Senasli LamiaMohammed ChetiouiMehdi DamouSenasli Nour El Houda SarahAbdelhakim BoudkhilMustafa SeçmenAbdelhakim, BoudkhilMohammed, ChetiouiLamia, SenasliSecmen, MustafaSarah, Senasli Nour El HoudaMehdi, Damou2025-10-062024155303961553-03962-s2.0-85213031392https://www.scopus.com/inward/record.uri?eid=2-s2.0-85213031392&partnerID=40&md5=01635ba7e0206407afedad9087ac6c6ahttps://gcris.yasar.edu.tr/handle/123456789/8143In the field of microwave modelling and design the Supervised K-Nearest Neighbor (S-KNN) algorithm has emerged as a valuable tool. An S-KNN based approach to the modelling of passband trisection filter components with emphasis on the optimization of the hyperparameter K is presented in this paper. Our technique introduces a novel S-KNN topology specifically designed for parametric modeling of microwave components with S-parameters as outputs. Unlike previous methods which often rely on manual parameter tuning or lack robust hyperparameter optimization our optimized S-KNN model achieves high accuracy (0.9429) and low mean squared error (0.0109) by tuning K to the specific dataset. The study compares various distance metrics and employs Euclidean distance for predictions. Results demonstrate that the optimized S-KNN model achieves strong alignment with electromagnetic (EM) simulations with an average accuracy of 94.29% offering a faster and potentially more efficient alternative to traditional design techniques. © 2024 Elsevier B.V. All rights reserved.Englishinfo:eu-repo/semantics/closedAccessDistance Metric, Em Simulations, K Nearest Neighbor, Microwave Filter, Rmse, S-parameters, Training-testingDistance MetricK Nearest NeighborEM SimulationsTraining-testingMicrowave FilterRMSES-parametersArticle