Medan has a high rainfall variability. The amount of rainfall affects the welfare of life such as in the fields of health, economy, agriculture, industry, transportation, tourism and so on. To find out changes in rainfall patterns, a prediction of rainfall levels is designed to see and analyze the rainfall patterns that will form in the future. Forecasting is the art and science of predicting future events by taking historical data and projecting it into the future by using some form of mathematical model. One of the methods used to predict an event is the fuzzy time series method. Fuzzy time series is a concept that can be used to predict problems where historical data is formed in linguistic values. While the latest data as a result are in the form of real numbers. The purpose of this research is to implement the fuzzy time series method to predict rainfall in Medan by comparing several developments of the fuzzy time series method, namely Fuzzy Time Series Chen, Markov Chain and Cheng. In determining the interval in the Fuzzy time series Avergae Based rules are used to get the best results. In this study the result is MAPE value of each method. Chen’s method give MAPE=8.002%, Markov chain’s method give MAPE=30.12% and cheng’s method give MAPE=34.5 %. So the best method for forecasting rainfall is Chen Method.
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.
Optimization is important in an algorithm. It can save the operational costs of an activity. In the Minimum Spanning Tree, the goal is to achieve how all vertices are connected with the smallest weights. Several algorithms can calculate the use of weights in this graph. The purpose of this study is to find out the Primary electricity distribution network graph model and correct algorithm to determine the minimum spanning tree. By comparing two algorithms, Prim’s and Boruvka’s algorithm, it will get an efficient algorithm to solve the minimum spanning tree problem. To get the output it takes several steps: Data collection: Designing Model: calculating the minimum spanning tree of Prim’s algorithm, the Boruvka’s algorithm: Comparing the efficiency of each algorithms. The analysis shows that the Prim’s and Boruvka’s algorithm have different steps even though the final result in the form of weights obtained in achieving the minimum spanning tree is the same. But in the case of electric network optimization, the Prim’s algorithm is more efficient.
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