Amiri, Yusril Izzi Arlisa (2017) Klasifikasi Debitur untuk Memprediksi Kelayakan Pengajuan Kredit di Koperasi Unit Desa Jaya Sekarputih Bondowoso Menggunakan Metode Regresi Logistik Biner dan Classification Tree. Undergraduate thesis, Institut Teknologi Sepuluh Nopember.
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Abstract
Koperasi Unit Desa (KUD) Jaya merupakan koperasi bertempat di Desa Sekarputih Kabupaten Bondowoso. Koperasi tersebut masih ke-sulitan untuk menentukan pemohon yang layak mendapatkan fasilitas kredit dan tidak beresiko menyebabkan kredit macet, sehingga perlu melakukan identifikasi dan memprediksi kelayakan nasabah dengan baik sebelum memberikan pinjaman dengan cara memperhatikan data historis pinjaman nasabah. Oleh karena itu, perlu dilakukan peng-klasifikasian calon debitur sebagai upaya untuk menentukan pemohon yang layak mendapatkan fasilitas kredit menggunakan metode Regresi Logistik Biner dan Classification Tree. Data yang digunakan diambil dari data debitur di KUD Jaya dengan jatuh tempo pelunasan kredit pada bulan Januari 2016 hingga Januari 2017.Variabel yang diguna-kan yaitu jumlah pinjaman, jenis kelamin, agunan, pekerjaan, jangka waktu pelunasan dan usia debitur. Variabel yang signifikan terhadap kolektibilitas kredit menggunakan metode Regresi Logistik Biner yaitu variabel agunan BPKB kendaraan bermotor dengan nilai accuracy, sensitivity dan specificity untuk data testing masing-masing sebesar 56,8205%, 47,1429% dan 65,4167%. Metode Classification Tree me-miliki nilai accuracy, sensitivity, dan specificity masing-masing sebesar 61,5897%, 57,1429% dan 65,8333% sehingga metode terbaik yang digunakan untuk memprediksi kelayakan pengajuan kredit di KUD Jaya adalah dengan menggunakan metode Classification Tree.
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Koperasi Unit Desa (KUD) Jaya is one of the union placed in Sekarputih Village Bondowoso District. This union still had difficulty to determine worthy applicant to get loan facility and is not at risk that can caused non performed loan. It is, therefore, necessary to identify and predict customers' worthiness well before providing loan by paying attention on customers' historical loan. It is also necessary to classify prospective borrowers as an attempt to determine applicant who is worthy to get loan facility by using Binary Logistic Regression and Classification Tree. The data used is taken from borrower data in KUD Jaya which has acquittal due date on January 2016 until January 2017. Some variable used is the amount of loan, sex, collateral, profession, acquittal due date period and age. The significant variable towards borrowers' collectibility using Binary Logistic Regression are BPKB vehicle collateral with accuracy, sensitivity and specificity value each 56,8205%, 47,1429% dan 65,4167% for data testing. While when Classification Tree method was employed, there has accuracy, sensitivity and specificity value 61.5897%, 57.1429% and 65.8333%. From this study, the best method used to predict the worthiness of loan application in KUD Jaya is by using Classification Tree method.
| Item Type: | Thesis (Undergraduate) |
|---|---|
| Additional Information: | RSSt 519.536 Ami k |
| Uncontrolled Keywords: | Classification Tree, Kelayakan Pengajuan Kredit, Koperasi Unit Desa, Regresi Logistik Biner, The Worthiness of Loan App lication, Binary Logistic Regression |
| Subjects: | H Social Sciences > HA Statistics Q Science > QA Mathematics > QA278.2 Regression Analysis. Logistic regression |
| Divisions: | Faculty of Mathematics and Science > Statistics > 49201-(S1) Undergraduate Thesis |
| Depositing User: | Yusril Izzi Arlisa Amiri |
| Date Deposited: | 20 Oct 2017 08:30 |
| Last Modified: | 05 Mar 2019 08:38 |
| URI: | http://repository.its.ac.id/id/eprint/47791 |
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