Clustering Titik Panas Menggunakan Algoritma Agglomerative Hierarchical Clustering (AHC)
DOI:
https://doi.org/10.31154/cogito.v8i2.438.501-513Keywords:
Clustering, Titik Panas, Agglomerative Hierarchical ClusteringAbstract
Kebakaran hutan di Indonesia setiap tahunnya masih sering terjadi. Dalam menanggulangi kebakaran hutan sendiri, para peneliti belakangan ini semakin fokus untuk melakukan pengembangan sistem yang mampu melakukan prediksi kebakaran hutan. Selain melakukan prediksi mengenai kebakaran hutan, antisipasi yang dapat dilakukan untuk membantu menangani pencegahan kebakaran hutan salah satunya adalah melakukan pengelompokkan terhadap wilayah yang memiliki potensi kebakaran. Pada penelitian ini melakukan Clusterisasi titik panas (hotspot) untuk membagi wilayah yang berpotensi untuk terbakar. Pengelompokkan wilayah dilakukan berdasarkan cluster kebakaran rendah, sedang, dan tinggi. Clusterisasi wilayah dilakukan menggunakan algoritma Agglomerative Hierarchical Clustering (AHC). Data yang digunakan pada perhitungan ini menggunakan wilayah Kalimantan Barat dengan menggunakan variable longitude, latitude, frp, confidence, dan curah hujan untuk menentukan clustering wilayah kebakaran. Tujuan dalam penelitian ini adalah untuk clusterisasi titik panas kebakaran hutan berdasarkan wilayah yang berpotensi terjadi kebakaran hutan sehingga memiliki peluang untuk segera ditindaklanjuti. Dari hasil pengujian diperoleh pembentukan 2 cluster yang dimana menunjukkan terbentuk 2 pengelompokkan titik panas (hotspot) pada kelas sedang dan tinggi dengan nilai evaluasi silhouette coefficient 0,771.References
BNPB, “Kerugian Kebakaran Hutan dan Lahan Sepanjang 2019 Capai Rp 75 Triliun - BNPB,” 2019. https://bnpb.go.id/berita/kerugian-kebakaran-hutan-dan-lahan-sepanjang-2019-capai-rp-75-triliun (accessed Jul. 25, 2022).
S. Sumaryati, N. Cholianawati, and A. Indrawati, “The impact of forest fire on air-quality and visibility in Palangka Raya,” Journal of Physics: Theories and Applications, vol. 3, no. 1, p. 16, Mar. 2019, doi: 10.20961/jphystheor-appl.v3i1.38071.
J. Homepage, K. Pratama Simanjuntak, and U. Khaira, “MALCOM: Indonesian Journal of Machine Learning and Computer Science Hotspot Clustering in Jambi Province Using Agglomerative Hierarchical Clustering Algorithm Pengelompokkan Titik Api di Provinsi Jambi dengan Algoritma Agglomerative Hierarchical Clustering,” vol. 1, pp. 7–16, 2021.
A. C. Meira Castro, A. Nunes, A. Sousa, and L. Lourenço, “Mapping the causes of forest fires in Portugal by clustering analysis,” Geosciences (Switzerland), vol. 10, no. 2, Feb. 2020, doi: 10.3390/geosciences10020053.
A. Vatresia, R. R. Rais, F. P. Utama, and W. Oktarianti, “MINING FIRE HOTSPOTS OVER NUSA TENGGARA AND BALI ISLANDS,” Indonesian Journal of Forestry Research, vol. 9, no. 1, pp. 73–85, 2022, doi: 10.20886/ijfr.2022.9.1.73-85.
N. Huda, N. Debataraja, and E. Sulistianingsih, “ESTIMASI CONFIDENCE TITIK PANAS PADA KEBAKARAN HUTAN MENGGUNAKAN METODE ORDINARY KRIGING”, doi: 10.13140/RG.2.2.35901.87526.
O. Yim and K. T. Ramdeen, “Hierarchical Cluster Analysis: Comparison of Three Linkage Measures and Application to Psychological Data,” 2015.
S. Hirano, X. Sun, and S. Tsumoto, “Comparison of clustering methods for clinical databases,” Information Sciences, vol. 159, no. 3–4, pp. 155–165, Feb. 2004, doi: 10.1016/j.ins.2003.03.011.
F. Murtagh and P. Contreras, “Algorithms for hierarchical clustering: An overview,” Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, vol. 2, no. 1, pp. 86–97, Jan. 2012, doi: 10.1002/widm.53.
E. N. Narciso, M. Delamaro, and F. L. S. Nunes, “Test case selection using CBIR and clustering Test Case Selection Using CBIR View project Automation of test Oracles for the processing of medical images of three-dimensional models View project,” 2013. [Online]. Available: https://www.researchgate.net/publication/290332664
Institute of Electrical and Electronics Engineers and Manav Rachna International Institute of Research and Studies, Proceedings of the International Conference on Machine Learning, Big Data, Cloud and Parallel Computing : trends, prespectives and prospects : COMITCON-2019 : 14th-16th February, 2019.
P. Phetsangkat, K. Chalermyanont, and R. Duangsoithong, “Hierarchical Clustering Electric Load: Case Study in Lower South Region of Thailand,” in 2019 16th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON), Jul. 2019, pp. 881–884. doi: 10.1109/ECTI-CON47248.2019.8955425.
Y. Reinaldi, N. Ulinnuha, and Moh. Hafiyusholeh, “Comparison of Single Linkage, Complete Linkage, and Average Linkage Methods on Community Welfare Analysis in Cities and Regencies in East Java,” Jurnal Matematika, Statistika dan Komputasi, vol. 18, no. 1, pp. 130–140, Sep. 2021, doi: 10.20956/j.v18i1.14228.
Z. G. Wei, X. D. Zhang, M. Cao, F. Liu, Y. Qian, and S. W. Zhang, “Comparison of Methods for Picking the Operational Taxonomic Units From Amplicon Sequences,” Frontiers in Microbiology, vol. 12, Mar. 2021, doi: 10.3389/fmicb.2021.644012.
NCSS and LLC, “445-1 Hierarchical Clustering / Dendrograms.”
M. Forina, C. Armanino, and V. Raggio, “Clustering with dendrograms on interpretation variables,” Analytica Chimica Acta, vol. 454, no. 1, pp. 13–19, Mar. 2002, doi: 10.1016/S0003-2670(01)01517-3.
P. Lemenkova, “R Libraries {dendextend} and {magrittr} and Clustering Package scipy.cluster of Python For Modelling Diagrams of Dendrogram Trees,” Carpathian Journal of Electronic and Computer Engineering, vol. 13, no. 1, pp. 5–12, Sep. 2020, doi: 10.2478/cjece-2020-0002.
G. P. Trayasiwi, “PENERAPAN METODE KLASTERING DENGAN ALGORITMA K-MEANS UNTUK PREDIKSI KELULUSAN MAHASISWA PADA PROGRAM STUDI TEKNIK INFORMATIKA STRATA SATU.”
A. Struyf, M. Hubert, and P. J. Rousseeuw, “Clustering in an Object-Oriented Environment.”
K. K.Mohbey and G. S. Thakur, “An Experimental Survey on Single Linkage Clustering,” International Journal of Computer Applications, vol. 76, no. 17, pp. 6–11, Aug. 2013, doi: 10.5120/13337-0327.
D. T. Utari and D. S. Hanun, “Hierarchical Clustering Approach for Region Analysis of Contraceptive Users,” EKSAKTA: Journal of Sciences and Data Analysis, pp. 99–108, Sep. 2021, doi: 10.20885/eksakta.vol2.iss2.art3.
P. K. Litoria, T. Singh Bhatia, H. Singh, P. K. Litoria, and B. Pateriya, “Web GIS Development using Open Source Leaflet and Geoserver Toolkit,” vol. 9, [Online]. Available: https://www.researchgate.net/publication/326971834
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2022 CogITo Smart Journal
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
