Pengelompokan Cuaca Kota Palembang Menggunakan Algoritma K-Means Clustering Untuk Mengetahui Pola Karakteristik Cuaca

Authors

  • Shanaz Khairunnisa Universitas Sriwijaya, Palembang
  • Muhammad Ihsan Jambak Universitas Sriwijaya, Palembang

DOI:

https://doi.org/10.30865/mib.v6i4.4810

Keywords:

Weather, Clustering, K-Means, Rapidminer, DBI, SPSS

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 application 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.

References

M. Yulianto and D. A. P. Putri, “Pengembangan Game Edukasi Pengenalan Iklim dan Cuaca untuk Siswa Kelas III Sekolah Dasar,†Emit. J. Tek. Elektro, vol. 20, no. 2, pp. 128–133, 2020, doi: 10.23917/emitor.v20i02.9088.

D. Iskandar, Ensiklopedia Seri Cuaca dan Iklim. Alprin, 2020.

BMKG, Masyarakat Indonesia Sadar Iklim dan Cuaca. 2019.

M. Herviany, S. P. Delima, T. Nurhidayah, and Kasini, “Perbandingan Algoritma K-Means dan K-Medoids untuk Pengelompokkan Daerah Rawan Tanah Longsor di Provinsi Jawa Barat,†MALCOM Indones. J. Mach. Learn. Comput. Sci., vol. 1, no. 1, pp. 34–40, 2021.

J. P. Han Jiawei, Micheline Kamber, “Data Mining Concepts and Techniques,†3rd ed., USA: Elsevier Inc, 2012.

P. Alkhairi and A. P. Windarto, “Penerapan K-Means Cluster pada Daerah Potensi Pertanian Karet Produktif di Sumatera Utara,†Semin. Nas. Teknol. Komput. Sains, pp. 762–767, 2019.

L. Fimawahib and E. Rouza, “Penerapan K-Means Clustering pada Penentuan Jenis Pembelajaran di Universitas Pasir Pengaraian,†INOVTEK Polbeng - Seri Inform., vol. 6, no. 2, p. 234, 2021, doi: 10.35314/isi.v6i2.2096.

I. Wahyudi, S. Bahri, and P. Handayani, “Aplikasi Pembelajaran Pengenalan Budaya Indonesia,†vol. V, no. 1, pp. 135–138, 2019, doi: 10.31294/jtk.v4i2.

F. Farahdinna, I. Nurdiansyah, A. Suryani, and A. Wibowo, “Perbandingan Algoritma K-Means Dan K-Medoids Dalam Klasterisasi Produk Asuransi Perusahaan Nasional,†J. Ilm. FIFO, vol. 11, no. 2, p. 208, 2019, doi: 10.22441/fifo.2019.v11i2.010.

Y. Darnita, R. Toyib, and Y. Kurniawan, “Penerapan Metode K-Means Clustering Pada Aplikasi Android Pada Tanaman Obat Herbal,†Pseudocode, vol. 7, no. 2, pp. 105–114, 2020, doi: 10.33369/pseudocode.7.2.18-27.

R. Sibarani and O. Omby, “Algorithma K-Means Clustering Strategi Pemasaran Penerimaan Mahasisswa Baru Universitas Satya Negara Indonesia,†J. Algoritm. Log. dan Komputasi, vol. 1, no. 2, pp. 44–50, 2018, doi: 10.30813/j-alu.v1i2.1367.

T. R. Rivanthio and M. Ramdhani, “Penerapan Teknik Clustering Data Mining untuk Memprediksi Kesesuaian Jurusan Siswa (Studi Kasus SMA PGRI 1 Subang),†Fakt. Exacta, vol. 13, no. 2, p. 125, 2020, doi: 10.30998/faktorexacta.v13i2.6588.

D. Feblian and D. U. Daihani, “Implementasi Model Crisp-Dm Untuk Menentukan Sales Pipeline Pada Pt X,†J. Tek. Ind., vol. 6, no. 1, 2017, doi: 10.25105/jti.v6i1.1526.

Risman, Syaripuddin, and Suyitno, “Implementasi Metode Dbscan Pada Pengelompokan Kabupaten/Kota Di Pulau Kalimantan Berdasarkan Indikator Kesejahteraan Rakyat,†Pros. Semin. Nas. Mat., pp. 22–28, 2019.

S. Santoso, Mahir Statistik Parametrik. Elex Media Komputindo, 2019.

H. J. A. Rohmah Maghfirotur, “PENGARUH MODEL PEMBELAJARAN INKUIRI TERBIMBING TERHADAP KETERAMPILAN PROSES SAINS FISIKA PADA HUKUM KE NOL TERMODINAMIKA,†vol. 9, no. 2, 2022.

M. K. Halimah Tus Sadiah, M. C. Muhamad Saad Nurul Ishlah, S. F. A. M. F. Nisa Najwa Rokhmah, and M. F. Zaldy Rusli, Aplikasi Komputer Farmasi: Buku Ajar Hasil Penelitian Hibah Dikti 2019. Lembaga Penelitian dan Pengabdian pada Masyarakat Universitas Pakuan, 2019.

A. Quraisy and N. Hasni, “Analisis Kruskal-Wallis Terhadap Kemampuan Numerik Siswa,†VARIANSI J. Stat. Its Appl. Teach. Res., vol. 3, no. 3, pp. 156–161, 2021, doi: 10.35580/variansiunm29957.

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Published

2022-10-25

Issue

Vol. 6 No. 4 (2022): Oktober 2022

Section

Articles

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