assist the analysis process of an industry as a problem solving strategy for companies or research departments.

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Availability

Inventory Code	Barcode	Call Number	Location	Status
2207004617	T82566	T825662022	Central Library (Referens)	Available but not for loan - Not for Loan

Detail Information

Series Title

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Call Number

T825662022

Publisher

Inderalaya : Prodi Sistem Informasi, Fakultas Ilmu Komputer, Universitas Sriwijaya., 2022

Collation

xii, 59 hlm.; ilus.; 29 cm

Language

Indonesia

ISBN/ISSN

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Classification

005.707

Content Type

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Media Type

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Carrier Type

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Edition

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Subject(s)

Prodi Sistem Informasi
Data dalam sistem-sistem informasi

Specific Detail Info

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Statement of Responsibility

SEPTA

Other version/related

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