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

HANNA, NUR and Heroza, Rahmat Izwan (2022) 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. Undergraduate thesis, Sriwijaya University.

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Abstract

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.

Item Type: Thesis (Undergraduate)
Uncontrolled Keywords: Data mining, Prediksi, Naïve bayes, Decision Tree, Support Vector Machine, Waktu Kelulusan Mahasiswa
Subjects: Q Science > QA Mathematics > QA75-76.95 Calculating machines > QA76.9.B45 Big data. Machine learning. Quantitative research. Metaheuristics.
Q Science > QA Mathematics > QA75-76.95 Calculating machines > QA76.9.D343 Data mining. Database searching. Big data.
Q Science > QA Mathematics > QA8.9-QA10.3 Computer science. Artificial intelligence. Computational complexity. Data structures (Computer scienc. Mathematical Logic and Formal Languages
Divisions: 09-Faculty of Computer Science > 57201-Information Systems (S1)
Depositing User: Nur Hanna
Date Deposited: 23 Nov 2022 05:23
Last Modified: 23 Nov 2022 05:23
URI: http://repository.unsri.ac.id/id/eprint/82566

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