Skripsi

PERBANDINGAN METODE SUPPORT VECTOR MACHINE DAN RANDOM FOREST DALAM KLASIFIKASI WAKTU LULUS MAHASISWA UNIVERSITAS SRIWIJAYA.

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Accreditation is a benchmark in assessing the quality and feasibility of universities. One of the accreditation assessments in universities is the percentage of student graduation. So, the percentage of late graduation and Drop Out (DO) can affect the assessment. Therefore, there is a need for a technique in classifying student graduation time that can help provide recommendations for making policies and preventing students from being late in their studies. This research aims to compare the performance of the Support Vector Machine and Random Forest methods. Both methods produce accuracy that can be used as a reference in comparing the performance of the two methods. The data used in this research is 243 data of Informatics Engineering students of Sriwijaya University consisting of 102 data on time and 141 data of students who did not graduate on time. The results of this study indicate that the accuracy of the Support Vector Machine method obtained the highest average accuracy value of 0.79 on the RBF kernel with parameter configuration C = 10, while Random Forest obtained the highest average accuracy value of 0.77 on the N-estimator parameter configuration = 50.

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Availability

Detail Information

Series Title

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Call Number

T1571602024

Publisher

Indralaya : Prodi Sistem Informasi, Fakultas Ilmu Komputer Universitas Sriwijaya., 2024

Collation

xviii, 151 hlm.; ilus.; 29 cm

Language

Indonesia

ISBN/ISSN

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Classification

006.310 7

Content Type

Text

Media Type

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Carrier Type

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Edition

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Subject(s)

Prodi Sistem Informasi
Pembelajaran Mesin

Specific Detail Info

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Statement of Responsibility

TUTI

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