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CLUSTERIZATION OF REGION IN SOUTH SUMATERA BASED ON COVID-19 CASE DATA Saragih, Anita; Sukanda, Dian Cahyawati; Eliyati, Ning
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 17 No 3 (2023): BAREKENG: Journal of Mathematics and Its Applications
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol17iss3pp1257-1264

Abstract

Based on Covid-19 case data as of July 2022, South Sumatra Province has the 15th highest rank out of 34 provinces in Indonesia, with confirmed cases totalling 82,407. This showed that the spread of Covid-19 in South Sumatra was still high. This study aimed to determine the cluster of regions in South Sumatra based on Covid-19 case data. Clustering regions used agglomerative hierarchical method. The process began with standardizing the data, calculating the similarity distance between objects, determining the optimal number of clusters using the Silhouette method, and the last was clustering analysis. This study found that the optimal number of clusters consisted of two clusters. The clustering process starts with objects 2 and objects 4 because these two objects have the closest similarity distance. In conclusion, objects with the closest similarity distance (in one cluster) have the same data movement (fluctuation).
Classification of weather events in Lahat regency using the K-Nearest Neighbor method Kresnawati, Endang Sri; Resti, Yulia; Eliyati, Ning; Zayanti, Des Alwine; Dewi, Novi Rustiana; Yani, Irsyadi
Bulletin of Applied Mathematics and Mathematics Education Vol. 5 No. 2 (2025)
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/bamme.v5i2.14613

Abstract

Weather event classification in a region is very important for various purposes, such as in the fields of transportation, health, agriculture, and others. Lahat has varying land elevations ranging from 26-106 meters above sea level in the East Merapi sub-district to 341-3032 meters above sea level in the Tanjung Sakti Pumi sub-district. It greatly affects local temperature, rainfall, and atmospheric pressure, which in turn affects the distribution of weather patterns and disasters such as floods. KNN is a prediction method that uses the concept of distance for a number of k nearest observations in determining the similarity between observations. Several metrics can be used for this prediction purpose. This study aims to predict weather events in Lahat Regency using the KNN method with several different distance metrics and then compare them to obtain the performance of the KNN prediction method. The results show that the Euclidean distance metric used in the KNN method has a better performance measurement, followed by the Manhattan and Minkowski metrics. In the Euclidean metric, the accuracy, precision, recall, f1-score, AUC, and MC value are 92.69%, 88.21%, 85.81%, 86.99%, 88.99%, and 76.37%, respectively.