The Aplications of Model Bayesian Networks For Analysis and Preventive Actions on Maritime Security Operations

Adi, Bandono (2020) The Aplications of Model Bayesian Networks For Analysis and Preventive Actions on Maritime Security Operations. The Aplications of Model Bayesian Networks For Analysis and Preventive Actions on Maritime Security Operations, 9 (3). pp. 3000-3006. ISSN 2277-8616

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Abstract

The implementation of Maritime Security Operations of Navy needed support for both major supporters down. In an operating system, necessary information, coordination, and readiness elements for the operation are achieved. In maritime security especially i n the Natuna Sea needed an operation pattern that effectively and efficiently as carrying out an act in which such actions can be repressive action or preventive acti on so that the response elements operating there can be maximum in deterring acts of territorial violations and theft of fish illegally by foreign fishermen. Need factors - factors (variables) that can be optimized to maritime security in the Natuna Sea can be maximized especially with the operati onal situation of uncertainty. To the reduction of various errors that may arise, then be made a Bayesian network model to measuring of the performance responsiveness of maritime security operations with a causal mapping approach. Causal mapping is used to form a network structure on a Bayesian network. The purpose of this research is to create a model system to determine the variables that build the model maritime security. This model was made using expert opinion and literature studies as the basis for preparing the variable and interdependence. The prior probability conditionals and conditionals probability tables using a questionnaire that was given to the expert. From the data obtained were then computed using software Netica with the results of the Navy capability that exists today when measured responsibility in implementing maritime security against acts of poaching in Natuna Sea by foreign fishermen only have a percentage of 74.7% of the 17 independent variables that are subsequently carried out a sensitivity analysis which produces 2 pieces of variables that affect the capability of the warship and capability of the aircraft as well as the first variable of the dependent variables, in this case, is better than the repressive measures of preventive measures

Item Type:	Article
Uncontrolled Keywords:	Bayesian Network, Maritime Security Operation, Causal Mapping
Subjects:	L Education > L Education (General) V Naval Science > V Naval Science (General)
Depositing User:	Dr. Adi Bandono
Date Deposited:	22 Dec 2020 01:24
Last Modified:	22 Dec 2020 01:24
URI:	http://repository.sttal.ac.id/id/eprint/29

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