K-Nearest Neighbor (KNN) is a method applied in classifying objects based on learning data that is closest to the object based on comparison between previous and current data. In the learning process, KNN calculates the distance of the nearest neighbor by applying the euclidean distance formula, while in other methods, optimization has been done on the distance formula by comparing it with the other similar in order to get optimal results. This study will discuss the calculation of the euclidean distance formula in KNN compared with the normalized euclidean distance, manhattan and normalized manhattan to achieve optimization results or optimal value in finding the distance of the nearest neighbor.

Forecasting is apply because of complexity and uncertainty faced by high-dimensional data available in the fields of bioinformatics, chemometrics, banking and other applications. A process for systematically estimating what is most likely to happen in the future based on past and present data requires an appropriate forecasting model, so that the difference between what happens and the estimated results can be minimized. To get the right method, a measuring technique is needed to detect the accuracy of forecasting value. In this paper we discuss the technique of measuring forecasting accuracy with Mean Square Error (MSE) and Mean Absolute Percentage Error (MAPE) using the Random K-Nearest Neighbor (RKNN) method. With the two measuring technique for the horizontal modeling above, the smallest MSE and MAPE values are chosen (the smallest error value). From the results of the analysis of the calculation of forecasting accuracy measurement values during training with RKNN, the MAPE accuracy value is 0.728427% and MSE is 0.545751, while the smallest accuracy value is achieved using MSE which is 0.545751.

Crude palm oil is a crop that has a harvest period of ± 2 weeks and is in dire need of dissemination of information using e-commerce in order to be able to predict the price of the yield of companies or individual gardens within the next 2 weeks in order to improve studies on business intelligence. The disadvantage of not implementing e-commerce is certainly detrimental to the garden owner because they have to go through an agent so prices are set based on the agent. So with the application of e-commerce, buyers of crude palm oil can predict prices in conducting business processes to the future. So the need to forecasting the price of crude palm oil heads in order to improve the application of business intelligence using the evolution-based artificial neural network (ANN) method which in this paper is tested with SECoS get a MAPE value of 0.035% and by applying business intelligence can protect transaction costs by 33.3%.

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The tea plants (Camellia Sinensis) are small tree species that use leaves and leaf buds to produce tea harvested through a monoculture system. It is an agriculture practice to cultivate one types of crop or livestock, variety or breed on a farm annually. Moreover, the emergence of pests, pathogens and diseases cause serious damages to tea plants significantly to its productivity and quality to optimum worst. All parts of the tea plant such as leaves, stems, roots, flowers and fruits are exposed to these harm lead to loss of yield 7 until 10% per year. The intensity of these attacks vary greatly on particular climate, the degree slope and the plant material used. Therefore, this study analyzes tea leaves as a common part used in recipes to create unique taste and flavor in tea production, especially in agro-industry. The decision making method used is Fuzzy Mamdani Inference as one of model with functional hierarchy with initial input based on established criteria. Fuzzy logic will provide tolerance to the set of value, so that small changes will not result in significant category differences, only affect the membership level on the variable value. Previous method using probabilities have shown 78% tea leaves have been attacked by category C (Gray Blight) while using Mamdani indicated 86% of tea leaves have been infected. In this case, this result pointed out that Fuzzy Mamdani Inferences have more optimal result compare to the previous method.

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Forecasting involves all areas in predicting future events. Many problems can be solved by using a forecasting approach to become a study in the field of data science. Forecasting that learns through data in the light age is able to solve problems with large-scale data or big data. With the big data, the performance of the k-Nearest Neighbor (k-NN) method can be tested with several accuracy measurements. Generally, accuracy measurement uses MAPE so it is necessary to conduct sensitivity on MAPE by combining it with the detection rate which is the difference technique. In addition, the k-NN process has been developed for the sake of running sensitivity by performing normalized distance using normalized Euclidean distance so that in this paper using the crude palm oil (CPO) price dataset, it is able to forecast and become a future model and apply it to Business Intelligence and analysis. In the final stage of this paper, the accuracy value in doing big data forecasting on CPO prices with MAPE is 0.013526% and MAPE sensitivity combined with a detection rate of 0.000361% so that future processes using different methods need to involve detection rates.

Business is an interpersonal and organizational activity that involves the process of selling, purchasing both goods and services with the aim of making a profit. But to get a large profit, it takes many partners who have a high desire to move forward. Information technology provides services for business people so that media information is available as a sign of obstacles. In addition it is necessary to do modeling where the process of communication between businesses running on information technology has a different profit from the business being run. Thus the union has the principle of kinship and has the principle of profitability divided by the amount of contribution given so that the creation of a model in electronic business (e-business) in the hope of having a family principle that is able to provide special profits for businesses other than the profits that run on certain businesses.