PEMODELAN PREDIKSI DAN STRATIFIKASI RESIKO SERANGAN JANTUNG DENGAN RANDOM FOREST DAN LOGISTIC REGRESSION BERBASIS CROSS-INDUSTRY STANDARD PROCESS FOR DATA MINING
DOI:
https://doi.org/10.31849/9g59ms76Keywords:
Serangan Jantung, CRISP-DM, Machine Learning, Random Forest, Logistic RegressionAbstract
Penelitian ini bertujuan mengembangkan model prediktif untuk klasifikasi dan stratifikasi risiko serangan jantung menggunakan pendekatan machine learning dalam kerangka kerja CRISP-DM. Data klinis yang digunakan berasal dari 1.319 pasien dengan atribut meliputi usia, jenis kelamin, detak jantung, kadar Creatine Kinase-MB (CK-MB), dan kadar Troponin. Dua algoritma klasifikasi, yaitu Random Forest dan Logistic Regression, diterapkan dan dievaluasi menggunakan pembagian data uji sebesar 20%. Hasil pengujian menunjukkan bahwa model Random Forest memberikan performa terbaik dengan tingkat akurasi sebesar 98,48%, sedangkan Logistic Regression memperoleh akurasi sebesar 81,06%. Analisis pentingnya fitur mengungkapkan bahwa kadar CK-MB dan Troponin merupakan faktor prediktif paling berpengaruh dalam menentukan risiko serangan jantung. Temuan ini menunjukkan bahwa penerapan model prediktif yang dikembangkan secara sistematis melalui metodologi CRISP-DM memiliki potensi besar untuk mendukung sistem pendukung keputusan klinis dalam deteksi dini serangan jantung. Penelitian selanjutnya disarankan untuk melakukan validasi eksternal menggunakan dataset dari populasi yang lebih beragam guna meningkatkan generalisasi model serta meningkatkan keandalan, akurasi, dan robustitas hasil prediksi secara menyeluruh.
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