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

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Penilaian

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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.

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Availability

Inventory Code	Barcode	Call Number	Location	Status
2507000958	T166306	T1663062025	Central Library (Reference)	Available but not for loan - Not for Loan

Detail Information

Series Title

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Call Number

T1663062025

Publisher

: Prodi Ilmu Matematika, Fakultas Matematika Dan Ilmu Pengetahuan Alam Universitas Sriwijaya., 2025

Collation

xvi, 118 hlm., ilus., tab.; 29 cm

Language

Indonesia

ISBN/ISSN

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Classification

510.07

Content Type

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Media Type

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Carrier Type

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Edition

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Subject(s)

Matematika

Specific Detail Info

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Statement of Responsibility

EM

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