<p class="Abstract">The KIP scholarship is one of the scholarships available at IAIN Bukittinggi, and prospective recipients will be chosen based on the number of quotas available. Thus far, the selection process has been carried out by calculating the total value based on the sum of the percentages of each criterion arranged according to the level of importance. The procedure does not include a decision-making system for determining whether or not to accept the KIP scholarship. As a result, a decision support system is required to quickly and accurately determine which students are eligible for scholarships. In this research, the decision-making system compares the SAW and TOPSIS methods, with the latter using normalized weights in calculating the preference value as a determining value for alternative scholarship recipients to be selected. The SAW method was found to be more sensitive than the TOPSIS method in the data for the KIP scholarship 2020 recipients at IAIN Bukittinggi, with a sensitivity value of 96.87 compared to 81.96 for the TOPSIS method. Based on these findings, the SAW method can be recommended as a decision return system for KIP scholarship recipients to study at IAIN Bukittinggi the following year.</p>

The Covid-19 pandemic that entered Indonesia in early 2020 has more or less had an impact on Indonesia's economic growth. One of the important factors that are indicators of the ups and downs of the economy, especially in Indonesia, is export activities. The Covid-19 pandemic has had quite an impact on the total value of Indonesia's exports, especially from 2020 to 2021. The fluctuation in the export value has made researchers interested in forecasting the total export value, especially after the Covid-19 pandemic. Forecasting of the total value of exports can certainly be used as a reference for the government to determine the direction of policies toward export activities to increase Indonesia's economic growth. Export values usually have seasonal patterns. One of the time series analyses that can be applied to data on total export values is the SARIMA model. Especially after Covid-19, no related studies have been found that use the SARIMA model in predicting the total value of exports in Indonesia. Using reference data on the total export value of Indonesia from January 2019 to March 2022, the best model was obtained and met the assumptions of residual normality and residual freedom, namely the ARIMA model (0,1,1)(0,0,1)12 without an intercept with an AICc value of 675.5562. Forecasting the total export value from April 2022 to March 2023 using this model indicates that the export value will increase slowly but decrease in September 2022 and January 2023.

A mixture experiment is a special case of response surface methodology in which the value of the components are proportions. In case there are constraints on the proportions, the experimental region can be not a simplex. The classical designs such as a simplex-lattice design or a simplex-centroid design, in some cases, cannot fit to the problem. In this case, optimal design come up as a solution. A D-optimal design is seeking a design in which minimizing the covariance of the model parameter.  Some model parameters are important and some of them are less important. As the priority of the parameters, the prior information of parameters is needed in advance. This brings to a Bayesian D-optimal design. This research was focus on a baking experiment in which consisted of three ingredients with lower bounds on the proportion of the ingredients. The assumption model was a quadratic model. Due to the priority of the model parameters, the Bayesian D-optimal design was used to solve the problem. A point-exchange algorithm was developed to find the optimal design. Nineteen candidates is used to choose twelve design points. It found that the potential term is feasible to the actual model and design points represent overall points in the design area.

Mixture design is known as experimental design which is often used. The total number of components in the mixture is 100% and the value of each component must be greater than or equal to 0%. The industry sector is usually used the mixture design. Then, the D-optimality criterion can help to determine the possible compositions of the mixture to conduct some trial and error composition of the product. However, this criterion very depend on the assumption of the model. To reduce its dependence, the Bayesian approximation is used. The Bayesian D-optimal algorithm applied to a mixture consisting of two components with constraint functions. Ten design points formed from eleven candidate points. By applying the Bayesian D-optimal algorithm on two components of the mixture, the design has no convergent design as the result. So, to find the result, the classical D-optimal was used and three different points was formed.