Decision tree algorithm is most popular for classification in machine learning and uses discrete data for classification. Information gain or Gini index is used for the entropy calculation in order to classify the given data. Decision tree can be implemented in several programming languages and many data mining tools uses this algorithm. Every implementation has its own advantages and disadvantages. To understand the difference between two implementations R-studio and Java. This paper explains about two different implementation methods gives the best one among two. We mainly focus on pros and cons of these two implementation methods