PREDIKSI KELULUSAN MAHASISWA PROGRAM STUDI MATEMATIKA UNIVERSITAS UDAYANA MENGGUNAKAN BACKPROPAGATION NEURAL NETWORK

Based on the rules of the BAN-PT Study Program Accreditation Instrument 4.0 (IAPS 4.0), one of the study program accreditation assessment indicators is the percentage of students who graduate on time. The percentage of Udayana University Mathematics Study Program students who graduated on time durin...

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Bibliographic Details
Published inE-jurnal matematika Vol. 12; no. 3; pp. 242 - 247
Main Authors NUR IHSAN, MUHAMMAD RIZKI, GANDHIADI, G.K., HARINI, LUH PUTU IDA
Format Journal Article
LanguageEnglish
Published Universitas Udayana 24.08.2023
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Summary:Based on the rules of the BAN-PT Study Program Accreditation Instrument 4.0 (IAPS 4.0), one of the study program accreditation assessment indicators is the percentage of students who graduate on time. The percentage of Udayana University Mathematics Study Program students who graduated on time during the graduation period from 2002 to 2019 was 52.5%. It can be seen that many students fail to complete their studies on time, which has an impact on the study program accreditation assessment. Based on these problems, this study aims to help academics increase the percentage of Udayana University Mathematics Study Program graduates by predicting the graduation of mathematics study program students using the backpropagation neural network method. This study uses data on alumni of students of the 2002-2017 mathematics study program. With the BNN 5-3-1 architecture, the predicted results of graduation for students of the Udayana University mathematics study program are 73%.
ISSN:2303-1751
2303-1751
DOI:10.24843/MTK.2023.v12.i03.p425