K-Means Clustering for Determining Quality of Outdoor Temperature Based on BMKG Datasets K-Means Clustering

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Muhammad Rizky Ramadhan Abd. Charis Fauzan Nurul Aziz Tri Wahyuni Riska Fitri Nur Alifah Adi Diantoro


The purpose of this study was to determine the quality of outdoor temperatures that are good for conducting activities. One of the factors that cause the body's healthy or not human temperature, either indoors or outdoors. The data used is the BMKG weather dataset. BMKG weather dataset is a type of quantitative data because it is numeric or nominal and can be calculated. At each day, BMKG also provides information about the temperature according to the state of each area. It is starting from the minimum temperature, maximum, average humidity, to the maximum wind speed. The method used is clustering using the k-means clustering with centroid value. This research resulted in outdoor temperature clustering, which is good, INTERMEDIATE, and bad quality. In the range of values 124.7 indicate good temperature, range values 133.1 indicate moderate temperature, range of values 146.8 indicate bad temperature. Based on research in 32 days produced 28 days of moderate temperature quality, two days of good temperature quality, and two days of poor quality.

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RAMADHAN, Muhammad Rizky et al. K-Means Clustering for Determining Quality of Outdoor Temperature Based on BMKG Datasets. Journal of Development Research, [S.l.], v. 4, n. 1, p. 12-17, may 2020. ISSN 2579-9347. Available at: <http://journal.unublitar.ac.id/jdr/index.php/jdr/article/view/90>. Date accessed: 18 sep. 2020. doi: https://doi.org/10.28926/jdr.v4i1.90.
Engineering and Technology