Identifikasi Donor Darah Berulang menggunakan Teknik Klasifikasi Machine Learning
DOI:
https://doi.org/10.26418/jp.v11i2.91772Keywords:
Donor Darah Berulang, Machine Learning, Support Vector Classifier, Klasifikasi, Prediksi Donor DarahAbstract
Donor darah berulang memainkan peran vital dalam menjaga stabilitas ketersediaan darah dalam sistem kesehatan. Untuk itu, penting memahami pola dan karakteristik individu yang cenderung melakukan donor darah berulang. Proses identifikasi ini merupakan masalah klasifikasi, yaitu menentukan apakah seseorang termasuk kategori donor berulang atau tidak, berdasarkan data historis mereka. Dalam konteks ini, teknik klasifikasi menjadi pendekatan yang relevan karena mampu mengelompokkan individu berdasarkan fitur-fitur tertentu. Penelitian ini menerapkan berbagai algoritma machine learning untuk membangun model klasifikasi donor darah berulang, termasuk Logistic Regression, Support Vector Classifier (SVC), Random Forest, Decision Tree, dan Multi-Layer Perceptron (MLP). Hasil evaluasi menunjukkan bahwa SVC memberikan performa terbaik dengan akurasi 98%, precision 89%, recall 92%, dan f1-score 89%. Dengan demikian, model machine learning, khususnya SVC, terbukti efektif dalam mengklasifikasikan donor darah berulang dan dapat menjadi alat bantu strategis bagi lembaga donor darah dalam meningkatkan efisiensi rekrutmen dan retensi donor. Optimalisasi lanjutan seperti balancing data, pemilihan fitur, dan tuning hyperparameter direkomendasikan untuk meningkatkan performa model.References
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