Skripsi

KLASIFIKASI PENYAKIT ASMA DENGAN MENGGUNAKAN SELEKSI FITUR ANOVA F-TEST DAN ALGORITMA MACHINE LEARNING

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This study discusses the classification of asthma disease using Machine Learning algorithms to address three research questions: the application of Machine Learning in asthma classification, the effect of different dataset conditions, and the algorithm that produces the best performance. The asthma dataset obtained from Kaggle was processed under three scenarios: original, undersampling, and Synthetic Minority Over-sampling Technique (SMOTE). Feature selection was performed using the ANOVA F-Test method. The classification models were evaluated using K-Nearest Neighbor (KNN), Support Vector Machine (SVM), and Random Forest algorithms. The research process included data preprocessing, data balancing, data splitting, hyperparameter tuning, and performance evaluation using accuracy, precision, recall, and F1-score. The results show that the SMOTE scenario provides the best performance, with the Random Forest model (n=100) achieving an accuracy of 98%. In conclusion, data balancing techniques and feature selection using ANOVA F-Test significantly influence classification performance, and Random Forest with SMOTE is the most optimal model for asthma detection. Keywords: Asthma, Machine Learning, ANOVA F-Test, KNN, SVM, Random Forest, SMOTE, Classification.

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Detail Information

Series Title

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Call Number

T1899882025

Publisher

Indralaya : Prodi Teknik Informatika, Fakultas Ilmu Komputer Universitas Sriwijaya., 2025

Collation

xvii, 123 hlm.; ilus.; tab.; 29 cm.

Language

Indonesia

ISBN/ISSN

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Classification

616.230 7

Content Type

Text

Media Type

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Carrier Type

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Edition

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Subject(s)

Penyakit Asma
Prodi Teknik Informatika

Specific Detail Info

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Statement of Responsibility

MI

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