Multi-Objective KNN Algorithm Based on the Level Expenditure in the Snack Food Group for Classification of City in Indonesia (Sainmatika: Jurnal Ilmiah Matematika dan Ilmu Pengetahuan Alam, 2025)
Classification is an analytical process aimed at grouping objects into specific categories based on the relationships between attributes. Classification can be used in planning the supply and distribution of specific products. This study aims to classify cities in Indonesia based on the level of expenditure in the snack food group by applying the Multi Objective Particle Swarm Optimization K-Nearest Neighbor (KNN-MOPSO). The results of this classification can be used to see an overview of the level of food expenditure in each district/city in Indonesia. The KNN-MOPSO algorithm is solved using Python programming. The RandomizedSearchCV module is used to determine the best k parameters and the Distributed Evolutionary Algorithms in Python (DEAP) module is used for the MOPSO solving stage. The ratio of training data and testing data used to 80% and 20% from 496 dataset. Based on testing data, there are 81 districts/cities in the low category and 19 districts/cities in the high category.The accuracy results obtained are 96% in very good criteria and F1 score 93.50%. Based on the data, the application of the Multiobjective KNN algorithm with the addition of the searchCV and DEAP modules can improve model performance.