The world today has a rapidly growing human population whose daily needs are certainly increasing. Supermarket is a place to fulfill daily needs. When going to the supermarket, we have to spend a lot of time both shopping and also queuing at the cashier. This turns out to be a problem for customers because it can take up customers' time which is also experienced by one of the largest supermarkets, namely Hypermart. Not only a problem for customers, but it can also create a threat to Hypermart companies. However, with advances in technology and information systems, the world is growing to adapt to current conditions, namely by improvising. The improvisation carried out in this research is the payment transaction business process at Hypermart, namely by implementing the Self-CheckOut system by replacing the old trolley with a "Hyper Smart Cart" as an improvisation which will certainly answer all existing problems.

Semiparametric regression is a combination of parametric and nonparametric components. The estimation of the semiparametric regression function uses a parametric approach and a nonparametric approach. This study uses longitudinal data. The estimation technique in this study uses Spline truncated which has very special, excellent statistical interpretation and visual interpretation. The estimation technique in longitudinal semiparametric regression uses the weighted least square (WLS). The choice of knots in semiparametric spline truncated regression is very important because the number of knot points and locations of each knot will affect the regression estimation form. The method of selecting knots in this study uses a Modification of Generalized Cross-Validation (mGCV) and aGCV. This study uses cases of life expectancy in East Java Province 2001-2015. Comparison of the two methods based on the R-square value and the value of Mean Square Error (MSE). The results show that the R-square value of mGCV is greater than aGCV and the MSE value of mGCV is smaller than aGCV. So, it can be concluded that the mGCV method is better than the aGCV method for optimal selection of knot points in the case of life expectancy in East Java Province.

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