Abstract. Support vector machine (SVM) is a popular classification method known to have strong generalization capabilities. SVM can solve the problem of classification and linear regression or nonlinear kernel which can be a learning algorithm for the ability of classification and regression. However, SVM also has a weakness that is difficult to determine the optimal parameter value. SVM calculates the best linear separator on the input feature space according to the training data. To classify data which are non-linearly separable, SVM uses kernel tricks to transform the data into a linearly separable data on a higher dimension feature space. The kernel trick using various kinds of kernel functions, such as : linear kernel, polynomial, radial base function (RBF) and sigmoid. Each function has parameters which affect the accuracy of SVM classification. To solve the problem genetic algorithms are proposed to be applied as the optimal parameter value search algorithm thus increasing the best classification accuracy on SVM. Data taken from UCI repository of machine learning database : Australian Credit Approval. The results show that the combination of SVM and genetic algorithms is effective in improving classification accuracy. Genetic algorithms has been shown to be effective in systematically finding optimal kernel parameters for SVM, instead of randomly selected kernel parameters. The best accuracy for data has been upgraded from kernel Linear: 85.12%, polynomial: 81.76%, RBF: 77.22% Sigmoid: 78.70%.

Students at almost all levels of education are required to be technology literate, especially information technology. In addition to supporting educational activities, it also supports students in solving administrative problems. One of the software that can be used to support education and administrative issues is Microsoft Office. The application has many features that are still not familiar to some users, especially students. This was experienced by STT Baptist Medan who was not familiar with the many tools provided in Microsoft Word, Microsoft Excel, and Microsoft PowerPoint software. Some of the obstacles that are often encountered are the less than optimal use of the merge function in Word, the function of conditional formulas in Excel and animation, and multimedia in PowerPoint. During the implementation of service activities, it was concluded that the importance of direct Microsoft office training to students, especially study programs outside of computers. In the implementation of the training, students are able to use Microsoft Excel to manage numerical data. Microsoft word in managing data. Also the use of Microsoft PowerPoint in making presentations.

The decision support system for predicting the amount of bakery and cake production using the fuzzy mamdani method is one of the processes for determining the prediction of the amount of bakery and cake production for the next month so that the desired production amount is according to needs. Neko-Neko Bakery & Cake so far in predicting the amount of production where the demand for bakery and cake is sometimes not fulfilled considering that the available bakery and cake production is not sufficient because the bakery and cake supplies in the production section do not meet and vice versa, namely the amount of production sometimes experiences excess in production. production so that it is not in accordance with demand, then there is often an error in predicting the amount of production in producing bakery and cake because the number of bakery and cake productions produced is not really needed while bakery and cake which are often needed are not produced since so far the Neko-Neko Bakery and Cake in Prediction data collection on the number of bakery and cake productions only relies on the Microsoft Office Excel application system where the data that is processed or processed is sometimes double in value. The method used in the decision support system to predict the amount of bakery and cake production is fuzzy mamdani. Mamdani fuzzy method is one method that has a simple structure and easy to understand. Mamdani fuzzy logic uses MIN-MAX or max-product operations with a predetermined set of rules, namely the previous IF…AND…THEN. Based on the application of this mamdani fuzzy method in predicting the amount of bakery and cake production, it can be stated that it is very feasible to apply to Neko-Neko Bakery & Cake. This decision support system was built using the PHP programming language and MySQL database.

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