KLASTERISASI CUACA KOTA PALEMBANG MENGGUNAKAN ALGORITMA K-MEANS (STUDI KASUS: BMKG STASIUN KLIMATOLOGI PALEMBANG)

KHAIRUNNISA, SHANAZ and Jambak, M. Ihsan (2022) KLASTERISASI CUACA KOTA PALEMBANG MENGGUNAKAN ALGORITMA K-MEANS (STUDI KASUS: BMKG STASIUN KLIMATOLOGI PALEMBANG). Undergraduate thesis, Sriwijaya University.

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Abstract

Weather related information is one of the things that is very important and has a big influence on all kinds of life activities such as in public safety, socio-economics, agriculture, aviation, and so on.The weather in each place or region is different, this happens because of the different weather elements in each place/region. By using data mining clustering techniques, weather clustering will be carried out in the city of Palembang. K-means is the algorithm chosen for clustering the weather in the city of Palembang. The test was carried out using daily weather data for 2020-2021 from BMKG by utilizing rapidminer and SPSS applications as learning techniques for data. So that we will get a group of weather characteristics of Palembang city based on similarities and dissimilarities. From the test results, the best k was obtained at k=3 with the parameters Measure Types (NumericalMeasure) and Divergences (DynamicTimeWarpingDistance) as well as a local random seed of 2500 seen from the results of the Davies-Bouldin Index (DBI). This weather grouping can later provide information on how the weather character is and reduce the impact of sudden changes in weather conditions.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Data Mining, K-Means, Klasterisasi Cuaca
Subjects:	Q Science > QA Mathematics > QA75-76.95 Calculating machines > QA76.9.D343 Data mining. Database searching. Big data.
Divisions:	09-Faculty of Computer Science > 57201-Information Systems (S1)
Depositing User:	Shanaz Khairunnisa
Date Deposited:	04 Aug 2022 04:26
Last Modified:	04 Aug 2022 04:26
URI:	http://repository.unsri.ac.id/id/eprint/75995

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