Competition in today's banking world is getting heavier and tighter. This is because the products of the bank with the other banks can be said to be the same. So the competition for good customers to obtain product deposits and loans (mortgages) increasingly difficult. But the banking world are now widely utilize information technology to banking activities. One of the use of information technology is the use of decision support applications for granting loans (mortgages). The method used in the decision support system is a Fuzzy Inference System (FIS) to determine the calculation method of Tsukamoto decision. The implementation of this application is more emphasis on the decision-making process for granting to produce a decision issued.

Research was conducted based on the need for a tool for farmers in determining disease in red chilli plants and how to control it so as not not wrong in taking peptisida. The tool is an expert system which in addition to diagnosis, the system is expected to provide suggestions for control. Basically the software consists of two main parts, namely the input knowledge base, and the consultation. Decisions taken by using the rules. Inference method used is the fuzzy inference method with defuzzyfication using max-min method and the center average. The output of this software are the types of diseases that attack plants and its control in accordance with the advice of the devastation wrought on the red chilli plants.

Back propagation neural network is part of a multilayered feedforward neural network (MFN) which has been developed and reliable enough to solve the problem of approximation and pattern classification. Application of artificial neural network (ANN) in pattern recognition is one of the signature pattern recognition. Signature of each person are generally identical but not the same. This means that often a person's signature changes every time. This change concerns the position, size and pressure factors signature. Signature is the most widely used form of identification of a person. In general, to identify the signature is still done manually, by matching signatures at the time of the transaction with a valid signature. Therefore, we need a system that can analyze the characteristic signature making it easier to identify the person's signature. The research methodology used in the development of the system is a method Rappid Guidelines for Application Engineering (GRAPPLE), which only covers the design stage needs (Requirement Gathering), analysis (Analysis), the design (Design), and development (Development). This signature recognition process through several stages. First image through image processing stages, where the image will be used as the image of the gray / grayscaling. Once the image is converted into binary data by using thresholding. After going through the binary image processing, the data obtained will be the input value to the training process by using the backpropagation method. The results of the training will be used for the process of signature recognition. Image signatures used in this study were 80 image signatures from 10 respondents. The ratio between training data and testing data is 5:3. The test results show that the signature is able to recognize applications built with precision signature 84% of the tested signatures. Errors in the identification of signatures occur for several reasons, namely: the position of the signature, the image file is damaged, and the learning process is not maximized.

Manual system vehicle parking makes finding vacant parking lots difficult, so it has to check directly to the vacant space. If many people do parking, then the time needed for it is very much or requires many people to handle it. This research develops a real-time parking system to detect parking. The system is designed using the HSV color segmentation method in determining the background image. In addition, the detection process uses the background subtraction method. Applying these two methods requires image preprocessing using several methods such as grayscaling, blurring (low-pass filter). In addition, it is followed by a thresholding and filtering process to get the best image in the detection process. In the process, there is a determination of the ROI to determine the focus area of the object identified as empty parking. The parking detection process produces the best average accuracy of 95.76%. The minimum threshold value of 255 pixels is 0.4. This value is the best value from 33 test data in several criteria, such as the time of capture, composition and color of the vehicle, the shape of the shadow of the object’s environment, and the intensity of light. This parking detection system can be implemented in real-time to determine the position of an empty place.

Mathematics lessons, is one lesson that is not liked by the students. Hearing the words of mathematics, will only think of the word difficult by the students. This is because the medium of learning that there are still less attractive. The material is a material that is considered integral to the most difficult of lessons. Thus the need for an application that can be used to study the material with the pattern of applied learning and interesting .. System development method used is waterfall method of analysis, design, programming, testing, and maintenance. This application uses the programming language PHP, MySql as database, Dreamweaver, Macromedia Flash for animation and Photoshop as graphic design. Web server used is Apache.