Penerapan Hyperparameter Tuning pada Model Klasifikasi untuk Prediksi Risiko Penyakit Jantung
Implementation of Hyperparameter Tuning for Classification Models in Heart Disease Risk Prediction
DOI:
https://doi.org/10.57152/malcom.v5i4.2138Keywords:
Algoritma Klasifikasi, Deteksi Dini, Hyperparameter Tuning, Machine Learning, Penyakit JantungAbstract
Penyakit jantung adalah penyebab utama kematian di seluruh dunia, sehingga penting untuk melakukan deteksi dini secara tepat untuk menurunkan angka kematian. Tantangan utama dalam penelitian ini adalah bagaimana cara meningkatkan efektivitas model klasifikasi dalam mendeteksi penyakit jantung. Tujuan dari penelitian ini adalah untuk membandingkan kinerja beberapa algoritma klasifikasi dan menilai dampak hyperparameter tuning terhadap peningkatan akurasi prediksi. Metode yang digunakan mencakup penerapan algoritma Logistic Regression, Decision Tree, Support Vector Machine (SVM), dan K-Nearest Neighbor (K-NN) pada dataset Cleveland Clinic Heart Disease yang diambil dari Kaggle. Proses hyperparameter tuning dilaksanakan dengan menggunakan gridsearchCV dan randomizedsearchCV bersama dengan cross-validation. Temuan penelitian menunjukkan bahwa setelah dilakukan tuning, logistic regression, K-NN, dan SVM mencapai akurasi tertinggi yang sama, yaitu 84%. Decision tree berada di posisi terendah dengan akurasi 80%. Selain itu, nilai precision, recall, dan F1-score juga meningkat, terutama pada logistic regression dan K-NN yang menunjukkan hasil paling seimbang. Hasil ini membuktikan bahwa hyperparameter tuning sangat membantu dalam meningkatkan kinerja model klasifikasi dan mendukung penggunaan machine learning untuk deteksi dini penyakit jantung secara lebih efektif.
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Copyright (c) 2025 Siti Kania Nur Alya Putri, Indah Jumiatin, Indri Sulistia, Novan Alkaf Bahraini Saputra, Nuruddin Wiranda
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