Analytical Hierarchy Proccess with Data Mining Classification to Reduce Traffic Accident Level

The high rate of traffic accidents in Indonesia is certainly detrimental to the various parties, both government and society. There have been many loss both materially and even human lives due to this condition. The low awareness of the drivers is one reason why it is often the case. This study used a combination of methods Analytical Hierarchy (AHP) proccess by Classification Data Mining. The combination is expected to be able provide a solution for parties involved in an effort to reduce the number of traffic accidents. After an analysis the human factor ranks first that caused the accident was followed by the condition of the vehicle, road and weather conditions. While K-Nearest Neighbor (KNN) is the best model classification of the existing method, through the T test and validation with a value of 0.900 and 90%. The best value for testing data is KNN, Naive Bayes and Neural Network with a value of 100%. The system created is included in the good category and helps management because after testing the software quality assurance score 83.