KLASIFIKASI KABUPATEN KOTA DI INDONESIA BERDASARKAN TINGKAT PENGELUARAN PERKAPITA DALAM KELOMPOK MAKANAN RINGAN MENGGUNAKAN ALGORTIMA K-NEAREST NEIGHBOR MULTI OBJECTIVE PARTICLE SWARM OPTIMIZATION (K-NN MOPSO)

SRIKANDI, AGISTHA and Susanti, Eka and Dewi, Novi Rustiana (2025) KLASIFIKASI KABUPATEN KOTA DI INDONESIA BERDASARKAN TINGKAT PENGELUARAN PERKAPITA DALAM KELOMPOK MAKANAN RINGAN MENGGUNAKAN ALGORTIMA K-NEAREST NEIGHBOR MULTI OBJECTIVE PARTICLE SWARM OPTIMIZATION (K-NN MOPSO). Undergraduate thesis, Sriwijaya University.

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Abstract

This study aims to classify regencies / cities in Indonesia based on the level of expenditure in the snack food group by applying the K-Nearest Neighbor (K-NN) algorithm, and K-NN which is optimized using Multi Objective Particle Swarm Optimization (MOPSO). The classification results with K-NN show that the accuracy value is 90% which is classified as good, but the f1_score value of 75.00%, precision 71.43%, and recall 78.95% are still in the sufficient category. Furthermore, calculations using the K-NN MOPSO algorithm obtained an accuracy of 96%, f1_score, precision, and recall values of 93.50%, including in the excellent category. The completion of the K-NN MOPSO algorithm was carried out using the Python programming language, with the help of the RandomizedSearchCV module to determine the best K parameter without the need to test all other parameter combinations in the classification process, as well as the Distributed Evolutionary Algorithms in Python (DEAP) module to optimally implement the MOPSO framework. The results show that combining K-NN and MOPSO can significantly improve classification performance compared to the K-NN algorithm.

Item Type:	Thesis (Undergraduate)
Subjects:	Q Science > QA Mathematics > QA1-43 General
Divisions:	08-Faculty of Mathematics and Natural Science > 44201-Mathematics (S1)
Depositing User:	Agistha Srikandi
Date Deposited:	23 May 2025 02:26
Last Modified:	23 May 2025 02:26
URI:	http://repository.unsri.ac.id/id/eprint/173760

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