Skripsi

PERBANDINGAN ALGORITMA KLASIFIKASI DALAM MENGANALISIS KONSISTENSI PEMILIHAN JURUSAN KULIAH BERDASARKAN PENJURUSAN SMA MENGGUNAKAN METODE K-NEAREST NEIGHBOR DAN SUPPORT VECTOR MACHINE

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Choosing a college major that is consistent with a student's high school background is a crucial factor in supporting academic achievement and career preparation. This study focuses on a comparative analysis of the Support Vector Machine (SVM) and K-Nearest Neighbor (KNN) algorithms in evaluating the consistency of college major selection. This study used processed data from 636 students for analysis. Model evaluation was conducted using the 5-Fold Cross Validation method, where the data was split several times into training and testing data to obtain consistent and unbiased results. The results showed that SVM demonstrated higher effectiveness, achieving an average score across precision, recall, F1 score, and accuracy of 85%. Meanwhile, KNN obtained an average performance score of 78%. These findings highlight that SVM provides better performance in analyzing the consistency between students' high school majors and their chosen college majors. These findings also contribute to the development of decision support systems and counseling services to guide students in making more informed major choices.

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Detail Information

Series Title

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Call Number

T1895852025

Publisher

Indralaya : Prodi Sistem Informasi, Fakultas Ilmu Komputer Universitas Sriwijaya., 2025

Collation

xv, 104 hlm.; ilus.; tab.; 29 cm.

Language

Indonesia

ISBN/ISSN

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Classification

006.350 7

Content Type

Text

Media Type

-

Carrier Type

-

Edition

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Subject(s)

Prodi Sistem Informasi
Analisis Sintimen--Analisis Data

Specific Detail Info

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Statement of Responsibility

MI

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