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

2020 ◽  
Vol 4 (3) ◽  
pp. 744
Author(s):  
Murdiaty Murdiaty ◽  
Angela Angela ◽  
Chatrine Sylvia
Keyword(s):  
Data Mining ◽  
Natural Disasters ◽  
Natural Disaster ◽  
Data Clustering ◽  
Volcanic Eruptions ◽  
Marine Resources ◽  
Clustering Techniques ◽  
Data Mining Approach ◽  
Disaster Data ◽  
Property Losses

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

Analisa Data Bencana Alam Untuk Prediksi Dampak Yang Ditimbulkan Dengan Algoritma J48 (Studi Kasus : Palang Merah Indonesia)

2019 ◽  
Vol 3 (1) ◽  
Author(s):  
Dewi Shintya Lumbansiantar
Keyword(s):  
Data Mining ◽  
Natural Disasters ◽  
Natural Disaster ◽  
Volcanic Eruptions ◽  
Red Cross ◽  
Natural Event ◽  
Disaster Data ◽  
Mining Methods ◽  
Using Data ◽  
The Impact

Natural disaster is a natural event that is difficult to avoid and difficult to estimate the exact impact of natural disasters that can be fatalities, social environment, propety, losses, even distrubance to the community even though it is very likely to occur. As for the disasters that often occur in Indonesia including floods, landslides, tsunamis, earthquakes and volcanic eruptions. The lack of relief supplies provided by the Indonesian Red Cross (PMI) was caused by the absence of data on the need for assistance provided. Therefore it is necessary to analyze natural disaster data that has happened before to be used to predict the impact caused by natural disasters. Prediction of the amount of assistance needed can be done using data mining techniques, therefore this study amis to analyzenatural disaster data using data mining methods using the J48 algorithm. To analyze natural disastr data for prediction of the impact can be used by rapidminer testing so that the results can be in the form of a decision tree.Keywords: Data Mining, Natural Disaster Data, J48 Algorithm

Purchaser's Optimistic Response to Social Media Advertisements—A Data Mining Approach

2019 ◽  
Vol 16 (2) ◽  
pp. 664-668
Author(s):  
S. Magesh ◽  
S. Vijayalakshmi
Keyword(s):  
Data Mining ◽  
Social Media ◽  
Clustering Techniques ◽  
Data Mining Approach ◽  
Online Social Media ◽  
Online Advertisements ◽  
Online Advertisement ◽  
Selection Of ◽  
Significant Factors

The paper aspires at discovering the most indispensable factors persuading customer reactions and purchasing commodities after observing online advertisements of social media and recognizing the distinctiveness of clusters of Purchaser having the optimistic reaction, over and above of buying customer clusters after analyzing online advertisement in social media. The selection of attribute and clustering techniques are incorporated in the analysis of data to find significant factors and target customer clusters correspondingly through data mining approach. It has been identifies that there is a strapping correlation between the advertisement being clicked on social media and the fulfillment with commodities, and amidst purchasing commodities online and saving information for supplementary deliberations. The findings also points out the characteristics of product and price Conscious clusters for Purchasers' reaction and procuring after seeing online social media advertisement.

Fatal Work Injuries Involving Natural Disasters, 1992–2006

2009 ◽  
Vol 3 (4) ◽  
pp. 201-209 ◽  
Author(s):  
Gregory M. Fayard
Keyword(s):  
Natural Disasters ◽  
Natural Disaster ◽  
Disaster Preparedness ◽  
Large Scale ◽  
Expert Knowledge ◽  
Occupational Injuries ◽  
Motor Vehicles ◽  
Disaster Data ◽  
The Government ◽  
Classification Structure

ABSTRACTObjective: Although a goal of disaster preparedness is to protect vulnerable populations from hazards, little research has explored the types of risks that workers face in their encounters with natural disasters. This study examines how workers are fatally injured in severe natural events.Methods: A classification structure was created that identified the physical component of the disaster that led to the death and the pursuit of the worker as it relates to the disaster. Data on natural disasters from the Census of Fatal Occupational Injuries for the years 1992 through 2006 were analyzed.Results: A total of 307 natural disaster deaths to workers were identified in 1992–2006. Most fatal occupational injuries were related to wildfires (80 fatalities), hurricanes (72 fatalities), and floods (62 fatalities). Compared with fatal occupational injuries in general, natural disaster fatalities involved more workers who were white and more workers who were working for the government. Most wildfire fatalities stemmed directly from exposure to fire and gases and occurred to those engaged in firefighting, whereas hurricane fatalities tended to occur more independently of disaster-produced hazards and to workers engaged in cleanup and reconstruction. Those deaths related to the 2005 hurricanes occurred a median of 36.5 days after landfall of the associated storm. Nearly half of the flood deaths occurred to passengers in motor vehicles. Other disasters included tornadoes (33 fatalities), landslides (17), avalanches (16), ice storms (14), and blizzards (9).Conclusions: Despite an increasing social emphasis on disaster preparation and response, there has been little increase in expert knowledge about how people actually perish in these large-scale events. Using a 2-way classification structure, this study identifies areas of emphasis in preventing occupational deaths from various natural disasters. (Disaster Med Public Health Preparedness. 2009;3:201–209)

Clustering Techniques and Their Applications: A Review

2020 ◽  
Vol 1 (4) ◽  
pp. 1-6
Author(s):  
Arjun Dutta
Keyword(s):  
Data Mining ◽  
Image Segmentation ◽  
Unsupervised Learning ◽  
Clustering Analysis ◽  
Data Clustering ◽  
Clustering Method ◽  
Clustering Techniques ◽  
Partitional Clustering ◽  
Gene Information ◽  
Scientific Fields

This paper deals with concise study on clustering: existing methods and developments made at various times. Clustering is defined as an unsupervised learning where the targets are sorted out on the foundation of some similarity inherent among them. In the recent times, we dispense with large masses of data including images, video, social text, DNA, gene information, etc. Data clustering analysis has come out as an efficient technique to accurately achieve the task of categorizing information into sensible groups. Clustering has a deep association with researches in several scientific fields. k-means algorithm was suggested in 1957. K-mean is the most popular partitional clustering method till date. In many commercial and non-commercial fields, clustering techniques are used. The applications of clustering in some areas like image segmentation, object and role recognition and data mining are highlighted. In this paper, we have presented a brief description of the surviving types of clustering approaches followed by a survey of the areas.

scholarly journals C.45 Algorithm for Classification of Causes of Landslides

SinkrOn ◽  
2019 ◽  
Vol 4 (1) ◽  
pp. 120 ◽  
Author(s):  
Yopi Handrianto ◽  
Muhammad Farhan
Keyword(s):  
Natural Disasters ◽  
Mass Movement ◽  
Volcanic Eruptions ◽  
Classification Model ◽  
Data Mining Approach ◽  
C4.5 Algorithm ◽  
Decision Tree Method ◽  
The Stability ◽  
Tree Method

Abstract— Natural disasters are disasters caused by natural events and cannot be avoided including earthquakes, tsunamis, volcanic eruptions, floods, hurricanes, droughts, and landslides. One of the natural disasters that often occurs in Indonesia is a landslide disaster. One of the regencies in West Java Province that had experienced a landslide was a Purwakarta district area. Landslide is one type of mass or rock mass movement, or a mixture of both, down or out of the slope due to the disruption of the stability of the soil or rocks making up the slope. With a data mining approach that uses the decision tree method or C4.5 Algorithm, a classification model will be made where the model functions as a classification of the causes of landslides in Purwakarta district.

scholarly journals Pentahelix synergity natural and non-natural disaster management in Pidie Jaya District Aceh Province to support national security

2021 ◽  
Vol 331 ◽  
pp. 02008
Author(s):  
Sugeng Yulianto ◽  
Fauzi Bahar ◽  
Sugimin Pranoto ◽  
Aam Amirudin
Keyword(s):  
National Security ◽  
Natural Disasters ◽  
Natural Disaster ◽  
Disaster Management ◽  
Volcanic Eruptions ◽  
Economic Life ◽  
Aceh Province ◽  
Prone Area ◽  
The Government ◽  
Natural Disaster Management

Geographically, Indonesia is located on disaster prone area. Natural disasters such as earthquakes, tsunamis, floods, landslides, volcanic eruptions and non-natural disaster such as Covid-19 Pandemic often occur in various places in Indonesia including in Pidie Jaya, Aceh Province. These disasters have a big influence on many aspects of the socio-economic life of the affected communities. Managing disaster properly will reduce the risk so that it will provide security and resilience community that can anticipate all the impacts of disasters. Collaboration in the form of the Pentahelix Synergy concept involving elements of the Government, Society, Academics, and the Business Industries is one of the important aspect in disaster management. Furthemore, the mass media and the private sector will provide great energy to solve the problem during disaster. This paper discuss about the synergy of Pentahelix in dealing with natural and non-natural disasters in Pidie Jaya Regency, Aceh Province. The results will be useful as lesson learned to support National Security program in Indonesia.

scholarly journals The Analysis of the Determination of the Funding Bpbd Perdaerah using the Method of Mfep in West Sumatra

KOMTEKINFO ◽  
2019 ◽  
Vol 6 (2) ◽  
pp. 198-203
Author(s):  
Nugraha Rahmansyah ◽  
Shary Armonitha Lusinia
Keyword(s):  
Natural Disasters ◽  
Natural Disaster ◽  
Central Government ◽  
Evaluation Process ◽  
Volcanic Eruptions ◽  
Existing Problems ◽  
West Sumatra ◽  
Information And Communication ◽  
Government Data ◽  
Natural Disaster Management

A natural disaster is a disaster caused by event or series of events caused by nature, such as earthquakes, tsunamis, volcanic eruptions, floods, droughts, hurricanes, and landslides. In this case the action of handling and natural disaster management is the responsibility of the central government and local government. Data budget or funding indicative SKPD BPBD West Sumatra that has been composed must be as effective as possible in its distribution. With the use of information and communication technology can help in penentuaan funding in each area. This study analyzes the natural disasters that occurred in each region in West Sumatra to determine funding in tackling natural disasters. In this case, day this provide a solution to existing problems by creating a decision support system methods of Multifactor Evaluation Process (MFEP).

scholarly journals An Efficient Density Based Clustering approach for High Dimensional Data

2018 ◽  
Vol 7 (2.32) ◽  
pp. 111
Author(s):  
Y Vijay Bhaskhar Reddy PP COMP.SCI.0560 ◽  
Dr L.S.S Reddy ◽  
Dr S.S.N. Reddy
Keyword(s):  
Data Mining ◽  
Data Clustering ◽  
Domain Knowledge ◽  
Pattern Mining ◽  
Data Extraction ◽  
Clustering Techniques ◽  
Density Based Clustering ◽  
Large Databases ◽  
Clustering Approach ◽  
Effective Analysis

Data extraction, data processing, pattern mining and clustering are the important features in data mining. The extraction of data and formation of interesting patterns from huge datasets can be used in prediction and decision making for further analysis. This improves, the need for efficient and effective analysis methods to make use of this data. Clustering is one important technique in data mining. In clustering a set of items are divided into several clusters where inter-cluster similarity is minimized and intra-cluster similarity is maximized. Clustering techniques are easy to identify of class in large databases. However, the application to large databases rises the following requirements for clustering techniques: minimal requirements of domain knowledge to determine the input specifications, invention of clusters with absolute shape & certainty of large databases.. The existing clustering techniques offer no solution to the combination of requirements. The proposed clustering technique DBSCAN using KNN relying on a density-based notion of clusters which is accomplished to discover clusters of arbitrary shape.  

scholarly journals Natural Disaster and Its Devastation

2015 ◽  
Vol 2 (4) ◽  
Author(s):  
Dr. Meghamala.S.Tavaragi ◽  
Mrs. Sushma. C ◽  
Dr. Srinivas Kosgi ◽  
Mrs. Mallika. B. N ◽  
Mrs. Gayatri Hegde ◽  
...  
Keyword(s):  
Natural Disasters ◽  
Natural Disaster ◽  
Volcanic Eruptions ◽  
Natural Phenomenon ◽  
Nepal Earthquake ◽  
Crisis Situation ◽  
Post Traumatic ◽  
Acts Of God

‘Disaster is a crisis situation that far exceeds the capabilities’. Disaster includes natural and man- made disasters. Natural disasters are brought about by change in natural phenomenon or what is known as acts of God. Manmade disasters are also known as anthropogenic disasters and they as a result of human intent, error or as a result of failed systems. Natural disasters include things such as floods, volcanic eruptions, earthquakes, floods, tornadoes, landslides and hurricanes. Manmade disasters are technological hazards, sociological hazards and transportation hazards etc .Earthquake being a type of natural disaster is being given specific importance in these journal due to recent devastating effects of earthquake in Nepal and neibouring countries like India. Along with incidence of Nepal earthquake, list of 10 most powerful earthquakes is mentioned to know the magnitude of devastation caused by earthquake. Like earthquake, be it any disaster, manmade or natural, almost everyone in the population is affected by it. It gives a brief account of psychiatric morbidities due to disasters in India. Those who suffer damage are called victims. The victims may die or live. Those who manage to live are called survivors. No particular event is guaranteed to result in post-traumatic psychopathology. It also mentions briefly of what disaster is and types of disasters are natural and manmade.

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