The Development of a Forecast Model for Predicting Product Orders Using Data Mining Techniques
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Abstract
The objective of this research is to test a forecasting model for predicting product orders and determine the efficiency of the system. The researcher created using the Cross Validation 10 Folds method through data mining techniques and find satisfaction survey results of entrepreneurs who have tried using the forecasting model system to predict product orders using data mining techniques. From the 5-level score criteria using basic statistics, namely finding the average and standard deviation. It was found that the Decision Tree technique (J48) had the highest accuracy and was higher than other techniques. It is calculated as 84.84 percent, including the lowest error value, calculated as 15.16 percent, followed by the technique for searching for friends near the house (K-Nearest Neighbors), which has an accuracy value calculated as 81.60 percent, with an error value calculated in hundreds. 18.40 each and the Naïve Bayes technique has the least accuracy value, accounting for 74.11 percent, which has the highest error value, accounting for 25.89 percent.
Therefore, the Cross Validation 10 Folds method through Decision Tree technique (J48) is used to classify learning data of product orders. There were satisfaction scores regarding the development of the order forecasting model system using data mining techniques on the efficiency of the system. From a trial of 50 stores in the central region, the average was 4.12 and the standard deviation was 0.10. The interpretation score was good.
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References
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