Analisa Perbandingan Algoritma Classification Berdasarkan Komposisi Label
DOI:
https://doi.org/10.29407/inotek.v8i1.4906Abstract
Di Indonesia, salah satu penyebab kematian dan gangguan neurologis paling umum adalah stroke. Tujuan penelitian ini adalah untuk membandingkan kinerja algoritma klasifikasi sebelum dan sesudah balance serta cross validation dalam mengklasifikasi penyakit stroke menggunakan dataset predict-stroke dari Kaggle. Random Forests, KNN, Naive Bayes, Decision Trees, SVM, Neural Networks, dan Logistic Regression adalah algoritma yang diuji. Pada penelitian ini, metode percobaan (eksperimen) digunakan. Langkah-langkah pemrosesan termasuk preprocessing, pembagian data untuk membedakan data pelatihan dan pengujian, dan evaluasi dengan cross-validation. nilai yang akan dinilai adalah akurasi, presisi, recall, dan F1. Hasilnya menunjukkan bahwa algoritma Random Forest menunjukkan tingkat akurasi tertinggi 94% setelah cross-validation dan penyeimbangan data. Meskipun, karena proses penyeimbangan, akurasi berkurang, Random Forest tetap memiliki performa yang baik dalam klasifikasi.
Keywords:
Data Mining, Algoritma Klasifikasi, Cross Validation##plugins.themes.default.displayStats.downloads##
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