Algoritma K-Means untuk Mengelompokkan Hotel di Sekitar Wilayah Indonesia yang Rentan Gempa Bumi
DOI:
https://doi.org/10.30865/resolusi.v4i1.1477Keywords:
Gempa Bumi; Clustering; Algoritma; K-Means; Nilai ShilhoutteAbstract
Due to its location on the Pacific Ring of Fire, Indonesia frequently experiences earthquakes. This phenomenon endangers society and infrastructure, including the hotel industry. Hotels are placed based on locations around areas of Indonesia vulnerable to earthquakes so that hotel owners can use hotel designs and construction that are earthquake-resistant when built. This is because areas with high earthquakes increase the risk of damage to hotel buildings. This research uses hotel geographic location data and Indonesian seismic data to classify hotels in earthquake-prone regions of Indonesia using the K-Means algorithm. Data was taken from USGS from January 2022 to November 2023. Using the K-Means algorithm, hotels were aggregated into clusters based on their proximity to the earthquake epicenter and recorded earthquake magnitude. The research results show that there are two ideal clusters based on the highest Shilhoutte value, namely 0.954, which means it can be sent into two different clusters based on latitude, longitude, and hotel address, with four hotels and three hotels as members of each cluster. Hotels Mutiara, Saparua, and Tiara have a higher level of vulnerability than other hotels because they are located near the earthquake. This research can help hotel owners and policymakers create strategies to reduce risk, increase safety, and develop sustainability in earthquake-prone regions.
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References
I. Setiawan, D. Krismawati, S. Pramana, and E. Tanur, “Klasterisasi Wilayah Rentan Bencana Alam Berupa Gerakan Tanah dan Gempa Bumi di Indonesia,” in Seminar Nasional Official Statistics, 2022, pp. 669–676.
Daryono, “Aktivitas Gempa Bumi di indonesia tahun 2022,” Jakarta, 2022.
A. P. W. Hadi, H. Pratiwi, and I. Slamet, “Pengelompokan Data Gempa Bumi di Indonesia dengan Algoritma K-Means dan DBSCAN,” in Seminar Nasional Pendidikan Matematika Ahmad Dahlan 2023, 2023, pp. 52–60.
M. Muhajir and N. N. Sari, “K-Affinity Propagation ( K-AP ) and K-Means Clustering for Classification of Earthquakes in Indonesia,” 2018 Int. Symp. Adv. Intell. Informatics, pp. 6–10, 2018.
A. Ahmad and N. Irsalinda, “Cluster Analysis of Earthquake ’ s Data Clustering in Indonesia using Fuzzy K-means Clustering,” vol. 3, no. April, pp. 3–7, 2020.
I. H. Rifa, H. Pratiwi, N. Sciences, and U. S. Maret, “Clustering of earthquake risk in indonesia using k-medoids and k-means algorithms,” Media Stat., vol. 13, no. 2, pp. 194–205, 2020, doi: 10.14710/medstat.13.1.194-205.
J. Han, M. Kamber, and J. Pei, Data Mining: Concepts and Techniques Edition: 3rd. Amsterdam: Morgan Kaufmann-Elsevier, 2022.
I. H. Rifa, H. Pratiwi, and Respatiwulan, “Implementasi Algoritma Clara untuk Data Gempa Bumi di Indonesia,” in Seminar Nasional Penelitian Pendidikan Matematika (SNP2M) 2019, 2019, pp. 161–166.
F. Reviantika et al., “Analisis Gempa Bumi Pada Pulau Jawa Menggunakan Clustering Algoritma K-Means,” J. Din. Inform., vol. 9, no. 1, pp. 51–60, 2020.
A. Amalia, U. Harmoko, and G. Yuliyanto, “Clustering of Seismicity in the Indonesian Region for the 2018-2020 Period using the DBSCAN Algorithm,” J. Phys. Its Appl., vol. 4, no. 1, pp. 1–6, 2021.
F. R. Senduk and F. Nhita, “Clustering of Earthquake Prone Areas in Indonesia Using K-Medoids Algorithm,” Ind. J. Comput., vol. 4, pp. 65–76, 2019, doi: 10.21108/indojc.2019.4.3.359.
A. S. Ahmar, D. Napitupulu, and R. Rahim, “Using K-Means Clustering to Cluster Provinces in Indonesia Using K-Means Clustering to Cluster Provinces in Indonesia,” in 2nd International Conference on Statistics, Mathematics, Teaching, and Research, 2018, pp. 0–6.
I. M. B. Suksmadana and C. Ramadhani, “Pengklasteran Kejadian Gempa Wilayah Indonesia Menggunakan Algoritma k-Means,” vol. 8, no. 2, pp. 62–67, 2022.
I. Rahadi, B. A. Novianti, K. Syahidi, M. Putra, M. Gazali, and R. H. Hirzi, “Applying K-Means Algorithm for Clustering Analysis Earthquakes Data in West Nusa Tenggara Province,” Indones. Phys. Rev., vol. 5, no. 3, pp. 197–207, 2022, doi: https://doi.org/10.29303/ipr.v5i3.148.
A. P. Joshi and B. V Patel, “Data Preprocessing?: the Techniques for Preparing Clean and Quality Data for Data Analytics Process,” Orient. J. Comput. Sci. Technol., vol. 13, no. 2–3, pp. 78–81, 2020.
S. Nath, “Analyzing earthquakes in USA to determine the possibly risky to quakes hotels.,” Towards Data Science, 2019. https://towardsdatascience.com/analyzing-the-earthquakes-in-usa-to-determine-the-possibly-risky-to-quakes-hotels-2a9ff162e747 (accessed Nov. 17, 2023).
S. S. Nagari, L. Inayati, U. Airlangga, E. Java, M. V. Midwife, and E. Java, “Implementation of Clustering Using K-Means Method To Determine Nutritional Status,” J. Biometrika dan Kependud., vol. 9, no. July, pp. 62–68, 2020, doi: 10.20473/jbk.v9i1.2020.62.
G. Shmueli, P. C. Bruce, I. Yahav, N. R. Patel, and K. C. Lichtendahl, Data Mining for Business Analytics?: Concepts, Techniques, and Application in R, First. John Wiley & Sons, Inc, 2018.
A. Wanto, M. N. H. Siregar, A. P. Windarto, and D. Hartama, Data Mining?: Algoritma & Implementasi, 1st ed. Medan: Yayasan Kita Menulis, 2020.
M. S. Yana, L. Setiawan, E. M. Ulfa, and A. Rusyana, “Penerapan Metode K-Means dalam Pengelompokan Wilayah Menurut Intensitas Kejadian Bencana Alam di Indonesia Tahun 2013-2018,” J. Data Anal., vol. 1, no. 2, pp. 93–102, 2018.
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