IT infrastructure at PT. Citra Multi Service is a very important part in the company where this is all part of the Foundation or framework that supports a system or organization to maintenance. The prospective employee selection process done by checking and selection is manually done by checking one by one the documentary was sent by the applicant, so make appraisers hassles and inefficient and require quite a long time, and the occurrence of human error as well as an assessment of subjective, sometimes the end result is not the best prospective employees. In this study using two aspects and in the aspect there are a few criteria. The basic Technique consists of aspect criteria Desktop Support, Network Engineer, Server Engineer, Security Engineer and Data Center Engineer. On the aspect of Personality consists of Dexterity, communication, discipline, Team Work and Responsibility. Method or Algorithm used is a Matching Profile. The results of this research is an application that produces the decision supporting systems output a rank of each prospective employees, so the management decision makers can see the ability of each candidate employees based on the rank.

PKH (Hope Family Program) is a government assistance program to help people experiencing poverty problems this program is an aid from the ministry of social affairs in order to reduce social inequality among poor groups. so it is hoped that in the long run it can break the relationship of poverty between generations. so that the next generation can come out of the abyss of poverty due to the increasing quality of human resources produced. The aspects used are health aspects, educational aspects and aspects of social welfare.  The selection of citizens who are not objective recipients of the PKH Program makes it a problem. Many protested against the village's devices in determining which residents were entitled to assistance and sometimes acts of vandalism. so that in this study want to help village devices in selecting citizens who are entitled to receive assistance using the system. The methods used are Simple Additive Weighting (SAW), and User Acceptance Testing (UAT) is used to test the feasibility of the application. A sample of 10 residents who were recommended to receive PKH assistance obtained the results that Mr. Anwar who ranked first for assistance with a score of 80.5 And for the testing UAT earned an average value of 87%.

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Research using artificial neural network methods has been developed as a tool that can help human tasks, one of which is for passion fruit UMKM entrepreneurs. The problem so far that has been faced by UMKM  entrepreneurs of passion fruit  is that it is difficult to identify ripe passion fruit with sweet and sour taste, because there are 6 colors of passion fruit and the color of passion fruit skin is visually slightly different, as well as the texture of maturity. The main purpose of this study was to identify the color structure and texture of the ripeness of passion fruit, in order to recognize the color and texture of the ripeness of passion fruit which is good for processing into syrup, jam, jelly, juice, passion fruit juice powder by entrepreneurs of UMKM of passion fruit. This study empirically tested the color and texture of the ripeness of 10 passion fruit using the perceptron artificial neural network learning method. The data is obtained from an image that will be entered into the program. The results of the identification process using the perceptron artificial neural network from the tests that have been carried out previously, the highest calculation results obtained with the best results using a learning rate of 0.8 and 500 epoch iterations and producing an accuracy of 80%.

Credit is a belief that one is given to a person or other entity which is concerned in the future that will fulfill all the obligations previously agreed. The objective of the research is to do credit analysis to determine the feasibility of a credit crunch, through credit analysis results, to investigate whether the customer is feasible or not. The method used to predict credit worthiness is by using two models, i.e. C4.5 classification algorithm model and Particle Swarm Optimization (PSO)-based C4.5 classification algorithm model. After testing with these two models, it is found that the C4.5 classification algorithm generates a value of 90.99% accuracy and AUC value of 0.911 to the level diagnostics Classification Excellent, but after the optimization with C4.5 classification algorithm based on Particle Swarm Optimization accuracy values amounted to 91.18% and the AUC value of 0.913 to the level of diagnosis Excellent Classification. These both methods have different accuracy level of 0.18%.