Analysis of Iterative Dichotomiser 3 Algorithm Uses Fuzzy Curves Shoulder as a Determinant of Grade Value

Data mining is a process that combines statistics, artificial intelligence, mathematics and machine learning to extract data on a large scale in the database. Data mining is always able to analyze the data so as to find the relevance of data that has a meaning and have a tendency to check large-scale data stored in the database to find a meaningful pattern or rules. The increasing availability of data is often not utilized to provide new knowledge so that large data accumulate is meaningless. The purpose of this research is to extract the information so as to produce knowledge through the decision tree and show the accuracy or influence of Iterative Algorithm Dichotomiser 3 which is used to predict a situation. The classes or attributes in the Iterative Algorithm Dichotomiser are continuously broken into relative categories. Fuzzy Curve Shoulder will be used as a function to form the categories of each attribute value. Using a fuzzy shoulder curve, the dataset is processed using a decision tree that is useful for extracting large amounts of data and searching for hidden links between multiple potential input variables with a target variable. The results of this study are decision trees that will provide predictive data with Iterative Dichotomizer (ID) Algorithm 3.