KLASIFIKASI TINGKAT KONSUMSI PENDUDUK INDONESIA WILAYAH PERKOTAAN BERDASARKAN KELOMPOK PENGELUARAN MENGGUNAKAN ALGORITMA K-NEAREST NEIGHBOR MULTIOBJECTIVE PARTICLE SWARM OPTIMIZATION (K-NN MOPSO)

SIHOTANG, ANGGELINA MARIA PUTRI and Susanti, Eka and Dwipurwani, Oki (2025) KLASIFIKASI TINGKAT KONSUMSI PENDUDUK INDONESIA WILAYAH PERKOTAAN BERDASARKAN KELOMPOK PENGELUARAN MENGGUNAKAN ALGORITMA K-NEAREST NEIGHBOR MULTIOBJECTIVE PARTICLE SWARM OPTIMIZATION (K-NN MOPSO). Undergraduate thesis, Sriwijaya University.

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Abstract

Classification is the process of grouping objects based on similarities and differences. In this study, a multi-objective classification model was developed with two objective functions, namely functions that maximize the value of accuracy and sensitivity. The model developed is applied to the problem of classifying the level of perpita consumption per week for the attributes of meat, eggs and fish, vegetables, nuts, and fruits. The classification method used is K-Nearest Neighbor(KNN) with two objective functions and the addition of the GridSearchCV module to the KNN calculation. The multiobjective model was completed using the weighting method and Particle Swam Optimization (PSO). The results obtained for objective function weights 1 and 2 were 0.8 and 0.2 respectively with excellent criteria for meat, fish and egg attributes as well as vegetables, nuts, and fruits. The addition of the GridsearchCV module can simplify the calculation of the KNN methodclassification because the model will provide the best K value without having to do repeated calculations.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	KNN, multiobjektif, PSO, KNN MOPSO, tingkat konsumsi
Subjects:	Q Science > QA Mathematics > QA299.6-433 Analysis > Q337.3 Swarm intelligence. Big data -- Social aspects. Information technology -- Economic aspects.
Divisions:	08-Faculty of Mathematics and Natural Science > 44201-Mathematics (S1)
Depositing User:	Anggelina Maria Putri Sihotang
Date Deposited:	22 Jan 2025 07:18
Last Modified:	22 Jan 2025 07:18
URI:	http://repository.unsri.ac.id/id/eprint/166306

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