In the software development indispensable is the suitability and accuracy in determining the size or value of the software to fit the operation to be performed. A wide variety of calculation methods have been widely used to estimate the size of the software, one of which is by using Function Point Analysis (FPA). Volume calculation software based on a scale of complexity. Since the point of measurement is highly subjective, in order to maintain consistency and validity of the results, the method should be run by an experienced professional. This method is then applied by the authors to measure the complexity of academic information system STIKOM Dinamika Bangsa Jambi using structured modeling approach. Measurements were performed in this study consisted of depictions information system is built into the structure. Which is then analyzed by counting models Crude Function Points (CRP), the relative complexity of Adjustment Factor (RCAF), and then calculate the point function. From the results of calculations using the FPA to software quality measurement academic system STIKOM Dinamika Bangsa Jambi obtained value FP 166.32 is good. Function point value produced will be used by developers in determining the price and the cost of software systems to be built or developed.
The weather anomaly phenomenon that occurs can have some negative impact such as flooding, floods will paralyze the economic activities of the community, transportation activities, damage public infrastructure. In this research forecasting weather parameters as a variable for predicting the amount of rainfall using the ANFIS method and Support Vector Regression (SVR) with the aim to provide information on future weather conditions quickly and accurately. The people can prepare themselves and prepare the equipment needed to deal with it. Rainfall predicted based on synop data such us relative humidity, wind, and temperature. Each parameters must forcasted by using ANFIS and the result used for predict rainfall. Accurate prediction calculated using MSE and RMSE. Predictions of parameters that affect rainfall using the ANFIS method shown that for wind speed predictions having RMSE of 1.975004, temperature predictions have RMSE of 0.742332, and predictions of relative humidity have RMSE of 3.871590. Predicted rainfall based on the data results of the nearest method pre-processing using the Support Vector Regression (SVR) method produces an MSE error value of 0.0928.
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