Clustering Titik Panas Bumi Pada Potensi Kebakaran Hutan Menggunakan K-Affinity Propagation

  • Sudhan Primantoro Universitas Mulawarman
  • Rito Goejantoro Universitas Mulawarman
  • Surya Prangga Universitas Mulawarman

Abstract

K-Affinity Propagation is a development of affinity propagation from Brendan J. Frey and Delbert Dueck. The purpose of this research is to cluster geothermal hotspots on potential forest fires in Indonesia using K-Affinity Propagation for the period July 2022 and obtain optimal cluster results using standard deviation with ratio calculations. The optimal cluster results are 4 clusters, with the number of members in cluster 1 being 12 members with copies in West Sumatera Province, the number of members in cluster 2 being 12 members with copies in Southeast Sulawesi Province, the number of members in cluster 3 being 4 members with copies in Central Sulawesi Province, the number of members in cluster 4 being 1 member with copies in North Sulawesi Province. The optimal cluster results using standard deviation with the smallest ratio value is cluster 4 with a ratio value of 0.057.

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References

Apriliana, A. dan Muhajir, M. (2019). “K-Affinity Propagation Clustering and GIS on Instagram Account of Tourist Destination in Java,” SEMANTIK, pp. 122-129.
Bunkers, M. J., Miller, J. R., & DeGaetano, A. T. (1996). Definition of elimate regions in the northern plains using an objective cluster modification technique. Journal of Climate, 9 (1), 130-146.
FWI. (2013). Kebakaran Hutan dan Lahan: Bukan Bencana Alam, Awal Bencana Ekologi. Tersedia di: http://fwi.or.id/publikasi/kebakaran-hutan-dan-lahan-bukan-bencana-alam-awal-bencana-ekologi, diakses pada 30 November 2023.
Guojun, G. dan Michael, K. (2014). “Subspace Clustering Using Affinity Propagation,”.
Han, J., Kamber, M. & Pei, J. (2012). Data Mining Concepts and Techniques Third Edition. Waltham USA: Pearson Education, Inc.
LAPAN. (2016). Panduan Teknis (V.01) Informasi Titik Panas (Hotspot) Kebakaran Hutan/Lahan. Jakarta: Pusat Pemanfaatan Penginderaan Jauh Deputi Bidang Penginderaan Jauh-LAPAN, Tersedia di: http://modis-catalog.lapan.go.id/monitoring, diakses pada 9 Februari 2023.
Muhajir, M., & Sari, N.N. (2019). K-Affinity Propagation (K-AP) and K-Means Clustering for Classification of Earthquakes in Indonesia. Proceeding – 2018 International Symposium on Advanced Intelligent Informatics: Revolutionize Intelligent Informatics Spectrum for Humanity, SAIN 2018, 6-10.
Mulaab. (2017). Data Mining Konsep dan Aplikasi. Media Nusa Creative.
Zhang, X., Wang, W., Norvag, K., & Sebag, M. (2010). K-AP: Generating specified K clusters by efficient Affinity Propagation. Proceedings- IEEE International Conference on Data Mining, ICDM, December, 1187-1192.
Published
2024-11-07
How to Cite
PRIMANTORO, Sudhan; GOEJANTORO, Rito; PRANGGA, Surya. Clustering Titik Panas Bumi Pada Potensi Kebakaran Hutan Menggunakan K-Affinity Propagation. EKSPONENSIAL, [S.l.], v. 15, n. 2, p. 73-80, nov. 2024. ISSN 2798-3455. Available at: <https://jurnal.fmipa.unmul.ac.id/index.php/exponensial/article/view/1299>. Date accessed: 21 nov. 2024. doi: https://doi.org/10.30872/eksponensial.v15i2.1299.