Journal of Digital Information Management Slamet Sudaryanto N, Sudaryanto, Maryani S 16 3 2018 https://doi.org/10.6025/jdim/2018/16/3/105-113 http://dline.info/fpaper/jdim/v16i3/jdimv16i3_1.pdf Query optimization is an important task in the client / server environment of a distributed database, where large data locations are widely distributed, such as distribution of health epidemiology data based DBD on geographic information systems (GIS). In order to generate query optimization on a distributed database it is necessary to have a proper method on a particular query process function. The query process requires important attention especially in distributed databases, because the result of a cost-based query process (access fees and communication costs) is affected by the involvement of the number of attributes and sites visited. If a query can be decomposed into subqueries that require operations on a separate database (geographically) and can determine the exact site access sequence of a query process query circuit then the operating costs for the query process will be minimal. When a query process in a distributed database occurs, queries operations will search for data from various attributes in a scattered database table, whereas query processes often do not require all the attributes of the table. Therefore, in order to optimize the query required minimum query operation cost (communication cost and access cost). One way to minimize the cost of queries is to separate attributes that are not required by a query, thereby reducing the amount of time communication and access. Can not make mistakes in the separation of attributes, attributes should not be split indiscriminately, because if not appropriate it will result in the amount of access costs are getting larger and ultimately reduce the performance of the query process itself. To perform such attribute separation can be done by Vertical Fragmentation method. In this experiment will be conducted by comparing the results of separation attributes. Separation of attributes will be done by using Vertical Fragmentation method to source health database tables (database testing), while the algorithm used for attribute separation is Bond Energy Algorithm (BEA) and Graphic Based Vertical Partitioning (GBVP). The initial result of vertical fragmentation in both algorithms is the determination of what attributes will be separated from a number of specific query processes. The result of separation of attributes from each algorithm will be compared and evaluated using Partitioned Evaluator (PE). The purpose of evaluation of the result of separation of attribute is to know the amount of access cost of some attributes from each algorithm. Thus it will be known the most optimal algorithm in the operation of the query process. Algorithms that have better performance are algorithms that have the lowest execution time. The purpose of this research is to perform query optimization by applying the correct algorithm, by way of framing the method of fragmentation between BEA algorithm with GBVT to then concluded algorithm which has query optimization performance from the most optimal query process so it is suitable to be applied as query operation on distributed database in field health.