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Abstrak :
ABSTRAK
Memasuki dekade tahun 2000 industri jasa pembiayaan di Indonesia semakin berkembang pesat, karena lcebutuhan masyarakat akan alat transportasi yang praktis dan murah semakin tinggi. Sehingga perusahaan pembiayaan dituntut untuk dapat menyesuaikan diri dengan kebutuhan masyarakat terhadap pelayanan jasa keuangan yang sangat kompleks. Penulis melakukan penelitian ini bertujuan untuk mengetahui pengaruh Income, Age, Marital Status, Down Payment, Tenor don Interest terhadap kemungkinan tegjadinya kredit gagal bayar, sehingga perusahaan pembiayaan melalui analisis kreditnya dapat mengidentifikasi konsu.men~konsumen yang layak untuk diberikan kredit agar dapat meruinimalisir texjadinya gagal kredit. Pengujian dalam peneiitian dilakukan dengan menggunakan metode analisis logit dengan mengambil data konsumen PT. ABC yang melakukan kredit sepeda motor sebanyak 14,718 konsumen. Hasil dari penelitian ini rnenunjukkan Income, Age, Marital Status, Interest, Age terhadap Income, Income terhadap Marital Status, Age terhadap Income terhadap Marital Status, DP terhadap Income dan Income terhadap Tenor signifikan terhadap kemunglcinan tenjadinya status kredit gagal bayar. Dari seluruh variabel ini Income mempakan bagian terpenting dalam kredit dan pengaruh ketidakpastian di masa yang akan datang sangat mempengaruhi konsumen terhadap kemungkinan default.

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Abstract
Entering the decade of 2000 financial services industry in Indonesia growing rapidly, because many people needs a practical transportation and low cost. Financing companies are required to be able to adjust to the needs of the community of financial services that are complex. The Purpose of this paper is to understand the influence of Income, Age, Marital Status, Down Payment, Tenor and Interest on the status of credit failed to pay, so the company through financial analysis can identify the consumer credit-worthy consumers who are given credit for in order to the occurrence of failed credit. Logit analysis method was used in this study with the data consumers takes from PT. ABC is doing a motorcycle loan as 14,718 customers. Results from this research indicate Income, Age, Marital Status, Interest, Age by Income, Income by Marital Status, Age by Income by Marital Status, DP by Income dan Income by Tenor of a significant effect on the status of credit failed to pay. From the all variable Income is the most important to get loan and the influence of uncertainty in the future greater influence on the consumer fails to pay the loans.

Abstrak :
Non-performing loans has been one of the biggest problems in the banking sector. One alternative to minimize credit risk is to improve the evaluation of the applicant's credibility. Credit risk assessment methods must be improved. Credit scoring is an evaluation of the feasibility of credit requests. Poor credit can lead to an increase in non-preforming loans that may reduce bank productivity even in the event of financial crises and financial institutions bankruptcy. The number of Data-mining-based Credit scoring model has increased. The performance of classifiers in solving financial problem become the main reason why it is growing rapidly. Previously, credit scoring is based on the conventional statistics such as logistic regression and discriminant analysis. Eventhough those techniques produce a good accuracy, some of the assumptions cannot be accomplished by the data. Along the development of infromation technology, more advance approach named data mining has been developed. Therefore, this study performs Data Mining approach to solve NPL percentage problems in Bank. The classification methods that will be used is Decision Tree C4.5, Back Propagation Neural Network, and ensemble classifier algorithms. Classifier with the best accuracy is Decision Tree C4.5 with Adaboost with 98,87% The best sensitivity also performed by Decision Tree C.5 complemented by adaboost with 97,3%. It is considered as the best model in terms of prevent the type II error which could impact to the increase of non-performing loan in a bank.