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Abstrak :
Penelitian ini bertujuan untuk merumuskan model credit scoring untuk kredit mikro dengan menggunakan metode Multivariate Adaptive Regression Splines (MARS). Metode MARS merupakan pendekatan regresi nonparametrik yang memiliki kemampuan untuk memodelkan hubungan yang kompleks antar variabel tanpa asumsi model yang kuat dan menghasilkan model dengan akurasi tinggi yang melebihi model credit scoring lainnya dan mampu mengolah data berdimensi tinggi. Dalam beberapa tahun terakhir, MARS telah banyak diterapkan untuk memodelkan berbagai data, namun belum ditemukan penggunaanya untuk credit scoring kredit mikro. Secara umum metode credit scoring yang umum digunakan adalah analisis diskriminan dan regresi logistik. Namun kedua metode tersebut memiliki keterbatasan yaitu perlunya asumsi parametrik antara variabel respon dan prediktor. Penelitian menggunakan studi kasus data kredit mikro PT. Bank ABC yang merupakan market leader kredit UMKM di Indonesia. Hasil penelitian ini menunjukkan bahwa model penilaian kredit mikro dengan menggunakan MARS memiliki akurasi prediksi yang lebih tinggi dengan tingkat kesalahan terkecil, kesalahan tipe I dan II dibandingkan dengan Metode Regresi Logistik. Sehingga hasil penelitian ini dapat digunakan sebagai bahan pertimbangan bagi bank dalam menerapkan metode MARS dalam credit scoring dalam rangka pengendalian Risiko Non Performing Loan Kredit Mikro. ......This paper aim to formulate the credit scoring model for micro loan using the Multivariate Adaptive Regression Splines (MARS) method. The MARS method is a nonparametric regression approach that has the ability to model complex relationships between variables without strong model assumptions and produce a model with high accuracy that exceeds other credit scoring models and is able to process high-dimensional data. In recent years, MARS has been widely applied to model various data, but its use for micro loan credit scoring has not yet been found. Generally, the credit scoring methods commonly used are discriminant analysis and logistic regression. However, there are limitations to both methods, namely the need for parametric assumptions between the response variables and predictors. This study use a case study of micro loan data from PT. Bank ABC, which is the market leader for MSME loans in Indonesia.The results of this study indicate that the microcredit credit scoring model using MARS has a higher predictive accuracy with the smallest error rate, type I and II errors compared to the Logistics Regression Method. So the results of this study can be used as considerations for banks in applying the MARS method in credit scoring in order to control the Non-Performing Loan Risk of Micro Loan.

Abstrak :
Kebutuhan untuk penilaian kredit dimulai ketika ada mekanisme yang berlaku secara masif untuk peminjaman dan pemberian pinjaman yang secara paralel berhubungan dengan kebutuhan untuk membayar kembali pinjaman di masa depan. Dalam praktiknya, pengembangan gagasan terkait metode penilaian kredit diperkenalkan oleh Durand pada tahun 1941. Pemerintah, melalui OJK, mengeluarkan peraturan nomor 1 POJK.05 tahun 2015 tentang manajemen risiko Lembaga Keuangan Non-Bank dan Peraturan Bank Indonesia No. 11/25 / PBI / 2009 sebagai upaya untuk mengendalikan risiko kredit perusahaan pembiayaan. Kredit mikro adalah subjek yang menarik untuk penelitian ini karena identik dengan prinsip meniadakan penggunaan jaminan untuk penilaian kreditnya. Untuk itu, mengetahui faktor-faktor penyebab gagal bayar sangat penting. Sampel yang digunakan dalam penelitian ini melibatkan 949 perempuan pra-sejahtera yang merupakan nasabah PNM Mekaar dan mewakili 34 wilayah operasional PNM Mekaar di Indonesia. 949 data yang digunakan pada penelitian ini terbagi menjadi 2 kelompok data yaitu 258 yang termasuk kategori gagal bayar dan 691 nasabah dengan kategori non-gagal bayar. Dengan menggunakan metode estimasi probit, penelitian ini menunjukkan probabilitas gagal bayar pada pembayaran pinjaman yang ada di PNM Mekaar. Hasil dari penelitian ini menunjukkan bahwa jangka waktu, siklus, umur, dan status perkawinan mempengaruhi probabilitas gagal bayar, sedangkan jumlah pinjaman tidak signifikan dalam mempengaruhi probabilitas gagal bayar.

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The need for credit scoring begins when there is a mechanism that applies massively to lending and borrowing parallel with the need to repay loans in the future. In practice, the development of ideas related to the credit assessment method was introduced by Durand in 1941. The Government, through the OJK, issued 2015 regulation number 1 POJK.05 concerning risk management of Non-Bank Financial Institutions and Bank Indonesia Regulation No. 11/25 / PBI / 2009 as an effort to control the credit risk of finance companies. Microcredit is an interesting subject for this research because no collateral is being used for doing credit assessment. For this reason, knowing the factors that cause default is crucial. The sample used in this study involved 949 underprivileged women who were PNM customers and represented 34 Mekaar operational areas in Indonesia. 949 data used in this study were divided into 2 data groups, 258 which included the category of default and 691 customers with the non-default category. By using the probit estimation method, this study shows the probability of default on loan payments at Mekaar PNM. The results of this study indicate that the time period, cycle, age, and marital status affect the probability of default, meanwhile loan size statistically insignificant affects the probability of default.

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.