Global Trends in the Occurrence and Characteristics of Blocking Anticylones Using Sen Innovative Trend Analysis

Atmospheric blocking plays an important role in modulating mid-latitude weather, in particular in the Northern Hemisphere (NH). Trend analysis of atmospheric blocking for both hemispheres by using Şen’s Innovative Trend Analysis (ITA) is performed in this study. The blocking data archived in the University of Missouri covers the period of 1968–2019 for the NH and 1970–2019 for the Southern Hemisphere is used in the study. Block occurrence, duration and blocking intensity (BI) is analysed by classifying the NH (and SH) into three groups according to the preferred blocking locations: Atlantic, Pacific and Continental (Atlantic, Pacific and Indian). In the NH, blocking intensity showed mixed results. It showed a decreasing trend for the entire hemisphere and Atlantic Region, whilst a different trend was shown for different BI clusters. For blocking numbers and duration, the entire hemisphere and regions showed increasing trends. These increasing trend values were also statistically significant. In the SH, blocking intensity showed a decreasing trend for low clusters, whilst medium and high cluster increased for the entire hemisphere. Block duration showed an increasing trend for the entire SH. Block numbers showed increasing trends, except for one point in the low cluster. Blocking characteristics showed different trends for different preferred blocking locations. Increasing trends of blocking numbers for the overall SH and Pacific region are statistically significant at 95% level. Increasing trends of blocking duration for the overall SH, Atlantic and Pacific region are statistically significant at 90%, 95% and 95% level, respectively.

Klasterisasi Kasus Kekerasan Terhadap Anak dan Perempuan Berdasarkan Algoritma K-Means

Violence is action or threats against themselves alone, a group of people or community a group of people or community, loss psychologist, trauma, or deprivation of rights. District Karawang is on of the district that exist in the province of Jawa Barat. Violence that befell children and women in the area of Karawang bloom occurs, such as the lacj awareness of the victim to follow up cases that happened. The purpose of knowing the results of the cluster of cases of violence against children and women into three clusters are statterd in every sub-district in the District Karawang with category level of hardness low, medium or high in order that the government Karawang can provide treatment that is defferent and more targeted and focused on the results ot the analysis for each-each district. Data mining is the process of extracting data to obtain new information. In this study using CRIPS-DM methodology.Research is doing computation algorithm k-means clustering on the data of case of violence against children and women in 2016-2020. Results of testing using tools WEKA 3.8 earnded three cluster or the three categories of the level of violence that is cluster 0 there are 4 members who categorized the level of violence high, cluster 1 there are 2 members categorized the level of violence medium, and cluster 2 there are 24 members who categorized the level of violence low, the results of clustering is evaluated using equation testing purity measure, generate value purity 0,617, case that shows the cluster is quite good.

Implementation of the K-Medoids Algorithm for Data Clustering of Covid 19 Cases in West Java

The Covid 19 pandemic has hit Indonesia for almost 15 months since March 2020. The virus has spread to all provinces in Indonesia. Various efforts were made to be able to reduce or prevent the spread of the coronavirus, including the implementation of the PSBB in various areas including in West Java province. In this study, the objective of this research is to cluster the data on cases of Covid 19 in West Java which are recapitulated daily based on districts/cities that occurred on May 20, 2021. For the clustering process, the K-medoids algorithm is used which determines 3 clusters based on the variables used, namely discarded close contact, suspects discarded, probable completed, probable died, totally positive, positive recovered, and positive died. For data processing, a calculation analysis was carried out using the stages in the K-medoids algorithm and the Rapidminer application with high cluster mapping of 6 districts/cities, medium clusters there were 19 districts/cities, while low clusters had 2 districts/cities. The results of the analysis are expected to provide information about the distribution and mapping of clusters in West Java province.

Implementation of address recording management using the K-Means clustering classification algorithm in Kebayoran District, DKI Jakarta

The area is the center of problems in the administrative record management of Kebayoran District, because of its dense condition and it is difficult to determine land measurements due to the density of residential areas. The problem in Indonesia to this day is that the administrative boundaries of the kelurahan already exist, but the administrative boundaries for the Rukun Warga / Rukun Tetangga (RW / RT) do not yet exist. The local government of DKI already has a large scale map (1: 1,000) to map RW administrative boundaries. Large-scale mapping (Batas RW) is useful for accurate information on incidence of dengue fever or other diseases, thereby eliminating information bias due to the use of village boundary maps. Another benefit is the accuracy of address management for customers, for example PDAM customers, to facilitate verification of customer data with large-scale maps, especially those that only include RT / RW addresses, without mentioning street names and household numbers. The method used is data mining K-Means Clustering. By using this method, the data that has been obtained can be grouped into several clusters, where the application of the KMeans Clustering process uses Excel calculations. The processed data is divided into 3 clusters, namely: high cluster (C1), medium cluster (C2) and low cluster (C3). The iteration process of this research occurs 2 times so that an assessment is obtained in classifying the household / neighborhood unit based on the Kelurahan. The results obtained are that there is 1 neighborhood unit with the highest cluster (C1), there are 4 neighborhood units with 4 medium clusters (C2), and 5 neighborhood units with the lowest cluster (C3). This data can be input to the sub-district to disseminate information about dengue fever, health education, and for the accuracy of PDAM customer address management and others.

Women’s cancers in China: a spatio-temporal epidemiology analysis

Background Women's cancers, represented by breast and gynecologic cancers, are emerging as a significant threat to women's health, while previous studies paid little attention to the spatial distribution of women's cancers. This study aims to conduct a spatio-temporal epidemiology analysis on breast, cervical and ovarian cancers in China, thus visualizing and comparing their epidemiologic trends and spatio-temporal changing patterns. Methods Data on the incidence and mortality of women’s cancers between January 2010 and December 2015 were obtained from the National Cancer Registry Annual Report. Linear tests and bar charts were used to visualize and compare the epidemiologic trends. Two complementary spatial statistics (Moran’s I statistics and Kulldorff’s space–time scan statistics) were adopted to identify the spatial–temporal clusters. Results The results showed that the incidence and mortality of breast cancer displayed slow upward trends, while that of cervical cancer increase dramatically, and the mortality of ovarian cancer also showed a fast increasing trend. Significant differences were detected in incidence and mortality of breast, cervical and ovarian cancer across east, central and west China. The average incidence of breast cancer displayed a high-high cluster feature in part of north and east China, and the opposite traits occurred in southwest China. In the meantime, the average incidence and mortality of cervical cancer in central China revealed a high-high cluster feature, and that of ovarian cancer in northern China displayed a high-high cluster feature. Besides, the anomalous clusters were also detected based on the space–time scan statistics. Conclusion Regional differences were detected in the distribution of women’s cancers in China. An effective response requires a package of coordinated actions that vary across localities regarding the spatio-temporal epidemics and local conditions.

Implementasi Metode K-Means pada Hasil Produksi Daging Jenis Ternak

Meat production results should have good quantity and quality. To increase meat production, of course it is necessary to look at healthy types of livestock. Meat continues to increase in line with the increase in population, community income, education, standard of living and awareness of the nutritional value of animal production. The need for livestock meat production is one of the driving factors for the economy in Indonesia. This research can provide and input to the local government which is the leading producer of meat for the type of livestock in North Sumatra province and as a basis for making policies to increase meat production for other provinces. The method used in this research is the K-Means Algorithm. Where K-Means is one of the Algorithms in Data Mining that can be used to group data clusters. So that the data from 33 districts / cities will be divided into 2 clusters where cluster 1 is the high group, while cluster 2 is the low group. The results obtained from the study show that the results of manual calculation Algorithms and Microsoft Excel data have the same value, namely high cluster 1 and low cluster 32, and entering Microsoft Excel calculations into rapidminer has the same value as well

Implementasi Metode K-Medoids Clustering Dalam Pengelompokan Data Penyakit Alergi Pada Anak

Allergies are an abnormal response from the immune system. People who experience allergies have an immune system that reacts to a substance that is usually harmless in the environment. There are two limitations in this study, namely, seafood allergy and air allergy. In this study, the data used were sourced from the National Statistics Agency in 2011-2019. This study uses data mining techniques in data processing with the k-medoids clustering method. The k-medoids method is a clustering method that functions to split the dataset into several groups. The advantages of this method are able to overcome the weaknesses of the k-means method which is sensitive to outliers. Another advantage of this method is that the results of the clustering process do not depend on the order in which the dataset is entered. This method can be applied to data on the percentage of children affected by allergies by province, so that it can be seen the grouping of provinces based on this data. From this grouping data obtained 3 clusters namely low cluster (2 provinces), medium cluster (30 provinces) and high cluster (2 provinces) from the percentage of allergy immunization under five in each province. It is hoped that this research can provide information to the health department, especially the public health center regarding data grouping of Allergic Diseases in children in Indonesia which has an impact on equity in giving anti-allergic immunization to children in Indonesia

Using Ideal Time Horizon for Energy Cost Determination

In most optimal VM placement algorithms, the first step to determine the proper time horizon, T for the prediction of the expected maximum future load, L. However, T is dependent on the proper knowledge of the required time for servers to switch from their initial SLEEP/ACTIVE state to the next desired state. The activities implemented by this policy are (a) to relocate the VM from an encumbered server, a server that operates in an undesirably high regime with applications forecasted to rise their burdens to compute in the subsequent reallocation cycles; (b) to conduct VM migration from servers that operate within the undesirable regime to shift the server to a SLEEP mode; (c) putting an idle server to SLEEP mode and rebooting the servers from the SLEEP mode at high cluster loads. A novel mechanism for forwarding arriving client desires to the utmost suitable server is implemented; thus, in the complete system, balancing the requested load is possible.

Autokorelasi Spasial Kemiskinan dan Luas Lahan Pertanian di Kabupaten Mesuji

This research aims to examine the spatial analysis autocorrelation to determine the pattern of relationships or correlations between locations (observations). In the case of the percentage of poverty in Mesuji Regency and the influence of agricultural land area, this method will provide important information in analyzing the relationship between the characteristics of poverty between regions. Therefore, in this study, a spatial autocorrelation analysis was carried out on the percentage of population poverty data in 2017. The methods used were the Morans I test and the Local Indicator of Spatial Autocorrelation (LISA). The results of the spatial autocorrelation of poverty among 7 sub-districts in Mesuji Regency in 2017 are spatially clustered. Poverty grouping occurs where there are sub-districts that have almost the same observational value as sub-districts that are located close to each other or neighbors.There is one grouping based on the level of poverty, which consists of one high-high cluster, namely Panca Jaya District. low-low cluster group. While the high-low outliers and low-high-outliers categories were not found in the inter-district research area in Mesuji Regency. Variable Agricultural land area has a negative and significant effect on the percentage of poor people in Mesuji Regency in 7 Districts in a statistical model, increasing agricultural land will decrease the percentage of the poor.

Penerapan Metode K-Means Dalam Mengelompokkan Banyaknya Desa/ Kelurahan Menurut Keberadaan Permukiman Di Bantaran Sungai Berdasarkan Provinsi

The research aims to classify the settlements along the river banks by province. To solve this problem, the researchers applied the K-Means Algorithm method. Where the source of research data was collected based on documents explaining the number of villages / sub-districts according to the existence of settlements on the river banks produced by the Central Statistics Agency (BPS). The data used in the study are data from 2014 - 2018 which consists of 34 provinces. The data will be processed by clustering in 2 clusters, namely the settlement level cluster on the high riverbank and the settlement level cluster on the low riverbank. The high cluster consists of 11 data, namely the provinces of Aceh, North Sumatra, Jambi, South Sumatra, West Java, Central Java, East Java, West Kalimantan, Central Kalimantan, South Kalimantan, and South Sulawesi. By conducting the research, it can provide input and as a solution to related parties in charge of dealing with settlement problems along the river banks, especially for the government, in order to get more attention in provinces with high riverbank settlement rates.