Decision Support System is a branch of artificial intelligence used to help make decisions in semi-structured cases, where it is not known exactly how decisions should be made. In this research, the design and making of decision support system that is used to help determine the nutritional status of children with weight input, age, height, head circumference, nutrition value of toddlers and the output of nutritional status of children under five and their handling. The problem of data uncertainty in decision support system is solved by using fuzzy mamdani method. The existence of data uncertainty in the process can occur because of the differences in existing calculations on the system. The process of determining the nutritional status of this decision support system is begun by entering the input data of toddlers, where the system will display some variables that have been made then start the calculation with the application. The end result of this research is decision support system to do nutritional handling in toddler along with their nutrition result. The calculations show the level of confidence in the system of nutritional status of the toddler and from the results of this study obtained the system accuracy of 83.33% of the 18 test data that has been tested is obtained. Therefore, it can be concluded that decision support system produces a good examination.  Keywords: Decision Support System, Fuzzy Mamdani, Underfive Nutrition

The Sleep disorder sometimes happens to someone unconsciously. Many health worker need a lot of time to detect the cause of sleep disorder, it is because there are some similar type of sleep disorder. The public health center in Karangmalang Sragen is needed a system to help the health workers to analyze the cause of sleep disorders. The aim of this research is to make a system to analyze the cause of sleep disorder by using the Naïve Bayes method. This research is conducted on Integrated Healthcare Center especially for the elderly in kedungwaduk. This research chooses the Naïve Bayes method to analyzing the type of sleep disorder experiences happen by the patient. The types of sleep disorders that used in this study were insomnia, hypersomnia, narcolepsy, sleep terror, disturbed sleep schedules and nightmares. The result in this study is patients can get solution about their sleep disorder, and also it can reduce the bad effect for the patient. The validation result of the Naïve Bayes method showed that 80% data was accuracy and it will be compared between 10 data from diagnostic test and 30 testing data.Keywords : Sleep disturbance, diagnosis, naïve bayes

Developments in the current era of globalization are very dependent on the economic sector which is the benchmark of success carried out by the government. The role of the community in national development in the economic field is the existence of Micro, Small and Medium Enterprises (MSMEs). To increase the role of MSMEs as a benchmark for the success of the economic sector, there must be support from the government, such as assistance for business owners with limited costs. The purpose of this study is to determine community business groups as a measure of the level of business, making it easier for the government to provide assistance. The K-Means Clustering method is a method used for grouping business levels based on the income that exists in today's society. The result of this research is a website-based business-level grouping system used by the Cooperatives and SMEs Office by grouping them into micro, small and medium-sized businesses based on income/assets.

One of the valuable assets in a company is human resources (HR).  Human Resource Information System (HRIS) has emerged as one of the drivers of competitiveadvantage and strategic decision making tool. One of the HRIS task is employee recruitment. Employees become an important role. Therefore, this research is conducted forclassification of employee status determination using the Naïve Bayes methods.  One of the duties of employees is to provide service to customer in the process of purchasing goods until the payment transaction process directly. Because of the large number of contract employees at the time of certain events, it requires companies to select every three months for the continuation of the work contract period for employees according to store needs so that the company's employee payroll expenses do not exceed the budget. One of the criteria is for being able to work in flexible groups between the ages of 17 and 25 for contract employees with a minimum of high school or vocational education. The purposes of this study are to design and build a Decision Support System application for the Continuation of Employee Employment Contracts Using the Naïve Bayes Method at PT. XYZ Retail. The result of the research is the application using the naïve bayes method with an accuracy rate of 90%.

YPAB is an institution that keeps and takes care babies and children who don’t have parents. The babies and the children can be adopted, but the institution has their own regulations for potential adopters who want to adopt children. All this time, adopter who adopted the babies or the children from YPAB institution is done manually which need long time. Therefore, DSS is needed for decide a potential adopter. The purpose of this research is developing a DSS in the process of selecting potential adopters. Meanwhile, the research method uses the Analytical Hierarchy Process (AHP) and the Exponential Comparison Method (MPE) and the System testing uses black-box and validity testing. System testing is used black-box and validity testing by comparing the real YPAB data and the calculation system results. The test results show that the adoption system using the Analytical Hierarchy Process (AHP) method and the Exponential Comparison Method (MPE) has a good performance so that the system is feasible to be implemented in YPAB.Keywords: DSS, MPE, Children adoption, YPAB.