EVALUASI PENENTUAN KELAYAKAN KREDIT DENGAN ALGORITMA NAÏVE BAYES CLASSIFIER BERBASIS PARTICLE SWARM OPTIMIZATION : STUDI KASUS PADA BANK MAYAPADA MITRA USAHA CABANG PGC

EVALUASI PENENTUAN KELAYAKAN KREDIT DENGAN ALGORITMA NAÏVE BAYES CLASSIFIER BERBASIS PARTICLE SWARM OPTIMIZATION : STUDI KASUS PADA BANK MAYAPADA MITRA USAHA CABANG PGC

• By Nuraeni, Nia;
• Tesis
• 420

Algoritma data mining klasifikasi banyak digunakan untuk menentukan kelayakan
kredit salah satunya Naive Bayes Classifier, NBC unggul dalam peningkatan nilai
akurasi yang tinggi tetapi lemah dalam seleksi atribut. Penelitian ini menguraikan
langkah-langkah yang menggunakan algoritma PSO untuk membobot atribut atau
menseleksi atribut sehingga akurasi NBC menjadi lebih akurat. Setelah dilakukan
pengujian dengan dua model yaitu model algoritma Naive Bayes Classifier dan
algoritma Naive Bayes Classifier berbasis PSO maka hasil yang didapat adalah
algoritma Naive Bayes Classifier menghasilkan akurasi sebesar 89.33% dan nilai
AUC 0.955 sedangkan untuk algoritma Naive Bayes Classifier berbasis PSO nilai
akurasinya sebesar 91.67% dengan nilai AUC 0.952. Selisih nilai akurasi sebesar
2.34% dan selisih untuk nilai AUC adalah 0.003

• Katakunci Analisis Kredit, Naive Bayes Classifier Algorithm, PSO
• URL http://
• Penerbit ,

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