Reduksi Dimensi Data menggunakan Metode Wrapper Sequential Feature Selection untuk Peningkatan Performa Algoritma Naïve Bayes terhadap Dataset Medis

Mochammad Yusa, Funny Farady Coastera, Muhammad Randa Yandika

Abstract


Penggunaan Machine Learning sebagai alat bantu dalam penanganan medis saat ini berkembang dengan pesat. Salah satu penyakit medis yang dikembangkan menggukan algoritma komputasi adalah Cardiovascullar Disease (CVD). Machine learning model yang diterapkan didasarkan dataset rekam medis. Tujuan penelitian ini adalah menginvestigasi performa algoritma naïve bayes dengan menerpakan metode Wrapper Sequential Feature Selection (WSFS). Metode penelitian dimulai dari pengumpulan dataset, data preprocessing, penerapan model Naïve Bayes, dan atribut scoring menggunakan Wrapper SFS, dan validasi performa menggunakan uji validasi 10-Fold Cross-Validation. Data history yang digunakan yaitu dataset Heart Failure Clinical Records yang terdiri dari 299 instances pada 13 features. Hasil penelitian menunjukkan bahwa metode Wrapper SFS dapat mengimprovisasi nilai performa Algoritma Naïve Bayes dari nilai akurasi, Precisi, dan Recall. Adapun kenaikan performa didapatkan dengan kombinasi 6 fitur ('anaemia', 'diabetes', 'ejection_fraction', 'serum_creatinine', 'gender', 'time') yang didapatkan dari seleksi fitur WSFS terhadap Algoritma tersebut yaitu nilai akurasi meningkat sebanyak 6,334%, skor recall meningkat 11,333%, dan nilai precision meningkat sebesar 20,07% dibandingkan dengan Algoritma Naïve Bayes.   


Keywords


Penyakit Kardiovaskuler; Machine Learning; Wrapper Sequential Feature Selection; Naïve Bayes

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References


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DOI: http://dx.doi.org/10.26418/jp.v8i2.54328

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