Implementasi Optimasi Hyperparameter GridSearchCV Pada Sistem Prediksi Serangan Jantung Menggunakan SVM
DOI:
https://doi.org/10.26594/teknologi.v13i1.3098Abstract
Serangan jantung disebabkan oleh penyumbatan atau penyempitan pada pembuluh darah secara tiba-tiba sehingga aliran darah yang mengalir ke jantung terjadi gangguan. Jika aliran darah berkurang, penderita akan merasakan nyeri dada sehingga itu pertanda atau peringatan seseorang dapat terkena serangan jantung. Salah satu cara efektif dalam mengidentifikasi dan memprediksi penyakit jantung adalah dengan memanfaatkan algoritma machine learning. Pada penelitian terdahulu banyak sistem prediksi serangan jantung menggunakan alogaritma naïve bayes. Tujuan dari penelitian ini adalah mengimplementasikan alogaritma Support Vector Machine (SVM) untuk memprediksi kemungkinan terkena serangan jantung dengan menggunakan model klasifikasi dari dua kelas yaitu kelas kemungkinan kecil terkena serangan jantung dan kelas kemungkinan besar terkena serangan jantung. Teknik data mining digunakan untuk pengolahan data untuk mencari pengetahuan baru dari basis data sehingga ditemukan pola-pola yang valid, bermanfaat dan dapat dipelajari dengan mudah. Penelitian ini menggunakan teknik optimasi hiperparameter GridSearchCV pada model klasifikasi SVM dan pengujian akurasi yang dilakukan menggunakan Confusion Matrix. Dari 302 total jumlah data, 211 data digunakan sebagai data training, 45 data digunakan sebagai data uji dan 46 data digunakan data validasi, sistem prediksi serangan jantung bekerja dengan performa yang baik karena memiliki nilai accuracy sebesar 86,0%, precision sebesar 84,0%, recall sebesar 91,0%, dan f1-score sebesar 87,0%.
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