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      Metode CART untuk Analisis Kolektibilitas Pembayaran Kredit PT. N.

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      Date
      2014
      Jenis/Type
      Undergraduate Thesis
      Author
      Sholihat, Kurnia
      Alamudi, Aam
      Sulvianti, Itasia Dina
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      Abstract
      Kredit merupakan salah satu bentuk bantuan keuangan yang diberikan baik oleh perbankan maupun non perbankan kepada perorangan atau badan usaha yang harus dikembalikan dalam jangka waktu tertentu. PT. N merupakan perusahaan atau badan usaha milik pemerintah daerah (pemda) Kabupaten Siak, Provinsi Riau yang bergerak di bidang pemberian kredit, yaitu Kredit Pembiayaan Modal Kerja (KPMK). PT. N sebagai pihak pemberi kredit memiliki resiko kredit. Resiko kredit akan semakin besar seiring dengan semakin banyaknya kredit yang disalurkan. Resiko kredit pada dasarnya dapat diperkecil dengan pemilihan nasabah yang tepat. Permasalahan untuk memilih nasabah yang tepat ini dapat diselesaikan dengan menggunakan teknik data mining. Salah satu metode klasifikasi dalam data mining adalah CART (Classification and Regression Tree). Peubah yang berpengaruh adalah lama angsuran, besar pinjaman, profesi, sektor ekonomi, dan pekerjaan pasangan. Metode ini memiliki tingkat keakuratan prediksi sebesar 71.5%.
       
      Credit is a form of financial assistance given by both the banking and non banking to individuals or entities that should be returned within a specified period. PT . N is a company which is owned by local governments of Siak, Riau province engaged in the provision of credit, namely Kredit Pembiayaan Modal Kerja (KPMK). PT . N as the lender has credit risk. Credit risk will be greater as more and more loans distributed. Credit risk can be minimized by basically selecting the right customers. The problem to select the right customers can be solved using data mining techniques. One method of classification in data mining is the CART (Classification and Regression Tree). Variables that influence are the number of installments, the loan amount, professions, economic sectors ,and partner’s professions. This method has a prediction accuracy rate of 71.5 %. Keywords : CART, collectibility, KPMK
       
      URI
      http://repository.ipb.ac.id/handle/123456789/72682
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      • UF - Statistics and Data Sciences [2260]

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