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

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

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

JURNAL MEDIA INFORMATIKA BUDIDARMA ◽

10.30865/mib.v4i3.2213 ◽

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

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Do Long-Term Natural Disasters Influence Social Trust? Empirical Evidence from China

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph18147280 ◽

2021 ◽

Vol 18 (14) ◽

pp. 7280

Author(s):

Yao Li ◽

Haoyang Li ◽

Jianqing Ruan

Keyword(s):

Natural Disasters ◽

Natural Disaster ◽

Social Trust ◽

Geographical Location ◽

Household Survey ◽

Flood Frequency ◽

Psychological Characteristics ◽

The North ◽

The Impact

The natural environment is one of the most critical factors that profoundly influences human races. Natural disasters may have enormous effects on individual psychological characteristics. Using China’s long-term historical natural disaster dataset from 1470 to 2000 and data from a household survey in 2012, we explore whether long-term natural disasters affect social trust. We find that there is a statistically significant positive relationship between long-term natural disaster frequency and social trust. We further examine the impact of long-term natural disaster frequency on social trust in specific groups of people. Social trust in neighbors and doctors is stronger where long-term natural disasters are more frequent. Our results are robust after we considering the geographical difference. The effect of long-term natural disasters remains positively significant after we divide the samples based on geographical location. Interestingly, the impact of long-term flood frequency is only significant in the South and the impact of long-term drought frequency is only significant in the North.

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Using Data Mining Methods to Detect Medical Fraud

Proceedings of the 2020 International Conference on Management of e-Commerce and e-Government ◽

10.1145/3409891.3409902 ◽

2020 ◽

Author(s):

Long-Sheng Chen ◽

Jia-Chuan Chen

Keyword(s):

Data Mining ◽

Mining Methods ◽

Using Data

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Data Mining zur Nacharbeitsdauerprognose*/Data Mining-based forecasting of rework duration - Predictive rework control and work process optimization in the automotive assembly

wt Werkstattstechnik online ◽

10.37544/1436-4980-2017-10-95 ◽

2017 ◽

Vol 107 (10) ◽

pp. 773-778

Author(s):

S. Krzoska ◽

M. Eickelmann ◽

J. Schmitt ◽

J. Prof. Deuse

Keyword(s):

Data Mining ◽

Process Optimization ◽

Quality Data ◽

Operating Process ◽

Automotive Assembly ◽

Related Quality ◽

Mining Methods ◽

Product And Process ◽

Using Data ◽

Manufacturing Execution

Der Fachbeitrag zeigt am Beispiel der Nacharbeitssteuerung und Arbeitsprozessoptimierung in der Automobilmontage, wie produkt- und prozessbezogene Qualitätsdaten durch den Einsatz von Data Mining-Methoden analysiert sowie effizient genutzt werden können. Dazu wurden Daten aus Manufacturing-Execution-Systemen (MES) mithilfe von Regressionsbäumen zur Entwicklung einer fahrzeugspezifischen Nacharbeitsdauerprognose ausgewertet. Das grundlegende Data Mining-Konzept sowie die Pilotierungsergebnisse werden nachfolgend dargestellt.   The article shows at the example of rework control and operating process optimization in the car assembly how recorded product- and process-related quality data can be analyzed and used efficiently by using Data Mining-methods. With data from MES-systems regression trees were built for a vehicle-specific rework duration forecast. The basic concept and validation results will be presented below.

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