Improve of Multiobjective Model on the Classification Problem of Food Consumption Levels in Indonesia - Eka Susanti; Novi Rustiana Dewi; Arsi Arsi (Department of Mathematics, Faculty of Science and Technology, UIN Sunan Gunung Djati Bandung, 2025)
Classification is the process of grouping objects based on similarities and differences. In this article, a multi-objective classification model is developed with three objective functions, namely the function that maximizes the values of accuracy, sensitivity and specificity. The developed model is applied to the problem of classifying meat, egg and fish consumption levels. The classification method used is K-Nearest Neighbor (KNN) with three objective functions and the addition of the GridSearchCV module to the KNN calculation. Completion of the multiobjective model using the weighting method and Particle Swam Optimization (PSO). Based on the data, with objective function weights of 1, 2 and 3 respectively being 0.7, 0.15 and 0.15, the results obtained for Rural Areas Meat, Fish and Egg Attributes of the model performance are in good criteria. for Urban Areas Attributes of Meat, Fish and Eggs the model's performance in the criteria is very good. Addition of the GridsearchCV module can facilitate the calculation of the KNN method classification because the model will provide the best k value without having to do repeated calculations.