scholarly journals Analisis Clustering K-Medoids Berdasarkan Indikator Kemiskinan di Jawa Timur Tahun 2020

2021 ◽  
Vol 22 (1) ◽  
pp. 1
Author(s):  
Febiyanti Alfiah ◽  
Almadayani Almadayani ◽  
Danial Al Farizi ◽  
Edy Widodo
Keyword(s):  
Clustering Analysis ◽  
The Other ◽  
Poor People ◽  
Clustering Method ◽  
Capita Expenditure ◽  
Poor Households ◽  
Per Capita ◽  
Poverty Indicators ◽  
Partition Method ◽  
Cluster 2

 Keberadaan pandemi COVID-19 di Indonesia, mengakibatkan kemiskinan di Indonesia semakin tinggi terutama di Jawa Timur yang menjadi satu diantara provinsi lain dengan kasus COVID-19 tinggi di Indonesia. Tujuan penelitian ini yaitu mengetahui pengelompokan kabupaten/kota di Jawa Timur yang mempunyai kesamaan karakteristik berdasarkan indikator kemiskinan tahun 2020. Penelitian ini menggunakan data yang didapatkan dari Badan Pusat Statistik. Metode yang digunakan ialah metode k-medoids clustering yang merupakan metode partisi clustering guna pengelompokan n objek ke dalam k cluster. Berdasarkan hasil penelitian, diperoleh pengelompokan karakteristik masing-masing cluster yang dibentuk berdasarkan nilai indikator kemiskinan di Jawa Timur tahun 2020 sebanyak 2 cluster. Dimana 30 kabupaten/kota pada cluster 1 dan dan 8 kabupaten/kota pada cluster 2. Cluster 1 memiliki karakteristik Persentase Rumah Tangga yang Mempunyai Sanitasi Layak, Angka Harapan Hidup, dan Persentase Angka Melek Huruf Umur 15-55 Th tinggi. Sedangkan cluster 2 memiliki karakteristik Persentase Rumah Tangga Miskin Penerima Raskin, Persentase Penduduk Miskin, dan Persentase Pengeluaran Perkapita untuk Makanan dengan Status Miskin tinggi. Kata kunci: Clustering; Jawa Timur; K-medoids; kemiskinan  K-Medoids Clustering Analysis Based on Poverty Indicators in East Java in 2020 ABSTRACT The existence of the pandemic COVID-19 in Indonesia has resulted in higher poverty in Indonesia, especially in East Java, which is one of the other provinces with high cases in Indonesia. The purpose of this study is to find out the grouping of regencies/cities in East Java that have similar characteristics based on the poverty indicators in 2020. This study uses data obtained from the Badan Pusat Statistik. The method used is k-medoids clustering method which is a clustering partition method for grouping n objects into k clusters. Based on the results of the study, it was found that the grouping of the characteristics of each cluster formed based on the value of the poverty indicator in East Java in 2020 was 2 clusters. Where 30 regencies/cities in cluster 1 and and 8 regencies/cities in cluster 2. Cluster 1 has the characteristics of the percentage of households that have proper sanitation, life expectancy, and a high percentage of literacy rates aged 15-55 years. While cluster 2 has the characteristics of the percentage of poor households receiving Raskin, the percentage of poor people, and the percentage of per capita expenditure on food with high poor status. Keywords: Clustering; East Java; K-Medoids; poverty

Shrinking Ecosystem Services in a Sinking Delta – Maintaining livelihoods in the Sundarbans Biosphere Reserve, India

2021 ◽  
Author(s):  
Oindrila Basu ◽  
Isha Das ◽  
Sudipa Pal ◽  
Tim Daw ◽  
Sugata Hazra
Keyword(s):  
Ecosystem Services ◽  
Fresh Water ◽  
Land Use Planning ◽  
Biosphere Reserve ◽  
Household Survey ◽  
Poor People ◽  
Shore Protection ◽  
Seed Collection ◽  
Poor Households ◽  
Per Capita

<p>A range of ecosystem services provide critical direct benefits to poor households living in the Sundarban Biosphere Reserve in India. These include artisanal fishing in creeks and rivers, crab collection, prawn seed collection, brackish and fresh-water aquaculture, fuel, fodder and honey collection from forests, and marine fishing in mechanized and non mechanized boats. The roles of these ecosystem services are largely invisible to official data. Triangulating between available statistics, key informant interviews and a new household survey, we estimate that nearly 30% of the 4.6 million population, mostly poor people rely on these ecosystem services. Ecosystem services supplement traditional rainfed agriculture, providing over 30% of household livelihood requirements. The availability of these ecosystem services is declining in per-capita terms due to the rapidly rising population in addition to ecosystem degradation. The area and health of mangrove is affected by sea level rise, differential subsidence, reduction of sediment and freshwater supply due to human obstruction and abstraction, increased salinity, high intensity cyclones, monsoon instability and temperature rise. Under a business as usual scenario, sharp decline of provisioning and regulating ecosystem services available per capita by 2030 is envisaged resulting in the threatening to increase poverty in the Biosphere Reserve. We review policy options to protect and enhance these critical ecosystem services for poor households including restoration of the estuarine mangrove habitat through river reconnection and rejuvenation and  fresh water provisioning and desalination, scientific plantation and shore protection using building with nature concept, regulating marine fishery and aquaculture practices , land use planning and population realignment.</p>

scholarly journals ANALYSIS OF EDUCATION LEVEL AND INCOME EFFECT TO THE NUMBER OF POOR INHABITANT IN INDONESIA

2020 ◽  
Vol 1 (1) ◽  
pp. 45-55
Author(s):  
Syamsul Arifin
Keyword(s):  
Time Series Data ◽  
Sampling Technique ◽  
Education Level ◽  
The Other ◽  
Series Data ◽  
Poor People ◽  
Collection Method ◽  
Level Of Education ◽  
Analysis Techniques ◽  
Per Capita

This research aims to analyze the level of education and income per capita of the number of poor people in Indonesia in 2004 - 2019 either partially or simultaneously. The population in this study was Indonesia in 2004 - 2019. The sampling technique is purposive sampling technique. The data collection method is documentation. Although the method of analysis using regression analysis techniques of time series data. The results of data analysis showed that the level of education is partially significant effect on the number of poor people in Indonesia. On the other hand revenues significantly influence the number of poor people in Indonesia. While simultaneously the levels of education and income influence significantly to the number of poor people in Indonesia.

POOR HOUSEHOLDS EXPENDITURE FOR PREVENTIVE AND CURATIVE HEALTH NEEDS

2020 ◽  
Vol 11 (1) ◽  
pp. 197-208
Author(s):  
Johannes Simatupang ◽  
Junaidi Junaidi
Keyword(s):  
Rural Areas ◽  
Local Governments ◽  
Health Expenditure ◽  
Health Expenditures ◽  
Multiple Regression Model ◽  
Health Needs ◽  
Capita Expenditure ◽  
Statistical Measures ◽  
Poor Households ◽  
Per Capita

The purpose of this research is to analyze: 1) patterns and allocation of household expenditure in poor urban and rural areas for preventive and curative health needs in Jambi Province; 2) socio-economic factors that affect their expenditure. Data is gath-ered thorough poor households at the locus of chosen village. To analyze the patterns and allocation of household expenditures, descriptive statistical measures as well as single and cross frequency tables is used. Furthermore, to analyze the factors influenc-ing, multiple regression model is used. The results found that: 1) the average health expenditure per capita per year of was IDR 67,391. It is 1.37 percent of the total annu-al expenditure per capita, or only 3.56 percent of the total per capita annual expendi-ture for non-food needs. Furthermore, detailed health expenditures for curative and preventive, it was found that 73.36 percent of health expenditures for poor households were for curative needs and only 26.64 percent were allocated for preventive health needs; 2) socioeconomic factors that significantly influence health expenditure are: family head age, head of the family education, field and business status, per capita expenditure, and structure of household members according to age, education and main activities. Therefore to improve health poor household service requires a massive campaign to encourage them to go to service center. This service is granted by local governments, though it still have difficulties to be implemented on health insurance scheme in Indonesia (BPJS).

scholarly journals Spatial Autoregressive Model and Spatial Patterns of Poverty In Lampung Province

2021 ◽  
Vol 16 (1) ◽  
Author(s):  
Ahmad Dhea Pratama ◽  
I Wayan Suparta ◽  
Ukhti Ciptawaty
Keyword(s):  
Spatial Analysis ◽  
Spatial Modeling ◽  
Autoregressive Model ◽  
Poor People ◽  
Spatial Autoregressive Model ◽  
Capita Expenditure ◽  
Negative Effect ◽  
Spatial Autoregressive ◽  
Per Capita ◽  
Positive Effect

Many research in economics only focus on the independence of a region while neglecting the effects of space and the interaction that occurs between mutually adjacent areas. The purpose of this study is to measure the multidimensional poverty concept in 15 districts/cities in the province of Lampung in 2015-2019. Spatial analysis such as moran i statistics, LISA clustered map, and lisa signification are used to analyze spatial patterns and spatial autocorrelation. Spatial modeling with spatial autoregressive model, geoda and geographical information systems are used as explanatory spatial data and spatial modeling. The results show that the percentage of poor people between districts/cities in Lampung Province have positive Moran's I values, there is a clustered pattern in 2015-2019, Moran scatter plot depicts 4 quadrants, LISA Cluster map indicates high-high and low-low areas, and LISA map has 4 significant areas. Spatial regression results show that per capita expenditure for nonfood has a negative effect, per capita expenditure for food has a positive effect, population growth rate has a positive effect, household clean water has a positive effect, life expectancy has a negative effect, mean years of schooling has a negative effect, and simultaneously the independent variables have a significant influence on the percentage of poor people. Poverty in Lampung Province is spatially related to each other between regions, the findings suggest that the variables used affect spatially. The implication of this result is one of the basis for inter-regional policies in the interests of multi-dimensional poverty alleviation between regions.Keywords: Poverty, Spatial analysis, Spatial Autoregressive Model (SAR)

scholarly journals Analisis Regresi Kuantil B-Splines Monoton Naik pada Hubungan Rata-Rata Lama Sekolah dan Pengeluaran Rumah Tangga Per Kapita di Provinsi Yogyakarta

2018 ◽  
Vol 4 (2) ◽  
pp. 128-136
Author(s):  
Yogo Aryo Jatmiko
Keyword(s):  
Household Expenditure ◽  
Poor Household ◽  
Poverty Level ◽  
Level Of Education ◽  
B Splines ◽  
Capita Expenditure ◽  
Poor Households ◽  
Per Capita ◽  
The Relationship ◽  
Years Of Schooling

The multidimensional problem in various countries that is always become the government's attention is the problem of poverty, Indonesia is no exception. Poverty is often associated with the education sector due to the function of education as a driving force of the transformation of society to break the chain of poverty. The pattern of relations between poverty and the education sector can be seen from the relationship between the level of education (mean years of schooling) and poverty level (per capita household expenditure). DI Yogyakarta is still the province with the largest percentage of poverty on the Java island despite showing a downward trend since 2007. This study aims to look at the relationship between the level of education (mean years of schooling) and poverty level (per capita household expenditure) in DI Yogyakarta Province 2016. The model that is suitable for determining household characteristics is quantile regression with the Increased monotone B-Splines method that links the mean years of schooling and per capita household expenditure. Estimation results based on the quantile regression model with Increased monotone B-Splines method found that households with the lowest education level are said to be very poor households if monthly per capita expenditure is less than 322,205 rupiah and is said to be a poor household if monthly per capita expenditure is between 322,205 rupiah to 426,666 rupiah. Meanwhile, households with the highest level of education are said to be very poor households if monthly per capita expenditure is less than 3,410,965 rupiahs and is said to be a poor household if monthly per capita expenditure is between 3,410,965 rupiahs up to 4,676,718 rupiahs

scholarly journals Penerapan Multivariate Adaptive Regression Spline (MARS) Pada Kasus Indeks Pembangunan Manusia di Kawasan Timur Indonesia

2019 ◽  
Vol 16 (2) ◽  
Author(s):  
Annisa Nur Insany ◽  
Nur Eni ◽  
Mohammad Fajri
Keyword(s):  
Human Life ◽  
Severity Index ◽  
Multivariate Adaptive Regression Spline ◽  
Poor People ◽  
Important Indicator ◽  
Regression Spline ◽  
Capita Expenditure ◽  
Mars Model ◽  
Adaptive Regression ◽  
Per Capita

Human Development Index (HDI) is an important indicator to measure success in effort to build the quality of human life. Multivariate Adaptive Regression Spline (MARS) is a regression approach developed from the Recursive Partitioning Regression (RPR) method and combined with the spline method to produce a model that is continuous on knots. MARS modeling is determined based on trial and error for a combination of basis function (BF), maximum interaction (MI), and minimum observation (MO) to get the value of minimum smoothing parameter. Automatic determination of knots on MARS uses a forward stepwise and backward stepwise algorithm based on minimum Generalized Cross Validation (GCV) values. The results of this study, obtained a combination value of BF = 52, MI = 3, and MO = 2 with a minimum GCV of 0,00049 then the best MARS model is as follows.Y = –1,045 + 0,446*BF1 – 0,592*BF2 + 0,234*BF3 – 0,352*BF4 + 0,218*BF5 – 0,254*BF6 + 0,338*BF7 – 0,304*BF8 – 3,974*BF9 + 0,097*BF13 – 1,267*BF15 – 41,784*BF16 – 1,845*BF18 – 0,519*BF19 – 0,046*BF21 + 1,275*BF23 – 0,006*BF24 – 0,110*BF25 + 0,021*BF26 + 0,153*BF27 + 14,607*BF28 – 9,276*BF30 – 5,335*BF32 – 0,053*BF34 – 0,219*BF35 – 0,111*BF36 + 0,033*BF37 – 0,238*BF38 + 0,037*BF39 – 0,017*BF41 – 0,039*BF42 – 0,632*BF43 + 0,096*BF45 – 0,147*BF46 + 0,070*BF48 + 0,339*BF49 + 0,017*BF50. The factors that influence HDI based on the level of importance in the best MARS model are per capita expenditure (X9), life expectancy (X7), long school expectations (X8), average school length (X6), poverty severity index (X4), percentage of poor people (X5), percentage of per capita expenditure for food (X12), population density per km2 (X1), and percentage of poor people aged 15 years and over do not work (X11).

scholarly journals ANALYSIS OF EDUCATION LEVEL AND INCOME EFFECT TO THE NUMBER OF POOR INHABITANT IN INDONESIA

2020 ◽  
Vol 1 (1) ◽  
pp. 45-55
Author(s):  
Syamsul Arifin
Keyword(s):  
Time Series Data ◽  
Sampling Technique ◽  
Education Level ◽  
The Other ◽  
Series Data ◽  
Poor People ◽  
Collection Method ◽  
Level Of Education ◽  
Analysis Techniques ◽  
Per Capita

This research aims to analyze the level of education and income per capita of the number of poor people in Indonesia in 2004 - 2019 either partially or simultaneously. The population in this study was Indonesia in 2004 - 2019. The sampling technique is purposive sampling technique. The data collection method is documentation. Although the method of analysis using regression analysis techniques of time series data. The results of data analysis showed that the level of education is partially significant effect on the number of poor people in Indonesia. On the other hand revenues significantly influence the number of poor people in Indonesia. While simultaneously the levels of education and income influence significantly to the number of poor people in Indonesia.

Economic Performance and Public Finance in Kenya, 1960-2010

2018 ◽  
Vol 47 (2) ◽  
Author(s):  
Kempe Ronald Hope
Keyword(s):  
Public Finance ◽  
Economic Performance ◽  
Emerging Economy ◽  
The Other ◽  
Government Investment ◽  
The Past ◽  
Other Hand ◽  
Private Savings ◽  
Per Capita ◽  
Real Growth

Countries with positive per capita real growth are characterised by positive national savings—including government savings, increases in government investment, and strong increases in private savings and investment. On the other hand, countries with negative per capita real growth tend to be characterised by declines in savings and investment. During the past several decades, Kenya’s emerging economy has undergone many changes and economic performance has been epitomised by periods of stability, decline, or unevenness. This article discusses and analyses the record of economic performance and public finance in Kenya during the period 1960‒2010, as well as policies and other factors that have influenced that record in this emerging economy. 

scholarly journals OP0038 SPLICEOSOME ALTERATIONS IN LEUCOCYTES FROM APS, SLE AND SLE+APS PATIENTS ARE CLOSELY RELATED TO THEIR MAIN CLINICAL FEATURES

2021 ◽  
Vol 80 (Suppl 1) ◽  
pp. 20.2-20
Author(s):  
A. M. Patiño-Trives ◽  
C. Perez-Sanchez ◽  
A. Ibañez-Costa ◽  
P. S. Laura ◽  
M. Luque-Tévar ◽  
...  
Keyword(s):  
Autoimmune Diseases ◽  
Clinical Features ◽  
Antiphospholipid Syndrome ◽  
Immune Cells ◽  
Inflammatory Mediators ◽  
Clustering Analysis ◽  
Renal Involvement ◽  
Molecular Clustering ◽  
Cluster 2 ◽  
Splicing Machinery

Background:To date, although multiple molecular approaches have illustrated the various aspects of Primary Antiphospholipid Syndrome (APS), systemic lupus erythematosus (SLE) and antiphospholipid syndrome plus lupus (APS plus SLE), no study has so far fully characterized the potential role of posttranscriptional regulatory mechanisms such as the alternative splicing.Objectives:To identify shared and differential changes in the splicing machinery of immune cells from APS, SLE and APS plus SLE patients, and their involvement in the activity and clinical profile of these autoimmune disorders.Methods:Monocytes, lymphocytes and neutrophils from 80 patients (22 APS, 35 SLE and 23 APS plus SLE) and 50 healthy donors (HD) were purified by immunomagnetic selection. Then, selected elements of the splicing machinery were evaluated using a microfluidic qPCR array (Fluidigm). In parallel, extensive clinical/serological evaluation was performed, comprising disease activity, thrombosis and renal involvement, along with autoantibodies, acute phase reactants, complement and inflammatory molecules. Molecular clustering analyses and correlation/association studies were developed.Results:Patients with primary APS, SLE and APS plus SLE displayed significant and specific alterations in the splicing machinery components in comparison with HD, that were further specific for each leukocyte subset. Besides, these alterations were associated with distinctive clinical features.Hence, in APS, clustering analysis allowed to identify two sets of patients representing different molecular profile groups with respect to the expression levels of splicing machinery components. Principal component analyses confirmed a clear separation between patients. Clinically, cluster 1 characterized patients with higher thrombotic episodes and recurrences than cluster 2 and displayed a higher adjusted global APS score (aGAPSS). Accordingly, these patients showed higher levels of inflammatory mediators than cluster 2.Similarly, in patients with APS plus SLE, clustering analysis allowed to identify two sets of patients showing differential expression of splicing machinery components. Clinical and laboratory profiles showed that cluster 2 characterized patients that had suffered more thrombotic recurrences, most of them displaying an aGAPSS over 12 points and expressing higher levels of inflammatory mediators than cluster 1. The incidence of lupus nephropathy was similarly represented in both clusters.Lastly, in SLE patients, molecular clustering analysis identified two sets of patients showing distinctive clinical features. One cluster characterized most of the patients positive for anti-dsDNA antibodies, further suffering lupus nephropathy, and a high proportion of them also presenting atheroma plaques and high levels of inflammatory mediators.Correlation studies further demonstrated that several deranged splicing machinery components in immune cells (i.e. SF3B1tv1, PTBP1, PRP8 and RBM17) were linked to the autoimmune profile of the three autoimmune diseases, albeit in a specific way on each disorder. Accordingly, in vitro treatment of HD lymphocytes with aPL-IgG or anti-dsDNA-IgG changed the expression of spliceosome components also found altered in vivo in the three autoimmune diseases. Finally, the induced over/downregulated expression of selected spliceosome components in leukocytes modulated the expression of inflammatory cytokines, changed the procoagulant/adhesion activities of monocytes and regulated NETosis in neutrophils.Conclusion:1) The splicing machinery, profoundly altered in leukocytes from APS, APS plus SLE and SLE patients, is closely related to the activity of these diseases, their autoimmune and inflammatory profiles. 2) The analysis of the splicing machinery allows the segregation of APS, APS plus SLE and SLE, with specific components explaining the CV risk and renal involvement in these highly related autoimmune disorders.Acknowledgements:Funded by ISCIII, PI18/00837 and RIER RD16/0012/0015 co-funded with FEDERDisclosure of Interests:None declared

scholarly journals Pattern of inflammatory immune response determines the clinical course and outcome of COVID-19: unbiased clustering analysis

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Eunyoung Emily Lee ◽  
Kyoung-Ho Song ◽  
Woochang Hwang ◽  
Sin Young Ham ◽  
Hyeonju Jeong ◽  
...  
Keyword(s):  
Immune Response ◽  
Immune Responses ◽  
Clustering Analysis ◽  
Validation Cohort ◽  
Inflammatory Responses ◽  
Clinical Course ◽  
Severe Disease ◽  
Outcome Data ◽  
Inflammatory Immune Response ◽  
Cluster 2

AbstractThe objective of the study was to identify distinct patterns in inflammatory immune responses of COVID-19 patients and to investigate their association with clinical course and outcome. Data from hospitalized COVID-19 patients were retrieved from electronic medical record. Supervised k-means clustering of serial C-reactive protein levels (CRP), absolute neutrophil counts (ANC), and absolute lymphocyte counts (ALC) was used to assign immune responses to one of three groups. Then, relationships between patterns of inflammatory responses and clinical course and outcome of COVID-19 were assessed in a discovery and validation cohort. Unbiased clustering analysis grouped 105 patients of a discovery cohort into three distinct clusters. Cluster 1 (hyper-inflammatory immune response) was characterized by high CRP levels, high ANC, and low ALC, whereas Cluster 3 (hypo-inflammatory immune response) was associated with low CRP levels and normal ANC and ALC. Cluster 2 showed an intermediate pattern. All patients in Cluster 1 required oxygen support whilst 61% patients in Cluster 2 and no patient in Cluster 3 required supplementary oxygen. Two (13.3%) patients in Cluster 1 died, whereas no patient in Clusters 2 and 3 died. The results were confirmed in an independent validation cohort of 116 patients. We identified three different patterns of inflammatory immune response to COVID-19. Hyper-inflammatory immune responses with elevated CRP, neutrophilia, and lymphopenia are associated with a severe disease and a worse outcome. Therefore, targeting the hyper-inflammatory response might improve the clinical outcome of COVID-19.

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