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

PAKPAHAN, NUR ZAHIRA INDRAYATI and Utami, Alvi Syahrini and Rodiah, Desty (2024) KLASIFIKASI SMS MENGGUNAKAN METODE SELEKSI FITUR MUTUAL INFORMATION (MI), ALGORITMA SUPPORT VECTOR MACHINE (SVM) DAN SMOTE. Undergraduate thesis, Sriwijaya University.

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Abstract

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.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	SMS, Synthetic Minority Oversampling, Mutual Information, Support Vector Machine
Subjects:	Q Science > Q Science (General) > Q334-342 Computer science. Artificial intelligence. Algorithms. Robotics. Automation.
Divisions:	09-Faculty of Computer Science > 55201-Informatics (S1)
Depositing User:	Nur Zahira Indrayati Pakpahan
Date Deposited:	03 May 2024 01:19
Last Modified:	03 May 2024 01:19
URI:	http://repository.unsri.ac.id/id/eprint/143655

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