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

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

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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%.

Availability
Inventory Code Barcode Call Number Location Status
2507000966T166690T1666902025Central Library (Reference)Available but not for loan - Not for Loan
Detail Information
Series Title
-
Call Number
T1666902025
Publisher
: Prodi Ilmu Matematika, Fakultas Matematika Dan Ilmu Pengetahuan Alam Universitas Sriwijaya.,
Collation
xvii, 86 hlm., ilus., tab.; 29 cm
Language
Indonesia
ISBN/ISSN
-
Classification
510.07
Content Type
-
Media Type
-
Carrier Type
-
Edition
-
Subject(s)
Matematika
Specific Detail Info
-
Statement of Responsibility
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