Detail Membangun Credit Scoring Untuk Menilai Kelayakan Pemberian Fasilitas Kredit Perumahan (Mortgage Loan) Dengan Model Decision Tree & PCA, Studi Kasus Pada Salah Satu Bank Umum Komersial Di Indonesia Bibliografi Author: Saadah, Siti (Advisor); Sitorus, Suandi Topik: Credit Scoring; Data mining; Classification; Decision Tree; Principal Component. Bahasa: (ID )     Penerbit: Program Studi Magister Ekonomi Terapan Sekolah Pascasarjana Universitas Katolik Indonesia Atma Jaya     Tempat Terbit: Jakarta    Tahun Terbit: 2020     Jenis: Theses - Master Thesis Fulltext: Suandi Sitorus_Master Theses_2020.pdf (8.29MB; 18 download) 201800110002_Suandi Sitorus_Lembar Administrasi.pdf (1.12MB; 2 download) Abstract One of the factors of an increase in credit risk when the analysis data are less precise. Banks probably make the wrong decision to approved credit facilities by accepting bad applicants, and also rejecting good applicants. Banks are faced a lot of data analysis while the credit analysis process need to be quick. Therefore, banks need to have tools that are able to identify the feasibility of credit applicant with a good level of accuracy. Credit scoring is one of the tools used by banking institutions in predict the level of credit risk of prospective customers. This thesis are builds the credit scoring with data mining techniques by using the decision tree model and principal component analysis (PCA). Classification techniques for training data to build a model and then the model that is formed is validated on the testing data. The results of this study indicate that credit scoring with the decision tree model and PCA has a fairly good level of accuracy. The results of the modeling can be used to support the decision of lending whether it can be approved or rejected. Opini Anda Klik untuk menuliskan opini Anda tentang koleksi ini! Lihat Sejarah Pengadaan  Konversi Metadata   Kembali

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