Design of Enterprise Application Integration (EAI) in E-Planning and E-Budgeting Systems

Information system integration is needed to support business processes in order to achieve organizational goals. E-Planning and E-Budgeting are systems that are managed by the Department of Communication and Information Technology in Pasuruan Regency. E-Planning is taking part in planning activities, while E-Budgeting is taking part in assigning activities budget. Both systems are working separately that may cause data to exist only in one of those systems. Lacks of data in another system can lead to activity without fund and vice versa. Of course, this problem can disrupt the annual budget plan of the Department of Communication and Information of Pasuruan Regency. This problem can be solved using Enterprise Application Integration (EAI). EAI allows the exchange of data and business processes from several different applications that are interconnected. EAI have 12-steps program that come from best practices for system integration. Despite requiring full steps, this research will only take the first seven steps and then proceed with making UML diagrams. The result of this research are UML diagrams such as use case diagram, class diagram, sequence diagram, and object-oriented data model. These designs can represent business processes that are needed for integration process between E-Planning and E-Budgeting system. These designs will be validated through user debriefing and user validation. These designs can be used as references to build a system to integrate E-Planning and E-Budgeting system

Optimization of Vehicle Routing Problem with Time Window (VRPTW) for Food Product Distribution Using Genetics Algorithm

Food distribution process is very important task because the product can expire during distribution and the further the distance the greater the cost. Determining the route will be more difficult if all customers have their own time to be visited. This problem is known as the Vehicle Routing Problem with Time Windows (VRPTW). VRPTW problems can be solved using genetic algorithms because genetic algorithms generate multiple solutions at once. Genetic algorithms generate chromosomes from serial numbers that represent the customer number to visit. These chromosomes are used in the calculation process together with other genetic operators such as population size, number of generations, crossover and mutation rate. The results show that the best population size is 300, 3,000 generations, the combination of crossover and mutation rate is 0.4:0.6 and the best selection method is elitist selection. Using a data test, the best parameters give a good solution that minimize the distribution route