Abstract. Expert system is dealt with system that used computer-based human intelligence to overcome particular problem which is commonly conducted by an expert. Frequent problem faced by the farmers of oil palm is the difficulty in defining the type of plant disease. As a result, the delay treatment of plant disease brings out the declining of farm products. An application system is needed to deal with the obstacles and diagnosing the type of oil palm plant disease. The researcher designed an intelligence-based application with input-output plan which is able to diagnose the type of oil palm plant disease by applying naive bayes method. Based on the research result by conducting bayes method with recognized symptom, diagnose of oil palm plant disease could be accomplished. The data of symptoms found are leaves turned yellow 0.4, dead leaves 0.4, black and brown color among the veins of leaves 0.5, young and old fruit with whole space 0.4, and decay of bunches is 0.3. The roots are tender in the amount of 0.5, and damage on sheath is 0.3. Through the chosen symptoms as mentioned above, the value of bayes is 80% with the type of disease is rotten bunch.

Data mining is a series of process to gain values such as informations that is manually unknown from a database by mining patterns from the data (with the intention to manipulate data into more useful information that is obtained by extracting data and recognizing important pattern or pulling data from the database). The intend of this research is to process cattle disease cases, which so far no research has been done to produce useful knowledge/science for institutions (Food and Livestock Security Agency) using data mining technique (C4.5 Algorithm). In the future, this research will be used by Food and Livestock Security Agency to counsel rancher in some area. This research will result a decision tree which will also classify rate of cattle disease. Proven by using the C4.5 algorithm, it can speed up the process of classification disease which initially takes about 1-2 months, and after using this application the results can be obtained right away.

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Employees are one of the resources used as a means of movement in promoting a company. Employee performance is highly influential on the profits obtained by the company. Therefore, to stimulate employee performance in selecting outstanding employees each period by providing additional bonuses or salaries for selected employees. However, because the process of evaluating and selecting outstanding employees carried out by managers of Human Resource Development (HRD) still uses a conventional system and takes a lot of time, so a decision support system is needed to evaluate the performance of HRD managers to be more effective and efficient while saving time and energy compared to the current system. For this reason, the author proposes a decision support system and was used to determine outstanding employee by using the simple multi-attribute rating technique (SMART) method. The Decision Support System Application applies the SMART method to help decision maker to determinate the outstanding employee and based on the table above, it was concluded that the employee recommended as an outstanding employee is employee C with the highest final score of 66.20.

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