PENERAPAN METODE DISCRETIZATION DAN ADABOOST UNTUK MENINGKATKAN AKURASI ALGORITMA KLASIFIKASI DALAM MEMPREDIKSI PENYAKIT JANTUNG

Authors

DOI:

https://doi.org/10.21927/ijubi.v5i2.2689

Keywords:

Penyakit Jantung Discretization, Adaboost, Decision Tree C45, KNN

Abstract

Angka kematian yang disebabkan oleh penyakit jantung dapat dikurangi jika ada diagnosa yang akurat sejak dini. Penelitian sebelumnya dalam memprediksi penyakit jantung dengan tingkat akurasi telah dilakukan namun menghasilkan akurasi yang kecil pada algoritma Decision Tree C4.5 dan K-Nearest Neighbor (KNN). Untuk itu diperlukan adanya peningkatan akurasi agar menghasilkan keakuratan informasi. Tujuan penelitian ini adalah untuk meningkatkan akurasi dari algortima klasifikasi Decision Tree C4.5 dan K-Nearest Neighbor (KNN) menggunakan data heart disease dataset dari kaggle.com dengan menerapkan teknik discretization dan metode ensemble yaitu adaboost. Hasil penelitian ini dengan algoritma tunggal menghasilkan akurasi sebesar 89,17% pada Decision Tree dan 84,68% pada KNN, sedangkan Decision tree menggunakan teknik discretization dan adaboots sebesar 99,81% dan KNN menggunakan teknik discretization dan adaboots sebesar 92,88%. Hasil menunjukkan adanya peningkatan algortima klasifikasi menggunakan teknik discretization dan adaboots.

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Published

2022-12-31

Issue

Vol. 5 No. 2 (2022): Indonesian Journal of Business Intelligence (IJUBI)

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