Improve of Multiobjective Model on the Classification Problem of Food Consumption Levels in Indonesia

Authors

  • Eka Susanti Universitas Sriwijaya, Indonesia
  • Novi Rustiana Dewi Universitas Sriwijaya, Indonesia
  • Arsi Arsi Universitas Sriwijaya, Indonesia

DOI:

https://doi.org/10.15575/kubik.v10i1.40632

Keywords:

Classification, Multiobjective, K-Nearest Neighbor, GridSearchCV

Abstract

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.

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Published

2025-02-04

How to Cite

Susanti, E., Dewi, N. R., & Arsi, A. (2025). Improve of Multiobjective Model on the Classification Problem of Food Consumption Levels in Indonesia. KUBIK: Jurnal Publikasi Ilmiah Matematika, 10(1), 1–9. https://doi.org/10.15575/kubik.v10i1.40632

Issue

Vol. 10 No. 1 (2025): IN PRESS

Section

Articles

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