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SELEKSI FITUR KLASIFIKASI PENYAKIT INFARK MIOKARD MENGGUNAKAN GENETIC ALGORITHM DAN SUPPORT VECTOR MACHINE

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In this study, the feature selection method employed is the Genetic Algorithm (GA), while the classification method used is the Support Vector Machine (SVM), with the aim of improving the performance of myocardial infarction disease classification. The study utilizes the Myocardial Infarction Complications dataset, which consists of 123 features. The research process includes several preprocessing stages, namely missing value handling, data normalization using Min–Max Scaling, data balancing using the SMOTE–Tomek Links method, and feature selection using the Genetic Algorithm. Subsequently, classification is performed using a Support Vector Machine with a linear kernel. Model performance is evaluated using accuracy, precision, recall, and F1-score. The experimental results demonstrate that the application of SMOTE–Tomek Links and the Genetic Algorithm improves classification performance while reducing the number of features used in the classification process. The proposed classification model achieves an accuracy of 97%, indicating that the combination of the Genetic Algorithm and Support Vector Machine is effective for the classification of myocardial infarction disease.

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Availability

Detail Information

Series Title

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Call Number

T2012862026

Publisher

Indralaya : Prodi Sistem Komputer, Fakultas Ilmu Komputer Universitas Sriwijaya., 2026

Collation

xiv, 81 hlm.; ilus.; tab.; 29 cm.

Language

Indonesia

ISBN/ISSN

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Classification

006.307

Content Type

Text

Media Type

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Carrier Type

-

Edition

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Subject(s)

Algoritma genetika
Prodi Sistem Komputer

Specific Detail Info

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Statement of Responsibility

MI

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