Analisis Penggunaan Metode Kernel Density Estimation pada Loss Distribution Approach untuk Risiko Operasional

Setiawan, Erwan; Murfi, Hendri; Satria, Yudi

doi:10.24198/jmi.v12i1.10248

Cited by 5 publications

(5 citation statements)

References 1 publication

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“…Spatial analysis of COVID-19 distribution aims to elucidate the level of the coronavirus transmission throughout the South Sulawesi region so that it can be used as a reference in making immediate decisions, such as whether or not a place institute a regional quarantine. The KDA approach was used because it has the advantage of transforming a collection of scattered points into a density index, in this case, that of COVID-19 susceptibility (Seaman and Powell, 1996;Setiawan et al, 2016).…”

Section: Discussionmentioning

confidence: 99%

Risk mapping and estimation of COVID-19 transmission in South Sulawesi, Indonesia by a self-identification survey

et al. 2022

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The rapid transmission rate of coronavirus disease 2019 (COVID-19) is multi-factorial but primarily due to population mobility and aggregation. This research aimed at estimating the rate based on risk mapping and investigation of geospatial distribution. It was divided into different phases that included data collection through a self-identification form available online; data validation of the data collected; application of spatial statistics; comparison with official numbers of positive COVID-19; and mapping of the results. The results show that self-identification based on procurement of independent personal data online had an accuracy of 89%.

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Section: Discussionmentioning

confidence: 99%

Risk mapping and estimation of COVID-19 transmission in South Sulawesi, Indonesia by a self-identification survey

et al. 2022

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“…Semakin dekat titik (x, y) dengan pusat titik api (x0, y0), semakin tinggi intensitas kebakaran. Penting untuk dicatat bahwa rumus ini hanya merupakan representasi matematis dari distribusi penyebaran titik api yang umum digunakan (Setiawan et al, 2016). Dalam praktiknya, terdapat banyak faktor-faktor lain yang mempengaruhi penyebaran nyata titik api, seperti arah dan kecepatan angin, kondisi cuaca, jenis vegetasi, dan sebagainya.…”

Section: Metodeunclassified

Analisis Spasial Titik Kebakaran Hutan dan Lahan di Kota Kupang dan Sekitarnya dengan Metode Kernel Density

2023

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Dampak kebakaran hutan dan lahan membuat aktivitas manusia terganggu dan memunculkan data titik api yang dapat diamati dengan penginderaan jauh dan pengembangan analisis spasial berupa heat map pada peta. Data yang digunakan pada penelitian adalah informasi tentang koordinat geografis titik api, intensitas, dan waktu terjadinya kejadian yang diambil dari website FIRMS (Fire Information for Resource Management System). Tujuan dari penelitian adalah melihat pola spasial dan korelasi spasial kebakaran hutan dan lahan di kota kupang dan kabupaten kupang pada tahun 2010 sampai tahun 2022. Pola spasial akan dianalisa menggunakan kernel density estimation berupa bentuk titik api berdasarkan evaluasi jarak dan kontribusi lokal referensi dalam radius tertentu dengan jumlah titik api tertinggi. Berdasarkan analisis kernel density estimation, analisis menggunakan pemanfaatan kernel density dapat melihat daerah dengan munculnya titik api terbanyak di suatu daerah. Hasil analisis menunjukkan bahwa daerah Rabeka adalah area dengan munculnya titik api terbanyak, sementara daerah di sekitar Amarasi Barat juga tercatat sebagai daerah dengan tingkat kejadian titik api yang signifikan. Informasi ini memberikan wawasan penting mengenai daerah-daerah yang rentan terhadap kebakaran hutan atau sering terjadi munculnya titik api.

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“…Settlements density using analysis Kernel Density is a statistical approach to estimate the probability distribution function (Setiawan et al, 2017). The form of data distribution is not used as a problem that needs to be considered (Handayani & Rudiarto, 2011).…”

Section: B Settlements Densitymentioning

confidence: 99%

Distribution Patterns and Settlement Density Using Nearest Neighbor Analysis and Kernel Density Analysis in Diy Coastal Areas

Yusliana

Fitria

Antus

et al. 2022

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DIY's coastal area is one of DIY's development priorities by establishing the site as an economic corridor that impacts physical development, such as settlements in the coastal area of DIY. This study aims to analyze the distribution and density of settlements. The method used in research is a quantitative descriptive and Geography Information System approach. The analysis used in this study is Nearest Neighbour Analysis (NNA) and Kernel Density Analysis with an analysis tool in ArcGIS 10.5. The results of the NNA show that the three districts located on the coast of DIY have the same distribution pattern characteristics, namely, the clustered pattern, which means the NNA index value is <1 or the T value ranges from 0-0.80. The results of the kernel density analysis show that the most significant density is found in Kulon Progo Regency, precisely in Wates District, and Bantul Regency, precisely in Srandakan District. At the same time, for Gunung Kidul Regency, it is less significant, and the density is only centered on one density point. Several factors cause settlement density, including physical factors (topography, slope, soil type, and clean water sources), accessibility (proximity to transportation routes and proximity to the city center), availability of facilities and infrastructure (electricity network, educational facilities, and health), and environmental factors (natural and human resources).Keywords: Settlements, Coastal, NNA, Kernel Density

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Analisis Penggunaan Metode Kernel Density Estimation pada Loss Distribution Approach untuk Risiko Operasional

Cited by 5 publications

References 1 publication

Risk mapping and estimation of COVID-19 transmission in South Sulawesi, Indonesia by a self-identification survey

Risk mapping and estimation of COVID-19 transmission in South Sulawesi, Indonesia by a self-identification survey

Analisis Spasial Titik Kebakaran Hutan dan Lahan di Kota Kupang dan Sekitarnya dengan Metode Kernel Density

Distribution Patterns and Settlement Density Using Nearest Neighbor Analysis and Kernel Density Analysis in Diy Coastal Areas

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