KLASIFIKASI TINGKAT KONSUMSI PENDUDUK INDONESIA WILAYAH PERDESAAN BERDASARKAN KELOMPOK PENGELUARAN MENGGUNAKAN MODEL K-NEAREST NEIGHBORS MULTIOBJECTIVE PARTICLE SWARM OPTIMIZATION (KNN-MOPSO)

RAJA, DHEA ADELINA LUMBAN and Susanti, Eka and Dwipurwani, Oki (2025) KLASIFIKASI TINGKAT KONSUMSI PENDUDUK INDONESIA WILAYAH PERDESAAN BERDASARKAN KELOMPOK PENGELUARAN MENGGUNAKAN MODEL K-NEAREST NEIGHBORS MULTIOBJECTIVE PARTICLE SWARM OPTIMIZATION (KNN-MOPSO). Undergraduate thesis, Sriwijaya University.

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Abstract

Classification is one of the data processing models to group data according to various categories. This study aims to classify the level of food consumption per capita per week using the K-Nearest Neighbor (KNN) model optimized with the Multi-objective Particle Swarm Optimization (MOPSO) approach which plays a role in determining the optimal K parameter and optimizing three objective functions by maximizing the value of accuracy, sensitivity and specificity with the addition of the GridsearchCV module to classify the level of food consumption in the attribute groups of meat, fish, and eggs as well as vegetables, fruits and nuts based on expenditure grouping. The results show that the applied model is able to improve good classification performance, namely accuracy on the attributes of meat, fish and eggs to 86%, sensitivity to 86% and Specificity to 80%.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Klasifikasi, KNN, MOPSO, Konsumsi Makanan
Subjects:	Q Science > QA Mathematics > QA1-939 Mathematics > QA37.3.1.64 Applied Mathematics T Technology > T Technology (General) > T57-57.97 Applied mathematics. Quantitative methods
Divisions:	08-Faculty of Mathematics and Natural Science > 44201-Mathematics (S1)
Depositing User:	Dhea Adelina Lumban Raja
Date Deposited:	24 Jan 2025 02:14
Last Modified:	24 Jan 2025 02:14
URI:	http://repository.unsri.ac.id/id/eprint/166690

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