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All Journal Jurnal Ekonomi Pembangunan Mobile and Forensics Science in Information Technology Letters Bulletin of Social Informatics Theory and Application Emerging Statistics and Data Science Journal
Tuti Purwaningsih, Tuti
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Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Electrical & Electronics Engineering Library & Information Science Mathematics Neuroscience Social Sciences

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Journal : Science in Information Technology Letters

 1 
Mapping dengue vulnerability: spatial cluster analysis reveals patterns in Central Java, Indonesia Fithriyyah, Anisahtul; Purwaningsih, Tuti; Konate, Siaka; Abdalla, Modawy Adam Ali
Science in Information Technology Letters Vol 4, No 2 (2023): November 2023
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/sitech.v4i2.1203

Abstract

In Indonesia, where the interplay between climate variability and infectious diseases is pronounced, Dengue Fever poses a significant threat, particularly in Central Java, ranking as the province with the third-highest incidence of Dengue cases nationwide. This study adopts a proactive approach, employing cluster analysis techniques—single linkage, average linkage, and Ward’s method—to categorize cities and regencies in Central Java based on their susceptibility to Dengue outbreaks. The comparative analysis, facilitated by standard deviation values, reveals nuanced vulnerability patterns, with the single linkage method presenting the most refined categorization, yielding four distinct vulnerability clusters: very low (0.097), low (0.150), medium (0.205), and high (0.303). Furthermore, spatial analysis utilizing Moran’s Index indicates a positive spatial autocorrelation among Dengue cases (Moran’s I = 0.62, p 0.05), underscoring the spatial homogeneity in case distribution across regions. These findings emphasize the critical need for targeted interventions and evidence-based policymaking to effectively combat Dengue transmission in Central Java and mitigate its public health impact.
Mapping crime determinants in Central Java: an in-depth exploration through local spatial association and regression analysis Humairoh, Nanda Lailatul; Purwaningsih, Tuti; Saifullah, Shoffan; Dwiyanto, Felix Andika; Rabbimov, Ilyos
Science in Information Technology Letters Vol 3, No 1 (2022): May 2022
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/sitech.v3i1.1212

Abstract

Economic development often brings prosperity to communities, but it can also be accompanied by growing disparities that, when unaddressed, lead to increased crime rates. Central Java, an Indonesian province, has been grappling with a persistent high crime rate, necessitating an in-depth examination of the factors underlying this phenomenon. In this study, we employ a rigorous research methodology, incorporating data sources from the Central Java Central Statistics Agency (BPS) and utilizing key independent variables, including population, unemployment, poverty, Age-Dependency Ratio (APS), and Relative Location Quotient (RLS). Through the application of advanced spatial analysis techniques such as the Local Indicator of Spatial Association (LISA) and the Spatial Autoregressive Model (SAR), this research offers a nuanced exploration of the spatial relationships and regression analysis of these variables. Notably, the study presents a tree map highlighting crime distribution in Central Java's districts and cities. The findings reveal that these five variables exhibit a 75.48% accuracy in predicting crime in Central Java. Through this comprehensive analysis, our research aims to provide valuable insights for policymakers, law enforcement, and the community at large, enabling informed strategies for crime reduction and the promotion of a safer, more prosperous Central Java
Gender inequality in HDI and per capita expenditure: A probabilistic distribution and spatial data analysis Fadilah, Zainal; Purwaningsih, Tuti; Inderanata, Rochmad Novian; Konate, Siaka; P, Cicin Hardiyanti
Science in Information Technology Letters Vol 3, No 2 (2022): November 2022
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/sitech.v3i2.1214

Abstract

Men and women have different habits or lifestyles, which inevitably leads to variances in other areas. As a result, gender statistics emerged. In this example, researchers seek to discover if there are discrepancies in HDI and per capita expenditure in Indonesia between men and women. To determine this, data from reliable sources is required; thus, researchers use data from the official BPS website, bps.go.id. The data comes from many tables, so the researcher will join them so that they may be studied. The data used in this scenario are HDI data by gender in 2020 and Per Capita Expenditure data by gender in 2020. Researchers employed graphical tools, such as boxplots and thematic charts, to examine whether there are differences in HDI and per capita expenditure between men and women in Indonesia. Aside from that, researchers used the two-sample t-test approach to see if there were variations in HDI and per capita expenditure between men and women. Researchers will utilize Python software to run this hypothesis test. According to the findings of the investigation, there is still gender imbalance in Indonesia in terms of HDI and per capita expenditure. As a result, it is intended that this research can be utilized as a reference in analyzing existing policies to ensure that there is no gender discrepancy in terms of HDI and per capita expenditure between men and women. It is also envisaged that this research would be beneficial to many people.
Factors Influencing open unemployment rates: a spatial regression analysis Purwaningsih, Tuti; Inderanata, Rochmad Novian; Pradana, Sendhyka Cakra; Snani, Aissa; Sulaiman, Sarina
Science in Information Technology Letters Vol 3, No 1 (2022): May 2022
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/sitech.v3i1.1202

Abstract

The present study employed spatial regression analysis as a methodological approach to get insights into the unemployment rates across Indonesian provinces in the year 2016. The official website of the Bureau of Labor Statistics (BPS) offers secondary data pertaining to several socio-economic indicators, including the Total Open Unemployment Rate, Economic Growth Rate, Human Development Index, Severity of Poverty Index, and School Participation Rates. The investigation employed the Geoda software package and encompassed Ordinary Least Squares (OLS) regression, Dependency/Correlation investigation, and Spatial Autoregressive Model. The data presented in the study revealed the existence of three distinct provincial groupings characterized by varying levels of unemployment rates. In the context of unemployment variance, the traditional regression model accounted for 30 percent of the observed variation. However, the spatial regression model used spatial dependencies to enhance accuracy in capturing the phenomenon. The aforementioned findings have the potential to assist policymakers in formulating strategies to address unemployment in regions characterized by distinct spatial attributes, hence offering a potential blueprint for other nations.
Co-Authors Abdalla, Modawy Adam Ali Ahmad Azhari As Shidiqi, Anis Fitriana Dwiyanto, Felix Andika Fadilah, Zainal Fithriyyah, Anisahtul Hardiyanti, Cicin Humairoh, Nanda Lailatul Inderanata, Rochmad Novian Konate, Siaka Kusumandari, Gita Evi Pradana, Sendhyka Cakra Purnaramadhan, Riza Rabbimov, Ilyos Saifullah, Shoffan Sarina Sulaiman Snani, Aissa Sumardi, Devina Gilar Fitri Ayu