Development of Nonparametric Path Model using Multivariate Adaptive Regression Spline (MARS) Method In Compliance Behavior of Paying Credit In Bank

The purpose of this study is to develop a Non-parametric Path with the MARS (Multivariate Adaptive Regression Spline) approach which is applied to the behavior of paying credit compliance at Bank. prospective debtor by a Bank. The data used in this study is primary data using a research instrument in the form of a questionnaire. There are 7 variables, namely 5 exogenous variables in the form of 5C variables (Character (X1), Capacity (X2), Capital (X3), Collateral (X4), Condition of Economy (X5)), and two endogenous variables, namely Punctual Payment (Y1), Obedient Paying Behavior (Y2). Variable measurement technique is done by calculating the average score on the items. Sampling in this study used a purposive sampling technique with the criteria of respondents in the study were mortgage debtors (House Ownership Credit) at Bank X. Respondents obtained in this study were 100 respondents. The analysis used is nonparametric path with Multivariate Adaptive Regression Spline (MARS) approach. The result of this research is the estimation of nonparametric Path function using MARS approach on various interactions. The best estimate of the function of obedient behavior in paying credit is when it involves 4 variables, namely Character (X1), Capacity (X2), Conditions of economy (X5), and On time pay (Y1) with a value of generalized cross-validation The smallest (GCV) obtained is 0.2496. The originality of this research is the development of a nonparametric path with the MARS approach that is able to capture interactions between existing variables and is also able to handle the limitations of the truncated spline to determine the position and number of knot points used when involving many predictor variables. There has been no previous research that has examined the development of a nonparametric path with the MARS approach.

Prediction of Production Performance in Smart Manufacturing Using Multivariate Adaptive Regression Spline

With the dynamic arrival of production orders and ever-changing shop-floor conditions within a production system, production scheduling presents a challenge for manufacturing firms to ensure production demands that are met with high productivity and low operating cost. Before a production schedule is generated to process the incoming production orders, the production planning stage must take place. Given the large number of input parameters involved in production planning, it is important to understand the interactions of input parameters between production planning and scheduling. This is to ensure that production planning and scheduling could be determined effectively and efficiently in achieving the best or optimal production performance with minimizing cost. In this study, by utilizing the capabilities of data pervasiveness in smart manufacturing setting, we propose an approach to develop a surrogate model to predict the production performance using the input parameters from a production plan. Based on three categories of input parameters, namely current production system load, machine-based and product-based parameters, the prediction is performed by developing a surrogate model using multivariate adaptive regression spline (MARS). The effectiveness of the proposed MARS model is demonstrated using an industrial case study of a wafer fabrication production through the random sampling of varying numbers of training data set.

Flexibility of Nonparametric Regression Spline Truncated on Data without a Specific Pattern

Bentuk pola hubungan antara variabel prediktor dan variabel respon ada yang diketahui, namun pada nyatanya ada pula yang tidak diketahui. Apabila bentuk pola hubungan antara variabel respon dan variabel prediktor tidak diketahui, pendekatan regresi nonparametrik merupakan pendekatan yang paling sesuai. Pendekatan regresi nonparametrik tidak tergantung pada asumsi bentuk kurva regresi tertentu, sehingga akan memberikan fleksibilitas yang tinggi. Salah satu estimator regresi nonparametrik yang terkenal adalah spline truncated. Spline truncated merupakan potongan-potongan polinomial yang memiliki sifat tersegmen dan kontinu. Pada penelitian ini, akan disimulasikan pola hubungan antara kedua variabel yaitu respon dan prediktor yang tidak memiliki pola tertentu, yang kemudian didekati dengan dua pendekatan regresi, yaitu parametrik dan nonparametrik. Berdasarkan ukuran kebaikan estimasi kurva regresi menggunakan koefisien determinasi diperoleh hasil bahwa pendekatan regresi nonparametrik lebih baik daripada pendekatan regresi parametrik. Hal ini dikarenakan pendekatan regresi nonparametric memiliki fleksibilitas yang tinggi sehingga mampu menyesuaikan sendiri bentuk estimasi kurva regresi.