Skripsi

KLASIFIKASI SMS MENGGUNAKAN METODE SELEKSI FITUR MUTUAL INFORMATION (MI), ALGORITMA SUPPORT VECTOR MACHINE (SVM) DAN SMOTE

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Penilaian

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Communication media such as SMS (Short Message Service) is now rarely used, but SMS is still needed because it can still be useful for users in receiving information. However, problems arise due to fraudulent messages. Thus, SMS classification becomes important by using the Synthetic Minority Oversampling Technique (SMOTE) method which can balance unbalanced data, Mutual Information (MI) which is used to measure the relationship between words and classes, and Support Vector Machine (SVM) for classifiers that can separate classes. The data used in this research is divided into promo, fraud and normal. Test modelling was performed with a comparison of parameter C. The results showed that the use of the SMOTE + SVM model produced a higher accuracy value than the addition of MI, but the SMOTE + MI + SVM method produced a more stable performance than without the use of MI.

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Availability

Inventory Code	Barcode	Call Number	Location	Status
2407002617	T143655	T1436552024	Central Library (References)	Available but not for loan - Not for Loan

Detail Information

Series Title

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Call Number

T1436552024

Publisher

Indralaya : Prodi Teknik Informatika, Fakultas Ilmu Komputer Universitas Sriwijaya., 2024

Collation

xv, 61 hlm.; ilus.; 29 cm

Language

Indonesia

ISBN/ISSN

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Classification

004.07

Content Type

Text

Media Type

unmediated

Carrier Type

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Edition

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Subject(s)

Prodi Teknik Informatika

Specific Detail Info

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Statement of Responsibility

KA

Other version/related

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