The effect of a SECoS in crude palm oil forecasting to improve business intelligence

Abstract

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%.

Sensitivity of MAPE using detection rate for big data forecasting crude palm oil on k-nearest neighbor

“…As in Al-Khowarizmi [20] predicting the price of crude palm oil (CPO) and turning it into business intelligence in order to cut processing operating costs are so high that the method used by reference in making predictions is the Simple Evolving Connectionist System method. CPO is a product that is transacted in the commodity market or natural resource product which is commonly processed into various derivative products, both in the form of consumer goods and industrial raw materials [21][22][23].…”

“…However, Prayudani [26] compared the smallest error value with MAPE, MAE and MSE where the MAPE and MAE results have the same value except that the difference is in units using percent (%). In addition, by getting the value of accuracy and the smallest error value, it will increase productivity in business intelligence [20]. So that the application of data science has been used in various fields of science such as in this paper to measure accuracy to get the smallest error value that will be used to do big data forecasting on CPO prices using the KNN method so that it can be used as a model in a business intelligence and analysis (BIA).…”

Classification of skin cancer images by applying simple evolving connectionist system

“…Although these traditional classifier may result high accuracy for given dataset, future complex and rich dataset require more efficient classifier. Simple evolving connectionist system (SECoS) or so called as evolved multilayer perceptron [12] is the smallest realization of ECoS that has capability in forecasting big data pattern. This method is powerfull in processing the rich dataset.…”

“…Since data is not a text type, feature extractions were performed [22], [23]. SECoS can be integrated into a single image processing module [24], [25], for instance, to isolate phoneme recognition [26] where large data patten can be memorized and new inputted data can be adapted. SECoS reduces a large number of hidden nodes.…”

Optimization of Applied Detection Rate in the Simple Evolving Connectionist System Method for Classification of Images Containing Protein

“…MAPE is the average of the overall percentage of error (difference) between the actual data with forecasting data that is measured and matched with time-series data and shown in percentage [24]. Meanwhile, to get the difference value based on the original value and forecast value can be calculated with the Detection rate formula [25]. So, the expectation of this paper is to get knowledge contribution by combining MAPE and detection rate in the process of classifying images containing protein as test data, namely egg images using the SECoS Method model to be optimal [26].…”