A Data Analysis of the Impact of Natural Disaster Using K-Means Clustering Algorithm

Prihandoko, Prihandoko; Bertalya, Bertalya

doi:10.28961/kursor.v8i4.109

Cited by 6 publications

(7 citation statements)

References 4 publications

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“…Nevertheless, the authors did not experiment with other clustering techniques on the same dataset for accuracy measures. Several previous studies above, such as Sadewo et al (2018), Supriyadi et al (2018), and Prihandoko and Bertalya (2016), did not validate the results of clustering on the mitigation and disaster grouping by province. In addition, the results have not yet been compared with mitigation/disaster grouping.…”

Section: Clustering Disaster-prone Areas and Mitigationmentioning

confidence: 78%

“…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. efforts using k-means in a disaster mitigation study.…”

Section: Clustering Disaster-prone Areas and Mitigationmentioning

confidence: 99%

“…Natural disaster mitigation is a continuous effort to reduce the impact of disasters against people and property (Sadewo et al, 2018). Prihandoko and Bertalya (2016) studied several factors for natural disasters in Indonesia and found that geographical condition was the main cause for natural disaster occurrence instead of weather condition. Anjayani (2008) suggested that earthquake hypocenters strongly correlated with the locations of many active volcanoes.…”

Section: Introductionmentioning

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: 78%

Section: Clustering Disaster-prone Areas and Mitigationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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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“…Penulis [8] menganalisis intensitas peristiwa banjir di sungai wujiang, cina selatan pada lebih 53 tahun dengan 5 indikator puncak debit, tingkat puncak, volume maksimum 24 jam, volume maksimum 72 jam dan total volume banjir, menggunakan algoritma fuzzy c-means menunjukkan bahwa tingkat banjir tinggi terjadi sejak pada tahun 1990-an di daerah aliran sungai. Pada penelitian [9] melakukan analisis clustering dampak bencana alam menggunakan algoritma k-means, hasil penelitian yang dilakukan menunjukkan bahwa kondisi cuaca bukanlah penyebab utama terjadinya bencana alam, namun kondisi geografis merupakan pemicu utama masalah bencana alam.…”

Section: Pendahuluanunclassified

Klasterisasi Menggunakan Agglomerative Hierarchical Clustering Untuk Memodelkan Wilayah Banjir

Pratikto

Damastuti

2021

JOINTECS

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Every year during the rainy season the problem of flooding in the province of East Java is a frequent disaster. Based on the records of the Disaster Management Agency (BNPB) from 2014 to 2015 there were 574 flood disasters in the province of East Java. Many factors cause floods, including the slow information obtained, so we need a modeling of potential flood areas in East Java using a more accurate and efficient method using an Agglomerative Hierarchical Clustering (AHC) method. This method will be used to group performance tests using the silhouette score method and cophenetic correlation coefficient. The results of this study are visualized in the form of GIS. Based on the optimal cluster results with the elbow method, the province of East Java is divided into 3 clusters of areas affected by flood potential i.e. low, medium, and high characteristics. Cluster performance results using cophenetic correlation coefficient indicate that the average linkage method provides a better cluster solution compared to other AHC methods which is 0.92.

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“…One of the most popular and widely studied grouping methods is kmeans algorithmically simple, relatively powerful, and gives "good enough" answers to various data sets. [12].…”

Section: K-medoids Clusteringmentioning

confidence: 99%

The Implementations of K-medoids Clustering for Higher Education Accreditation by Evaluation of Davies Bouldin Index Clustering

Ghufron

Surarso

Gernowo

2020

kursor

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The need for data analysis in tertiary education every semester is needed, this is due to the increasingly large and uncontrolled data, on the other hand generally higher education does not yet have a data warehouse and big data analysis to maintain data quality at tertiary institutions is not easy, especially to estimate the results of university accreditation high, because the data continues to grow and is not controlled, the purpose of this study is to apply k-medoids clustering by applying the calculation of the weighting matrix of higher education accreditation with the data of the last 3 years namely length of study, average GPA, student and lecturer ratio and the number of lecturers according to the study program, so that it can predict accurate cluster results, the results of this study indicate that k-medoid clustering produces good cluster data results with an evaluation value of the Bouldin index davies cluster index of 0.407029478 and is said to be a good cluster result.

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A Data Analysis of the Impact of Natural Disaster Using K-Means Clustering Algorithm

Abstract: Indonesia is one of the country with a lot of natural disasters

Cited by 6 publications

References 4 publications

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

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

Klasterisasi Menggunakan Agglomerative Hierarchical Clustering Untuk Memodelkan Wilayah Banjir

The Implementations of K-medoids Clustering for Higher Education Accreditation by Evaluation of Davies Bouldin Index Clustering

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