Decision tree technique for classifying cassava production

A decision tree is a technique for finding and describing structural patterns in data as tree structures. The tree is composed of a root node, a set of internal nodes, and a set of terminal nodes. Each node of the decision tree structure makes a binary decision that separates either one class or some of the classes from the remaining classes. The processing is carried out by moving down the tree until the terminal node is reached. The aim of this research is to classify the cassava production in regencies and cities in Java Island, Indonesia using Chi-square Automatic Interaction Detection (CHAID), Exhaustive CHAID and Quick-Unbiased-Efficient Statistical Tree (QUEST). The dependent variable is cassava production and the predictors are harvested area, rainfall, temperature, and altitude. CHAID and Exhaustive CHAID analysis yield three classifications, while QUEST analysis yields four classifications. Across the three methods, two factors were identified as influencing factors, namely, rainfall and altitude.