Items delivery companies generally use a model to minimize delivery costs. From a mathematical perspective, the model is an objective function that involves constraints. Meanwhile, from a practical point of view, these constraints include aspects that affect item delivery, for example, delivery zones, number of delivery vehicles, vehicle capacity, trip routes, etc. However, the models built so far have not paid attention to changes in road density. This aspect can result in a nonoptimal delivery model, which results in not a minimum delivery cost. For this reason, this paper discusses how to divide zones using the clustering method and predict changes in the shipping zone of a dynamic network using predictive distribution. So, the model can work optimally if the delivery zones and delivery strategies are suitable.

Dengue fever is an endemic disease transmitted through the Aedes Aegypti mosquitos. Dengue virus can be transmitted from human hosts who have been infected by the virus to the mosquitoes to be transmitted back to other humans. So that, it is possible for the virus to be transmitted to several surrounding locations. Aedes Aegypti is one of the dengue mosquitoes that likes a warm climate and not too wet or dry. In addition, many un-expected factors can cause a significant increase in the number of dengue fever cases. So that the number of dengue fever cases can increase significantly far different from other data. An observation data that has different characteristics from others is called outlier. The existence of outliers can indicate individuals or groups that have very different behavior from the most of the individuals of the dataset. Outlier data in a data set are often encountered in various kinds of data analysis. Frequently, outliers are removed to improve accuracy of the estimators. But sometimes the presence of an outlier has a certain meaning, which explanation can be lost if the outlier is removed. In this paper, modeling dengue fever cases using GSTAR(1;1) with outlier factors was firstly proposed.

Critical Illness Insurance is an insurance product with a lump sum benefit or cash payment if the policyholder is diagnosed with critical illness in an insurance contract. The health state change process can be observed and modeled by a multi-state Markov Chain with a time-continuous parameter. In this article, we will illustrate how the mathematics of Markov Chain can be used to develop a model of state change in critical illness in the case of a cancer patient. Health state changes process in critical illness modeled by four states Markov chain. Using transition probability which is based on transition intensity of continuous Markov chain. We will estimate the transition intensity and used it in a differential equation of transition probability. The application of Markov chain model will be used to estimate the value of the premiums in some of critical illness insurance benefit model. The premiums value will be shown in the table in section five.

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In this paper, we introduce an alternative approach as model for cluster analysis. The data were analyzed by rule-k-means algorithm. It's combine between k-means algorithm and rules. As an application, we use the simulate of item delivery data to classify items based on destination addresses. The goal is to map the item based on type of delivery vehicle. The clustering can be used as a recommendation to the item delivery service company.