VARIANSI: Journal of Statistics and Its Application on Teaching and Research
Vol. 5 No. 03 (2023)

Metode Radial Basis Function Neural Network Untuk Klasifikasi Kab/Kota Tertinggal Di Provinsi Sulawesi Selatan

Ruliana, Ruliana (Unknown)
Rais, Zulkifli (Unknown)
Mar'ah, Zakiyah (Unknown)
Hasnita, Hasnita (Unknown)



Article Info

Publish Date
31 Dec 2023

Abstract

A disadvantaged area is an area that has the characteristics of tending to be left behind compared to other areas. Radial basis function neural networks are a part of Artificial Neural Networks, which use radial basis activation functions and are commonly used in classification cases. All districts/cities in South Sulawesi province have different characteristics from other districts/cities. Therefore, districts/cities are grouped into 2 groups to identify districts/cities that have characteristics that tend to be the same based on indicators of regional underdevelopment. The grouping results are then used as actual values ​​for classification using the RBFNN method, to determine the classification results and performance of the RBFNN method. In classifying districts/cities in South Sulawesi province based on indicators of regional underdevelopment using the radial basis function neural network method, an accuracy value of 91% was obtained using a comparison of 55% training data and 45% testing data and an f-measure value of 92% was obtained

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Journal Info

Abbrev

variansi

Publisher

Subject

Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Mathematics

Description

VARIANSI: Journal of Statistics and Its application on Teaching and Research memuat tulisan hasil penelitian dan kajian pustaka (reviews) dalam bidang ilmu dasar ataupun terapan dan pembelajaran dari bidang Statistika dan Aplikasinya dalam pembelajaran dan riset berupa hasil penelitian dan kajian ...