Penerapan Metode K-Means dalam Pengelompokan  Wilayah Menurut Intensitas Kejadian Bencana Alam  di Indonesia Tahun 2013-2018

Yana, Mira Suci; Setiawan, Lathifah; Ulfa, Elvitra Mutia; Rusyana, Asep

doi:10.24815/jda.v1i2.12584

Cited by 15 publications

(11 citation statements)

References 2 publications

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“…A study by Supriyadi et al (2018) used k-means to classify disasterprone areas into three clusters: high, medium, and low. In addition, Yana et al (2018) found two regional clusters in Indonesia, namely prone to and not prone to natural disasters. Prihandoko and Bertalya (2016) suggested the cluster correlation between natural disasters, the number of victims, and weather conditions using k-means.…”

Section: Clustering Disaster-prone Areas and Mitigationmentioning

confidence: 99%

“…Cluster methods such as k-means have been widely used to group areas with relatively the same number of disaster characteristics to see which areas are prone to natural disasters (Yana et al, 2018). A study by Supriyadi et al (2018) used k-means to classify disasterprone areas into three clusters: high, medium, and low.…”

Section: Clustering Disaster-prone Areas and Mitigationmentioning

confidence: 99%

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A Hybrid K-Means Hierarchical Algorithm for Natural Disaster Mitigation Clustering

Prasetyadi¹,

Nugroho²,

Tohari³

2022

Journal of Information and Communication Technology

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Cluster methods such as k-means have been widely used to group areas with a relatively equal number of disasters to determine areas prone to natural disasters. Nevertheless, it is dificult to obtain a homogeneous clustering result of the k-means method because this method is sensitive to a random selection of the centers of the cluster. This paper presents the result of a study that aimed to apply a proposed hybrid approach of the combined k-means algorithm and hierarchy to the clustering process of anticipation level datasets of natural disaster mitigation in Indonesia. This study also added keyword and disaster-type ields to provide additional information for a better clustering process. The clustering process produced three clusters for the anticipation level of natural disaster mitigation. Based on the validation from experts, 67 districts/cities (82.7%) fell into Cluster 1 (low anticipation), nine districts/cities (11.1%) were classiied into Cluster 2 (medium), and the remaining ive districts/cities (6.2%) were categorized in Cluster 3 (high anticipation). From the analysis of the calculation of the silhouette coeficient, the hybrid algorithm provided relatively homogeneous clustering results. Furthermore, applying the hybrid algorithm to the keyword segment and the type of disaster produced a homogeneous clustering as indicated by the calculated purity coeficient and the total purity values. Therefore, the proposed hybrid algorithm can provide relatively homogeneous clustering results in natural disaster mitigation.

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Section: Clustering Disaster-prone Areas and Mitigationmentioning

confidence: 99%

Section: Clustering Disaster-prone Areas and Mitigationmentioning

confidence: 99%

A Hybrid K-Means Hierarchical Algorithm for Natural Disaster Mitigation Clustering

Prasetyadi¹,

Nugroho²,

Tohari³

2022

Journal of Information and Communication Technology

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“…Sebagian besar setiap provinsi di Indonesia mempunyai catatan bencana alam yang telah terjadi. Dampak dari kejadian bencana alam tersebut sering sekali menimbulkan kerugian berupa harta benda dan korban jiwa [1]. Sekitar 13% dari gunung berapi dunia yang bertempat di kepulauan Indonesia memiliki potensi untuk memicu bencana dengan kekuatan dan intensitas yang berbeda.…”

Section: Pendahuluanunclassified

Pengujian Optimization dan Non-Optimization Query Metode Topsis untuk Menentukan Tingkat Kerusakan Sektor Bencana Alam

Safitri

Almais²,

Syauqi

et al. 2022

eltikom

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Volume data yang sangat besar dari tim surveyor Perencanaan dan Pengendalian Penanganan Bencana(P3B) menciptakan masalah yang luas dan beragam sehingga dapat menghabiskan sumber daya sistem dan waktu pemrosesan yang terbilang lama. Oleh karena itu penelitian ini mengusulkan solusi dengan melakukan Optimasi query pada metode TOPSIS yang diimplementasikan pada sistem pendukung kepeutusan untuk menentukan tingkat kerusakan pasca bencana. Berdasarkan 3 kali uji coba dengan jumlah data yang berbeda-beda yaitu ujicoba ke-1 menggunakan 114 data, ujicoba ke-2 sebanyak 228 data dan ujicoba ke-3 menggunakan 334 data. Selain itu, setiap ujicoba dilakukan lagi pengukuran re-spons time sebanyak 3 kali maka didapatkan hasil rata-rata (average) response time dari masing-masing langkah metode TOPSIS. Didapati bahwa hasil dari tahapan perangkingan menggunakan query optimiza-tion lebih cepat 0.00076 dibandingakan dengan qury non-optimization. Sehingga dapat di simpulkan bahwa response time yang didapat query optimization pada setiap langkah metode TOPSIS pada sistem pendukung keputusan kerusakan sektor pasca bencana alam lebih kecil dibandingkan dengan response time pada query non-optimization.

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“…Penelitian lainnya dilakukan oleh Supriyadi et al (2018) yang meneliti mengenai pengklasifikasian daerah rawan bencana menggunakan metode clustering k-means dengan bantuan aplikasi RapidMiner [7]. Penelitian selanjutnya, yaitu penelitian oleh Yana et al (2018) yang bertujuan mengelompokkan wilayah Indonesia menurut frekuensi kejadian bencana alam tiap provinsi di Indonesia dengan mengambil data dari tahun 2013 sampai tahun 2018 [8].…”

Section: Pendahuluanunclassified

Pengelompokkan Data Bencana Alam Berdasarkan Wilayah, Waktu, Jumlah Korban dan Kerusakan Fasilitas Dengan Algoritma K-Means

Murdiaty¹,

Angela²,

Sylvia³

2020

mib

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Indonesia has fertile soil, natural resources and abundant marine resources. However, Indonesia is also not immune to the risk of natural disasters which are a series of events that disturb and threaten life safety and cause material and non-material losses. Indonesia's strategic geological location causes Indonesia to be frequently hit by earthquakes, volcanic eruptions and other natural disasters. From the data collected, natural disasters that occurred in Indonesia consisted of several categories, namely earthquakes, volcanic eruptions, floods, landslides, tornados, and tsunamis. Many natural disasters in Indonesia have caused casualties, both fatalities and injuries, destroying the surrounding area and destroying infrastructure and causing property losses. The trend of increasing incidence of natural disasters needs to be further investigated to prevent the number of victims from increasing. This information can be obtained through a data mining approach given the large amount of data available. In relation to natural disaster data, clustering techniques in data mining are very useful for grouping natural disaster data based on the same characteristics so that the data can be adopted as a groundwork for predicting natural disaster events in the future. Thus, this research is supposed to group natural disaster data using clustering techniques using the k-means algorithm into several groups, in terms of natural disaster types, time of disaster, number of victims, and damage to various facilities as a result of natural disasters

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Penerapan Metode K-Means dalam Pengelompokan Wilayah Menurut Intensitas Kejadian Bencana Alam di Indonesia Tahun 2013-2018

Cited by 15 publications

References 2 publications

A Hybrid K-Means Hierarchical Algorithm for Natural Disaster Mitigation Clustering

A Hybrid K-Means Hierarchical Algorithm for Natural Disaster Mitigation Clustering

Pengujian Optimization dan Non-Optimization Query Metode Topsis untuk Menentukan Tingkat Kerusakan Sektor Bencana Alam

Pengelompokkan Data Bencana Alam Berdasarkan Wilayah, Waktu, Jumlah Korban dan Kerusakan Fasilitas Dengan Algoritma K-Means

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