One of the world's leading causes of death is traffic accidents. Data from World Health

Indonesia is one of the countries in this world that has the most outstanding fishery potential. There are more than 3000 fish species under Indonesia's sea, yet the people are still not able to relish them completely. Illegal fishing by foreign ships in Indonesia's territorial sea is one of the reasons why this happens. In order to minimize this kind of loss, those ships should be detected automatically by implementing image processing and artificial intelligence techniques. The study proposed techniques for automatic detection of ships at sea on digital images. These techniques are global image thresholding and artificial neural network backpropagation. The result of this research is proposed of technique able to detect ship with 85% accuracy level. This method may be improved by adding more training data varieties.

This paper describes flying multi-vehicle control strategies and its benefit for saving fuel. Exposition starts from inspiration of flying multi-vehicle in daily life. Furthermore, from model of single flying vehicle, we construct the model of multi-vehicle and cost functional model that describe the state of the cost to be met the flying vehicle. The flying multi-vehicle control designed with optimal control strategy. The design of optimal control is done through the Pontryagin Maximum Principle, brings the model to a system of equations consisting of state equations and costate equations. In the system of states equations, each having initial and final condition, in the costate equations system has no requirements at all. The next problem is converted to the initial value problem and search for the approximate initial condition equation of costate equations system which has no requirements using a modified method of steepest descent. Thus, the control of multi-vehicle successfully performed and the simulation results presented on the results and discussion section. In addition, we also calcute the fuel which used by multi-vehicle, compared by the fuel which used by each vehicle in solo flying. The result can be conclude that the fuel more efficient if the flying vehicles in formation flying.

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C4.5 Algorithm is one of the classification technique in machine learning which is used in data mining process by build a decision tree which is represent in the rules. The aims of classification technique in data mining is to recognize the regularity of the pattern and the relation in a huge dataset by historical data collection. Students' modalities measurement which is done by the questionnaire is produce historical data which is potentially to be processed to generate the classification that can be converted in rules. The expert acquisition and the C4.5 algorithm classification rules are used as knowledge base in the expert system. Therefore this research is done to build an expert system of the student's modalities identification by implementing C4.5 algorithm that can produce seven categories of modalities classification, they are : visual, auditory, kinesthetic, visualauditory, visual-kinesthetic, auditory-kinesthetic and visual-auditory-kinesthetic which has good in accuracy. The accuracy of the C4.5 algorithm classification and the expert system testing prediction is 80%.