Classification of High School History Questions Based on Cognitive Level Revised Bloom's Taxonomy Using K-Nearest Neighbor Method

Education plays an important part in transmitting knowledge to its students and to measure how well the student understands, hence testing is needed based on the cognitive level of knowledge. In measuring the cognitive level, it can be applied with reference to the Revised Bloom's Taxonomy which explains the regulation of learning processes and targets. Thus by testing knowledge through questions that have been made, it is necessary to classify the questions into several cognitive levels based on Revised Bloom's Taxonomy to determine learning process and understanding of each individual. With how types of questions are formed in a test may result classifying to be rather difficult because the method is still done manually, and due to how it's still done manually will make it difficult to measure student's cognitive level during the study, therefore machine learning is needed. This research will focus on testing the accuracy result of classification of questions machine learning using the History subject in high school. The dataset used is downloaded from internet searches of USBN, PAS, PTS, and other exams. This study focuses on RBT C4 to C6 only. This research uses the K-Nearest Neighbor algorithm to obtain accuracy and with the imbalance of data in the dataset, an oversampling method using SMOTE will also be used. The accuracy results obtained are precision is 76%, recall is 76%, f1-score is 74%, accuracy is 76%.