Algoritma K-Means dan Analisis Komponen Utama untuk Mengatasi Multikolinearitas pada Pengelompokan Kabupaten Tertinggal

Introduction: This study aims to quantify the inequalities in melanoma incidence, mortality, and the mortality-to-incidence ratio (MIR) across countries with varying levels of the Human Development Index (HDI) and other socioeconomic indicators. Methods: We conducted an ecological study using data on age-standardized melanoma incidence and mortality rates from GLOBOCAN 2022, along with socioeconomic indicators (including the Human Development Index, years of education, average years of schooling, life expectancy, and Gross National Income) sourced from the World Bank for 165 countries. We calculated the mortality-to-incidence ratio and categorized the socioeconomic indicators into four groups. Differences between melanoma incidence and socioeconomic indicators were evaluated using ANOVA tests, and correlations were calculated using the Spearman method. Results: Our analysis revealed significant differences in incidence and mortality rates based on socioeconomic indicators (ANOVA p-value < 0.001 for all indicators). The Spearman correlation demonstrated a direct relationship between incidence and various indicators: Mean Years of Schooling (MYS) (0.615), HDI (0.595), Expected Years of Schooling (EYS) (0.577), Gross National Income (GNI) (0.532), and Life Expectancy at Birth (LEB) (0.5). Similarly, mortality rates also showed direct correlations with these indicators: MYS (0.548), EYS (0.526), HDI (0.501), GNI (0.422), and LEB (0.399). Conversely, we observed an inverse relationship between the mortality-to-incidence ratio (MIR) and socioeconomic indicators: GNI (-0.624), HDI (-0.612), LEB (-0.553), EYS (-0.542), and MYS (-0.521). There were significant differences in the MIR when analyzed by groups based on socioeconomic indicators (ANOVA p-value < 0.001 for all indicators). Conclusion: This study establishes a direct correlation between melanoma incidence and mortality rates with the Human Development Index (HDI), highlighting significant disparities. Additionally, we found an inversely proportional relationship between the melanoma mortality-to-incidence ratio and HDI. This indicates that higher levels of development are associated with lower mortality rates relative to incidence. These findings underscore the need to address inequalities in melanoma outcomes on a global scale. Citation Format: Joseph A. Pinto, Roberto Paz-Manrique, Henry L. Gomez. Global disparities in melanoma incidence, mortality and mortality-to-incidence ratio: An analytical framework utilizing the human development index [abstract]. In: Proceedings of the 18th AACR Conference on the Science of Cancer Health Disparities; 2025 Sep 18-21; Baltimore, MD. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2025;34(9 Suppl):Abstract nr C026.

The Human Development Index (HDI) serves as an indicator for assessing socio-economic development in a region. Each area strives to improve its HDI by considering the factors that influence it in that specific region. This research aims to identify the direct and indirect influences of economic and social factors, such as Life Expectancy (LE), Gross Regional Domestic Product per capita (GRDPpc), Labor Force Participation Rate (LFPR) through Average Years of Schooling (AYS) on the HDI in South Sulawesi in 2022. The data used in this study are secondary data obtained from the Central Statistics Agency (BPS) of South Sulawesi Province in 2022. The method applied in this research is a path analysis that examines the relationships between variables, both direct and indirect influences. The research results show that in the equation of sub-structure 1, LE and GRDP per capita ADHB have a direct influence on AYS, while LFPR does not have a direct impact on AYS. The magnitude of the influence of variables in sub-structure 1 is 53%. In the equation of sub-structure 2, LE, GRDP per capita ADHB, LFPR, and AYS have a significant direct impact on HDI. Additionally, LE and GRDP per capita ADHB have an indirect influence through AYS on HDI. The magnitude of the influence of variables in sub-structure 2 is 93.5%. Therefore, the variables that have both direct and indirect effects on HDI through AYS are LE and GRDP per capita ADHB.

The Human Development Index (HDI) is an important indicator for measuring the success of efforts to improve the quality of human life and serves as one of the indicators of development performance in a region. The objectives are (1) to provide input to the Bojonegoro Regency Government on the extent of achievements in the quality of life of its people and (2) to evaluate human development through efforts to improve the achievement of each indicator, including Life Expectancy, Expected Years of Schooling, Average Years of Schooling and per capita Purchasing Power of the people of Bojonegoro Regency. This research employs a mixed-method approach, combining both qualitative and quantitative research. Successful human development is characterized by providing opportunities for people to enjoy a long and healthy life and lead a productive life. The purchasing power of the community positively influences the increase in Clean and Healthy Living Behavior (PHBS), which in turn positively impacts the increase in Life Expectancy. Improvements in education can raise public awareness of the importance of Clean and Healthy Living Behavior. Thus, PHBS itself can mediate and strengthen purchasing power and average years of schooling, thereby influencing the increase in Life Expectancy. Conclusions (1) The HDI of Bojonegoro Regency from 2020 to 2023 experienced an average increase of 0.54 points. In 2023, the HDI was 71.80, with the achievement of each indicator as follows Life Expectancy was 74.72 Expected Years of Schooling was 12.92 Average Years of Schooling was 7.45 and per capita Purchasing Power was Rp. 10,776,000. (2) The projected HDI for 2024 is 72.53, an increase of 0.73 points, with the HDI indicators being Life Expectancy at 75.01, Expected Years of Schooling at 13.04, Average Years of Schooling at 7.51, and per capita Purchasing Power at Rp. 11,263,000.

Human development is needed to create prosperity and assist development in a country. In realising this, it is necessary to first look at the quality of human resources in the country, so that its use is more targeted. The measure used as a standard for the success of human development in a country is the Human Development Index (HDI). HDI figure are calculated from the aggregation of three dimensions, namely longevity and healthy living, knowledge, and decent standard of living. The longevity and healthy living dimension is represented by the Life Expectancy. Average Years of Schooling (AYS) and Expected Years of Schooling (EYS) are indicators representing the knowledge dimension. Meanwhile, the decent standard of living dimension is represented by the Expenditure per Capita indicator. The purpose of this study is to explain the characteristics of each cluster obtained from Hierarchical Cluster Analysis of districts/cities in North Sumatra Province based on HDI indicators in 2022 using Pseudo-F. The methods used are Hierarchical Cluster Analysis and Calinski-Harabasz Pseudo-F Statistic. The main concept of this method is to determine the optimum number of groups. This research uses secondary data obtained from BPS. The sample size in this study are 33 districts/cities and the number of variables are 4 variables. The results of the analysis of this study are the formation of 4 clusters with the best method is Ward. Cluster 1 consists of four members, namely Medan City, Pematang Siantar City, Binjai City, and Padang Sidempuan City, where this cluster has a very high HDI level. Meanwhile, Cluster 4 is a cluster that has a very low HDI level with four cluster members, namely Nias District, South Nias District, North Nias District, and West Nias District. Thus, it can be seen that there is a gap between regions in North Sumatra Province.

Indonesia faces a critical challenge in balancing economic growth with environmental sustainability and improving people’s welfare. As one of the countries with the largest carbon dioxide emissions in the world, increasing carbon emissions have negatively impacted environmental quality, health and life expectancy. This study aims to analyse the relationship between carbon dioxide emissions, gross domestic product and average years of schooling on life expectancy in Indonesia, with human development index (HDI) as a mediating variable. Using 12 years of data sourced from the Central Bureau of Statistics and the World Bank, this study applies a quantitative approach with Sobel test analysis to identify the direct and indirect effects of these variables through HDI. The results of the analysis show that carbon dioxide emissions have a significant negative impact on HDI, which in turn reduces life expectancy. In contrast, GDP and average years of schooling have a positive influence on HDI and life expectancy. However, the disparity in the distribution of GDP benefits and access to education between regions remains a major obstacle in improving human development equitably. This study confirms that HDI acts as an important mediator that strengthens the relationship between economic, social and environmental variables and life expectancy. The findings have significant policy implications, including the reduction of carbon emissions through sustainable strategies, increased investment in the education sector, and a more equitable redistribution of economic benefits. These evidence-based recommendations support the achievement of the sustainable development goals (SDGs) in Indonesia and make an important contribution to the development of scientific literature related to human development.

The Human Development Index (HDI) is an important indicator in assessing the success of development in a region, encompassing aspects of health, education, and economic welfare. This study aims to analyze the factors that influence HDI in West Java Province in 2023 using a principal component approach, specifically Principal Component Analysis (PCA) and factor analysis with five factor components: labor force participation rate, life expectancy, open unemployment rate, poverty, and average years of schooling. West Java was chosen as the focus of this research because, despite showing a significant increase in HDI reaching 73.74% in 2023, it is still below that of DKI Jakarta, which has an HDI of 83.55%. The results show that the HDI in West Java has high data variability, is multivariately normally distributed, and the dependent data is sufficient for factor analysis. The formed components are two factors with a total variance of 82.71% across two factors, which can be explained by three factors: labor force participation rate, life expectancy, and poverty percentage. Two factors were identified: the first factor represents population conditions, including life expectancy, poverty percentage, and average years of schooling, while the second factor represents labor conditions, including labor force participation rate and open unemployment rate. However, the two formed factors were unable to fully capture all the factors influencing the Human Development Index in West Java in 2023. This study is expected to serve as a reference for local and central governments in formulating more equitable development policies, aiming to promote sustainable HDI improvement in West Java and other regions in Indonesia.

The Human Development Index is one approach to measuring the success rate of human development. Central Java Province is one of the provinces that experienced an increase in the human development index in 2022. Therefore, this study was conducted to determine the regression model used and the factors that affect the human development index in Central Java Province in 2022. Some of the factors used in this study are life expectancy, average years of schooling, expected years of schooling and adjusted per capita expenditure. The method used in this research is multiple linear analysis, parameter significance test, and classical assumption test. By using the human development index as the response variable (Y), life expectancy (X1), average years of schooling (X2), expected years of schooling (X3) and adjusted per capita expenditure (X4) as predictor variables. From the results of the analysis that has been done, the equation Y = 6.55 + 0.4626X₁ + 1.341X₂ + 0.8971X₃ + 0.0008329X₄ +e is obtained. This shows that there is a relationship between the human development index and life expectancy, average years of schooling, expected years of schooling and adjusted per capita expenditure. The classical assumption test, namely the normality test, multicollinearity test, autocorrelation test and heteroscedasticity test, shows that the regression model can be used

The aim of this research is to 1) determine the influence of life expectancy on the human development index in districts/cities in Bali Province; 2) Knowing the effect of average length of schooling on the human development index in districts/cities in Bali Province; 3) Knowing the effect of poverty levels on the human development index in districts/cities in Bali Province; 4) Knowing the effect of life expectancy, average length of schooling, and poverty level on the human development index in districts/cities in Bali Province. Research was conducted in districts/cities in Bali Province. This research was conducted in districts/cities in Bali Province using 117 observation points taking into consideration the occurrence of disparities in life expectancy, average length of schooling, poverty level, and human development index between districts/cities in Bali Province. This research uses data released by the Bali Province Central Statistics Agency (BPS). The object of this research focuses on four main variables, namely life expectancy, average years of schooling, poverty level, and human development index. The data analysis techniques used to solve the problems in this research are: Classic Assumption Test and Hypothesis Testing with Multiple Linear Regression Analysis Techniques.Based on the results of the analysis, it is known that with a confidence level of 98.1 percent, all independent variables have a significant effect, both simultaneously and partially, on the dependent variable. This means that the factors studied which consist of life expectancy, average length of schooling and poverty level influence the human development index in districts/cities in Bali Province have a significant effect both partially and simultaneously. If we look at the high coefficient of determination, 98.1 percent of the variation in life expectancy, average years of schooling and poverty levels, which can explain variations in the human development index in districts/cities in Bali Province, so that they can provide different contributions for each model. Studies.

This study aims to examine the effect of life expectancy, poverty rate, and average years of schooling on the Human Development Index (HDI) in Bali Province, both partially and simultaneously. The sample consists of 9 regencies/cities in Bali Province from 2010 to 2024. The data analysis technique used is multiple linear regression analysis. The results of the study indicate that life expectancy and average years of schooling have a positive and significant partial effect on the Human Development Index in Bali Province. The poverty rate has a negative and significant partial effect on the Human Development Index. Furthermore, life expectancy, poverty rate, and average years of schooling simultaneously have a significant effect on the Human Development Index in Bali Province.

Bladder cancer is an international public health problem. It is the ninth most common cancer and the fourteenth leading cause of death due to cancer worldwide. Given aging populations, the incidence of this cancer is rising. Information on the incidence and mortality of the disease, and their relationship with level of economic development is essential for better planning. The aim of the study was to investigate bladder cancer incidence and mortality rates, and their relationship with the the Human Development Index (HDI) in the world. Data were obtained from incidence and mortality rates presented by GLOBOCAN in 2012. Data on HDI and its components were extracted from the global bank site. The number and standardized incidence and mortality rates were reported by regions and the distribution of the disease were drawn in the world. For data analysis, the relationship between incidence and death rates, and HDI and its components was measured using correlation coefficients and SPSS software. The level of significance was set at 0.05. In 2012, 429,793 bladder cancer cases and 165,084 bladder death cases occurred in the world. Five countries that had the highest age-standardized incidence were Belgium 17.5 per 100,000, Lebanon 16.6/100,000, Malta 15.8/100,000, Turkey 15.2/100,000, and Denmark 14.4/100,000. Five countries that had the highest age-standardized death rates were Turkey 6.6 per 100,000, Egypt 6.5/100,000, Iraq 6.3/100,000, Lebanon 6.3/100,000, and Mali 5.2/100,000. There was a positive linear relationship between the standardized incidence rate and HDI (r=0.653, P<0.001), so that there was a positive correlation between the standardized incidence rate with life expectancy at birth, average years of schooling, and the level of income per person of population. A positive linear relationship was also noted between the standardized mortality rate and HDI (r=0.308, P<0.001). There was a positive correlation between the standardized mortality rate with life expectancy at birth, average years of schooling, and the level of income per person of population. The incidence of bladder cancer in developed countries and parts of Africa was higher, while the highest mortality rate was observed in the countries of North Africa and the Middle East. The program for better treatment in developing countries to reduce mortality from the cancer and more detaiuled studies on the etiology of are essential.

The purpose of this study is to analyze the acceleration of the Expected Years of Schooling (EYS) and Average Years of Schooling (AYS) in West Java. The region continues to face several educational challenges, including uneven distribution of quality and quantity of teachers, disparities in the management of educational institutions, inadequate facilities and infrastructure, and limited access to education, particularly in rural areas. These challenges hinder the effective acceleration of EYS and AYS, preventing them from meeting the expected targets. This study employs a qualitative approach using a case study method, with data collected through triangulation of observations, interviews, and documentation analysis. The study concludes that the strategy for accelerating EYS and AYS in West Java is implemented in an integrated manner involving the Provincial Education Office, the Regional Office of the Ministry of Religion of West Java Province, and Higher Education Service Institutions. Despite the coordinated efforts in planning, implementation, control, and assessment, various obstacles remain, and the results have not yet met expectations. This is the case even though substantial funding has been allocated, including Central and Regional School Operational Cost Assistance and through the Smart Indonesia Card.

Purpose: This study aims to analyse the influence of education, health, and unemployment on the Human Development Index (HDI) in East Java, Indonesia. Given the disparities in HDI across regions, this research seeks to identify the extent to which these factors contribute to HDI variations in the province. Design/methodology/approach: A quantitative research approach was employed, using secondary data from the Central Bureau of Statistics (BPS) of East Java for the years 2021 and 2022. The study analysed data from 38 regencies/cities in East Java, using multiple linear regression to examine the impact of education (average years of schooling), health (number of hospitals), and unemployment (percentage of the labour force) on HDI. Findings: The results indicate that education and health have a significant positive impact on HDI, while unemployment has a significant negative effect. The findings confirm that increasing education levels and improving healthcare access contribute positively to human development, whereas higher unemployment rates hinder HDI growth. The coefficient of determination (R²) suggests that these three factors collectively explain a substantial portion of HDI variation in East Java. Research limitations/implications: This study is limited to secondary data analysis and does not consider qualitative factors such as policy effectiveness, governance, and social infrastructure that may also influence HDI. Future research should incorporate qualitative methods and longitudinal data to capture broader determinants of human development. Practical implications: The findings provide valuable insights for policymakers in East Java to focus on education and healthcare improvements while addressing unemployment challenges. Policy recommendations include expanding access to quality education, improving healthcare facilities in underdeveloped areas, and implementing effective employment programs to reduce regional disparities in HDI. Originality/value: This research contributes to the understanding of HDI determinants at the provincial level in Indonesia, offering empirical evidence on the interplay between education, health, and unemployment. The study highlights the need for integrated policy approaches to enhance human development in East Java. Paper type: Research paper

The paper investigates the relationship between human capital and economic growth in Morocco during the period from 1965 to 2015. In order to test this relationship we estimated a growth function using firstly the Johansen multivariate cointegration test and the Granger causality test. Secondly, we used the method of the Bayesian Model Averaging (BMA) that takes into consideration the uncertainty related to the specification of the model studied. In the theoretical literature, the difficulty of measuring human capital is often stressed. In order to overcome this problem, we use four proxies of human capital: first, we employ the average years of schooling. Second, we use the index of the gap in life expectancy between Morocco and developed countries. Third, we integrate the qualitative aspects of education and health by constructing two composite indicators of human capital using Principal Component Analysis (PCA) method.The main results of regression analysis confirm that in the specification of determinants of GDP per worker the average years of total schooling, the life expectancy index and the indicator of quality of health affect positively and significantly level of GDP per worker. However, in the specification of determinants of the growth of the GDP per worker, we found there is no proxy of human capital that affects significantly the growth of the GDP per worker.In addition, the results of Granger causality test show that only the indicator of quality of health that cause the GDP per worker. As well, these results show that the average years of total schooling and the indicator of quality of education cause the growth of GDP per worker. We suggest that the Moroccan authorities should make additional efforts to raise the level of quality of human capital especially in the health sector and increase the productivity of both public and private investment.

The HDI (Human Development Index) is one of the important components to measure the level of success in efforts to improve the quality of human life. The human development index is built with three dimensions, namely the longevity and health dimension, the knowledge dimension and the decent standard of living dimension. The longevity and health dimension is measured using Life expectancy at birth. The knowledge dimension is measured using expected years of schooling and average years of schooling. Meanwhile, the decent standard of living dimension is measured using Adjusted per capita expenditure. This study aims to find factors that influence HDI (Human Development Index) in Western Indonesia Region using machine learning models. The results obtained are that HDI is influenced by average years of schooling, expected years of schooling, Life expectancy at birth, and Adjusted per capita expenditure which are sorted from the most significantly influential. The model used in this study is GWR (Geographically Weighted Regression) with evaluation results including, AIC of 215.3162, AICc of 226.5107, and the accuracy level in the form of R-square of 99.38% which means this model is good to use.

This study aims to investigate the factors influencing the Human Development Index (HDI). Five variables—GDP per capita, health expenditure, education expenditure, infant mortality rate (per 1,000 live births), and average years of schooling—were analyzed to develop a regression model assessing their impact on HDI. The results indicate that GDP per capita, infant mortality rate, and average years of schooling are significant predictors of HDI. Specifically, the study finds a positive relationship between GDP per capita and average years of schooling with HDI, while infant mortality rate is negatively associated with HDI.