Abstract Catfish is one of the important strategic commodities in Indonesia which is widely cultivated. Catfish needs continue to increase along with the increasing demand for catfish as foodstuffs such as the need for many lamongan stalls. However, efforts to increase production and development of catfish farming have several obstacles that can reduce production by 50% due to certain diseases. This situation is certainly very detrimental to the catfish in particular and the wider community in general. However, farmers have very low knowledge about the technical maintenance of catfish plants. This situation resulted in farmers being highly dependent on the control of a limited number of plant pests. To overcome this problem, a website-based application was made. To optimize computational resources on mobile devices, we need a Cased Base reasoning method that consists of frame based and rule based representation with rules or rules that are used to determine whether catfish plants contract certain diseases, which types of diseases can be detected in the process this research includes bacterial pustular disease, interchnose disease, mosaic virus disease, collapsing disease, rust disease and blight disease. then analyzed which cased base reasoning is the most optimal. This can be done with a scenario comparing some of the cased base reasoning from the level of accuracy at 87.5%.

The purpose of this research is to produce tangible results in providing comfort or convenience in turning on or off the night life at home everyday, especially for people who forget to turn off the lights. The method used in making microcontroller-based home lighting control using an android smartphone is. This research method consists of several stages, analysis methods, design methods, implementation testing methods, and maintenance. The results achieved are improving the aspects of comfort and convenience for agencies, where this series of tools works after bluetooth devices are available on android smartphones. connected to a bluetooth module which is connected to the Arduino microcontroller. Basically, the principle of using this tool is that users must be able to operate a smartphone based on the Android operating system. The conclusion of this research is that the Arduino-based light control device using bluetooth can fulfill its function to control the lights properly and the device is designed to be minimalist and safety so it is safe to use.

The case of drop out at the Weleri STEKOM is often done by the campus. Drop out is a problem that is often done by Weleri STEKOM students because of a GPA of less than 2, Number of organizations followed, Tuition not paid for students and student who have exceeded the limit of 14 semesters. This study discusses predicting drop out students with C4.5 and ID3 decision tree methods that are useful to assist the campus in anticipating student dropouts. This study uses student data as many as 1087 students. Student data is divided into training data and testing data in order to obtain a model or rule in predicting DO students. Variable of this reseach contain V1(GPA) and then V2 (Distance beetween home and campus), V3(how long the lecture has been done), V4(Having a Job), V5(Family) and V6(school fee). This   research  The results of this study obtained 18 rules or rules for ID3 algorithm and 8 rules for C4.5 algorithm. The algorithm ID3 test results obtained an average of 95.17%, precision of 94.7% and recall of 96.18%, while for Decision Tree C 4.5 obtained an average of 96.45%, precision of 96.90% and recall of 95.38. This research prove that Decision using C4.5 is better for prediction of drop out students at STEKOM.