Urea Fertilizer Quality Testing with Chi-Squared Automatic Interaction Detection (CHAID) Algorithm - Ahmad Nauvan Zikri Al Ghifran; Yunita Yunita; Desty Rodiah (Sriwijaya Journal of Informatics and Applications, 2020)
PT. XYZ has a Laboratory section in each of its factories that performs its duties manually to determine the quality of the fertilizers to be produced. This manual method is most likely at risk of human error and causes errors in the results of determining the quality of urea fertilizer. An expert system was built using the Chi-Squared Automatic Interaction Detection (CHAID) algorithm which can test the quality of urea fertilizer. The CHAID algorithm applies the decision tree technique where the technique will always branch off two or more as a basis in establishing rules. The system takes the values of the urea fertilizer test parameters as attributes. These attributes are processed to produce the most significant values that will be branches in the decision tree. The parameters used include Nitrogen, Biuret, Moisture, Free Ammonia, Iron, Oil Content, Crushing Strength, and Size Distribution. CHAID algorithm is suitable to be used to test the quality of urea fertilizer because in this study produced 4 different decision trees with an accuracy value of 99% using as much as 100 test data. This number influenced by the amount of training data used to build the rules.