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Combinatorial problems are problems that have a finite set of feasible solutions. Although in principle the solution to this problem can be obtained with complete enumeration, in complex problems it takes time that cannot be practically accepted, combinatorial analysis is a mathematical study of the arrangement, grouping, ordering, or selection of discrete objects, usually limited in number (finite in number) , many combinatorial optimization problems have been generated by research in computer design, computational theory, and by computer applications on numerical problems that require new methods, new approaches, and new mathematical insights, on combinatorial optimization, the function g (x) is defined as mapping g: {0,1} n → {0,1}, obtaining feasible regions Most methods for solving combinatorial optimization problems propose feasible regions, namely areas that are constrained by problem constraints after relaxation such as counts or binary variables, Field slices generally use the metaheuristic method proposed by para Other researchers, for example Genetic algorithms, simulated annealing, taboo search, plant propagation, methods do not use feasible regional concepts, but proposing the starting point for abstract completion is a brief summary of the paper to help readers quickly ascertain the research objectives and match the research needs.

Computer system development is increasing very rapidly in generating and collecting data that can be seen in terms of the application of computerized systems that continuously improve transaction data in the business world and in government systems, as well as the ability of hardware to store data with large capacity, increasing interest in mining data in accordance with technological developments, including problems related to computer science and data representation which are considered effective and efficient solutions. In this study the technique used is processing time series data, some of the knowledge produced is then ranked so that priority knowledge is obtained with a high level of confidence. Proximity distance using j-measure euclidean and cracking. Based on the results of cracking found rules based on the level of confidence that can be used as a decision support

The development of information technology is very rapid and is supported by the development of storage media technology and its application to all fields that produce huge amounts of data stacks generated from various sources, therefore need new techniques in managing data stacks. Data mining has become very important as an object and research study at this time because there are many data stacks found in agencies. Data mining is an analytical process of knowledge discovery in large and complex data sets. In this study the technique used is to conduct time series data mining clusters, using proximity to manhattan city. The time series graph is carried out by the sliding window to produce an analysis of the window for each cluster result. Based on cluster results, an analysis of knowledge transformation is carried out into new knowledge obtained from data mining time series data.

The clusters number optimization problem is a problem that still requires continuous research so that the information produced can be a consideration. Cluster evaluation techniques with Sum of Square Error (SSE) and Davies Bouldin Index (DBI) are techniques that can evaluate the number of clusters from a data test. Research with these two techniques utilizes Stunting data from a number of regions in Indonesia. The result is information on stunting data which is formed from the optimal number of clusters where the largest SSE is formed at k = 5 and the smallest DBI is formed at k = 5, with values of 23.403 and 1,178 respectively. Changes in the number of clusters also influence the information produced and DBI is proven to produce optimal number of clusters that contain information with a better pattern because it has a small intra-cluster value. It is expected that the results of this study can show the performance of the two evaluation techniques in producing the optimal number of clusters so that grouping information is in accordance with the expected pattern.

In realizing the success of a city into a smart city, it is necessary to improve services for people who are residents of the city, improving public services is a focus for stakeholders. Various efforts have been made, such as the construction of public service infrastructure and transportation infrastructure which has recently focused on intelligence. Providing health care services for people who need them quickly and on target is intelligent technology that can support the achievement of smart cities. In this study specifically discussed the provision of services to the community or patients who experience sudden health disorders or called emergency, in terms of managing emergency patients then given the main service (priority), so that the priority level of patient priorities can be divided into several priorities.