Human Development Index In Indonesia Research Articles

Enhancing Human Development Quality in Indonesia: Socio-Economic and Technological Capabilities

Research Originality: Human development is a complex issue faced by various countries worldwide. Technology is considered to have the potential to significantly impact the landscape of human development due to its potential for penetration. This research contributes to the development literature in Indonesia by focusing on the important aspects of the influence of socio-economic factors and technological capabilities on the quality of human development.Research Objectives: This research aims to analyse how socio-economic and technological capabilities influence the quality of human development in Indonesia.Research Methods: Using panel data from 34 provinces spanning the period from 2015 to 2022, the research employs a Panel Data Regression Analysis model with the best-fitted Fixed Effect model.Empirical Results: The results of this study indicate that socio-economic factors, as seen through unemployment and poverty, significantly negatively impact Indonesia's Human Development Index (HDI). Economic growth has an insignificant effect on Indonesia's HDI. Technological capabilities, measured through the ICT Development Index, and the proportion of adolescents and adults aged 15-59 with ICT skills, have a significantly positive impact on Indonesia's HDI.Implications: The results of this research identify factors that can either encourage or hinder HDI, offering valuable insights for government policymakers to enhance HDI in Indonesia, particularly in each region.JEL Classification: O15, O30, O47How to Cite:Setyowati, I. Y., Malik, N., & Suliswanto, M.S.W. (2024). Enhancing Human Development Quality in Indonesia: Socio-Economic and Technological Capabilities. Etikonomi, 23(1), 93 – 108. https://doi.org/10.15408/etk.v23i1.35508.

Determinants of Human Development Index in Indonesia with Maqashid Sharia approach

The study examines factors that influence the Indonesian Human Development Index using the Maqashid Sharia approach. This study's sample is made up of several provinces with the Human Development Index for different categories of people. Data analysis employs a panel data regression model with a fixed effect model. The study's findings indicate that whereas variables Hifzu al-din, Hifzu al-aql, and Hifzu al-mal have a significant impact on the Index of Human Development, variables Hifzu al-nafs and Hifzu al-nasl have no significant impact on the Index of Human Development. Simultaneously, the variables Hifzu al-din, Hifzu al-nafs, Hifzu al-aql, Hifzu al-nasl, and Hifzu al-mal exhibit significant effects on the Human Development Index. Results of the determination test show that 98% of independent variables can influence dependent variables, with the remaining 2% being affected by variables not examined in this study. The study's findings recommended to the government that they support Hifzu Al-din in reducing crime rates, Hifzu Al-Aql by increasing the average length of schooling, and Hifzu Al-Mal in raising per capita income. By paying more attention to these issues, human development will eventually reach its full potential.

Towards Equity Projecting and Converging the Human Development Index in Indonesia

Human development has become an imperative for Indonesia amid the ongoing demographic bonus. However, various challenges persist in the realm of human development in the country, as evidenced by disparitie?s in Human Development Index (HDI) achievements. This research aims to project Indonesia's HDI for the years 2023, 2024, 2035, and the "Golden Indonesia" period in 2045 to discern the developmental trajectory of HDI and its disparities. Additionally, it seeks to measure the convergence of Indonesia's HDI to attain equitable development. The results of the trend projection estimations indicate that Indonesia's HDI is projected to be 73.74 in 2023 and 74.28 in 2024, which is anticipated to fall below the target set by the government, aiming for an HDI of 75.54 in 2024. Indonesia's HDI is projected to reach 80.22 in 2035 and 85.62 in 2045, categorizing it as 'Very High,' aligning with the vision for the Golden Indonesia in 2045. Furthermore, conditional convergence analysis results indicate that it would take 75 years for provinces with lower HDI to catch up with those with higher HDI, achieving zero inequality. Based on the estimation results, it is found that Per Capita Spending on Educational Functions is a significant variable influencing HDI convergence.
 
 Keywords: Human Development Index, Disparity, Convergence, Projection
 JEL Classification: C01, O11, O15

Pengaruh Angka Partisipasi Sekolah (APS) Dan Tingkat Partisipasi Angkatan Kerja (TPAK) Terhadap Indeks Pembangunan Manusia (IPM) di Indonesia

This study aims to determine the effect of the School Participation Rate (SPR) and the Labor Force Participation Rate (LFPR)) on the Human Development Index (HDI in Indonesia. This study uses secondary data sourced from the Indonesian Central Bureau of Statistics (BPS) for the 2012-2021 period. Data were analyzed using the Multiple Linear Regression analysis method. The results of the research partially show that the variable School Participation Rate (SPR) has a positive and significant effect on the Human Development Index (HDI) and the Labor Force Participation Rate variable (LFPR) has no effect and is not significant on the Human Development Index (HDI) in Indonesia. The results of the study simultaneously with the School Participation Rate (SPR) and Labor Force Participation Rate (LFPR) variables have a positive and significant effect on the Human Development Index (HDI) variable.

Spatial Analysis of The Human Development Index in Indonesia Before and During The Covid-19 Pandemic

In 2020 Indonesia’s HDI reached 71.94 or in a very high category, it is in 107th place out of 189. The HDI in every province in Indonesia shows varying numbers and has increased every year, but there has been a slowdown increase from 2019 to 2020, which caused by the Covid-19 pandemic. This study aims to analyze the variables which affect HDI in Indonesia before and during the Covid-19 pandemic, it also to analyze Covid-19 pandemic affect HDI in Indonesia. This research uses a quantitative method with descriptive analysis and inferential analysis by spatial regression using geoda software. The HDI before the Covid-19 pandemic is significantly affected by the percentage of poor residents and the university gross enrollment rate, while the during Covid-19 pandemic, HDI is also significantly affected by Gross Regional Domestic Product (GRDP) per capita and ratio of the number of hospital beds to 1.000 residents. Based on the spatial dependency test, it is known that there is a spatial dependency for HDI in Indoncovid-10esia during Covid-19 pandemic period. From these results, it is necessary to have a policy that is oriented to reducing poverty, increasing the scope of education services, improving macroeconomic conditions in a region and also ensure a sufficient number of hospital beds, especially during the Covid-19 pandemic.

The Influence of Expected Years of Schooling, Average Years of Schooling and Per Capita Expenditures on the Human Development Index in North Sumatra Province 2010-2022

This research aims to find out how the expected number of years of schooling, average length of schooling, per capita expenditure affect the human development index in Indonesia. The components of the independent variable X1 are the expected number of years of schooling, and variable X2 is the average number of years of schooling and variable X3 is per capita expenditure. The dependent variable (Y) is the Human Development Index. The population and sample for this study are data from BPS regarding the expected number of years of schooling, average length of schooling, per capita expenditure in North Sumatra province for the 2010-2022 period via the official website www.bps.go.id. The method used is a quantitative method with research data, namely secondary data. The collected data is processed using IBM SPSS 25, as well as journals and theses as references. The data analysis used is descriptive tests, classical assumption tests, regression tests, coefficient of determination and significance tests. The results of this research partially show that the average length of schooling and per capita expenditure have a significant effect on the human development index, while the expected number of years of schooling does not have a significant effect on the human development index.

IMPLEMENTATION OF THE K-MEDOIDS METHOD IN CLUSTERING HUMAN DEVELOPMENT INDEXES IN INDONESIA

The Human Development Index (HDI), which takes into account three fundamental aspects of human existence, a long and healthy life, knowledge, and a reasonable level of living, is one tool used to assess the effectiveness of human progress. Clustering provinces based on the human development index is important so that development disparities can be identified and help identify provinces with high, medium or low levels of development. The purpose of this study was to use the k-medoids approach to perform a cluster analysis of HDI in Indonesia based on life expectancy, average years of schooling, expected years of schooling, and expenditure per capita adjusted for 2022. The analysis indicate that two clusters were created: cluster 1 had a high human development index, while cluster 2 had a low human development index. More provinces belonged to cluster 1 than cluster 2 suggesting that human development index in Indonesia in 2022 was largely in the high category

Prediction of the Human Development Index for Equitable Development in West Sumatra Province Using the C4.5 Algorithm

Unequal development in Indonesia can be seen from the Human Development Index. The Human Development Index is a tool used to measure the attainment of the quality of life of a region or country and as material for economic policy on quality of life. It contains components of health level, education level and welfare level. In 2022, West Sumatra Province achieved the 9th highest Human Development Index in Indonesia, namely 73.26, with this figure the West Sumatra Province Human Development Index is above the national average. However, there are still regencies/cities in West Sumatra Province that have achievements below the national average. This factor causes the development conditions in West Sumatra Province to be uneven. Uneven human development conditions will make it difficult for the government to improve Human Resources (HR). In this research, the C45 Data Mining Algorithm was implemented to predict the Regency/City Human Development Index in West Sumatra Province. As is the method of the Central Bureau of Statistics in measuring the Human Development Index, the variables used from the Human Development Index indicators are Life Expectancy, Years of School Expectation, Average Length of Schooling, and Per Capita Expenditures. The Central Statistics Agency data used in this research covers all regencies/cities in West Sumatra during the period 2018-2022. Range levels are grouped into three groups, namely, low, medium, and high. Based on testing using RapidMiner software with the Cross Validation operator, an accuracy value of 86.61% was obtained.

Determinants Of Human Development Index In Papua Province 2012-2021

The Human Development Index (HDI) aims to emphasize that a country's development assessment should be based on the capabilities of its population. Papua, as the province with the lowest HDI in Indonesia, reflects the poor level of living and human development in Papua. Although Papua is a special autonomous region that receives an allocation of funds, its public services and people's welfare still lag behind other provinces in Indonesia. The purpose of this study is to understand how Special Autonomy Funds, Sharia Financing, Information and Communication Technology (ICT), and Average Length of School (ALS) affect the Human Development Index. This study uses Error Correction Model (ECM) as an analysis method. The results showed that ICT did not have a significant effect on HDI, Special Autonomy Fund had a negative influence in the short term and a positive influence in the long term, Average School Year had a positive influence in the short term and a negative influence in the long term, while Islamic Bank Financing had a positive influence both in the short and long term.

Analysis of Government Funding Performance on Economic Growth and Human Development Index in Indonesia

Economic growth shows the extent to which economic activity will generate additional public income in a certain period, that is, an economy is said to experience growth if the real income of the community in a certain year is greater than the real income of the community in the previous year. Economic growth is the most important factor in development that determines the success of the development of a region/region which is measured based on the high or low levels of economic growth achieved. This study aims to analyze the effect of General Allocation Funds (DAU), Special Allocation Funds (DAK) and Revenue Sharing Funds (DBH) on Economic Growth and Human Development Index (IPM). The object of research was carried out in South Halmahera Regency in 2012-2021 using the multiple linear regression analysis testing method. The results show that the influence of the General Allocation Fund, the Special Allocation Fund and the Profit Sharing Fund simultaneously influence Economic Growth with the Human Development Index in South Halmahera Regency. While the results of partial testing, the influence of General Allocation Funds, Special Allocation Funds and Profit Sharing Funds also affect Economic Growth with the Human Development Index in South Halmahera Regency. This indicates that the greater the regional potential revenue, it will be able to increase the achievement of Economic Growth and the Human Development Index.

Sustainable Development of Indonesia’s Land Border Areas; Overview from an Economic Perspective

The development of land border areas is continuously inevitable, so the government annually rolls out programs and policies. The sustainability of this development is expected to make the border area a center of economic growth, have a multiplier effect on the surrounding area, reduce poverty and backwash the economy, attract investment, and improve the country's income. In comparison, eliminates problems in the land border area, such as low indicators of development performance and regional isolation. This research was conducted in provinces with land border areas, namely West Kalimantan, East Kalimantan, North Kalimantan, East Nusa Tenggara, and Papua. Observations were made from 2015 – 2020 using secondary data sourced from the Central Bureau of Statistic. The research method used Principal Component Analysis to reduce thirteen to six variables of government programs supporting development performance in border areas. The reduced factors then used as variables in the Data Regression Panel to find out what programs affecting gross domestic product, poverty, and human development index in Indonesia's land border areas. The fifth variables are investment, health, agriculture, road infrastructure and markets and transportation. All variables have a significant effect on poverty and human development index, but not gross domestic product. This result shows a government economic strategy for developing sustainable land border areas.

Application of Principal Component Regression in Analyzing Factors Affecting Human Development Index

The human development index is an indicator to measure the quality of people's lives. If the human development index number increases, the better the quality of people's lives. There are many factors or variables that affect the level of the human development index, ranging from economic issues, education, health and other factors. However, not all factors have a positive and significant effect. Thus, this study aims to determine the factors that significantly affect the human development index in South Sulawesi. The method used in this study is principal component regression which involves many variables. The variables involved are expected length of schooling, average length of schooling, percentage of population with the highest Diploma, Bachelor and Masters education, school enrollment rate for people aged 7-24 years, percentage of poor people, spending per capita, and life expectancy. From the results of data processing using principal component analysis, 4 main components are obtained which represent the other components, for principal component regression, taking into account the cumulative proportion of > 80%. The results of this study indicate that the human development index in South Sulawesi is influenced by all the variables involved, which is equal to 95.7%. With the variable percentage of poverty being one of the variables that has a negative effect on HDI in South Sulawesi which shows that the higher the percentage of poverty, the lower the human development index. Thus, in order to increase the human development index in Indonesia, it is necessary to take strategic steps to improve people's welfare.

Analisis Determinan Indeks Pembangunan Manusia di Indonesia Tahun 1990-2021

The purpose of this study is to analyse the determinants of the human development index in Indonesia. The variables analyzed include economic growth, government spending on education and the inflation rate. The data period is 1990-2021, with the analysis method using the autoregressive distributed lag (ARDL) model. The results obtained show that economic growth and government spending have a positive and significant effect on HDI in both the long and short term, while inflation has a negative and significant effect on HDI. The theoretical implication is that there is a need for stable economic growth and increased spending on education to support an increase in people's welfare as reflected in the increase in the HDI value. In addition, the government needs to control the inflation rate more effectively.

Determinants of Tax Revenues and Human Development Index in Indonesia

This research is motivated by curiosity about the relationship between taxation and the Human Development Index.Taxes are one of the most important sources of government revenue for development financing, one of which is human development. The purpose of this study is to find out the extent of the relationship between taxation and the human development index. The samples used came from 30 provinces in Indonesia The method used in this study was quantitative descriptive analysis. Secondary data from 1991 to 2021 were used and analyzed using the Eviews application 12 The results of the study concluded that variables such as interest rates, tax ratios, working-age population, and exports affect tax revenues both in the short and long term

Bayes' approach of linear regression to modeling the human development index in Indonesia

Regression analysis is one of the data analysis techniques that can be used to examine the correlation between two variables, namely dependent variable and independent variable. It’s can be used to determine the parameter estimation of linear regression models are; the method of least squares or ordinary least square (OLS), Maximum likelihood estimation (MLE), and the Bayes method. Bayes' method defines the parameter as a random variable that describes the initial comprehension of the parameter before the observation was initiated and elucidated in an initial distribution refer as the prior distribution. The prior distribution used in this study is the pseudo prior distribution. The Data used in this study is secondary data, namely human development index (HDI) data in 2020, which was obtained from the website of the Central Statistics Agency (BPS). This study aims to estimate the regression model parameters using the Bayes method on the HDI data and the population data which adepts with information and communication technology (ICT) in Indonesia in 2020. The results of the specimen and analysis showed that population variables with ICT adepts have a significant effect on HDI variables. The results of the determination coefficient showed that 78.42% of HDI variables are affected by the population variables with ICT adepts while the remaining 21.58% are affected by other factors that have not been studied.
 Keywords: Bayes method, human development index, information communication and technology, linear regression, pseudo prior.MSC2020: 62F15

KLASIFIKASI INDEKS PEMBANGUNAN MANUSIA DI INDONESIA TAHUN 2022 DENGAN SUPPORT VECTOR MACHINE

The success of national development apart from being seen from the high rate of economic growth, the most important thing is the success in the aspect of human development. The Human Development Index is a measure used to monitor and evaluate human development. The purpose of this study is to measure the classification accuracy of the Human Development Index in Indonesia in 2022 with the Support Vector Machine (SVM) and a performance measurement tool for classification problems, namely the Confusion Matrix. Based on the research results, it can be obtained that the percentage accuracy of the classification of the Human Development Index in Indonesia in 2022 with the Support Vector Machine (SVM) is not directly proportional to the increase in the amount of training data, this conclusion implies that it is necessary to consider scientifically in determining the ratio of testing data and training data.

Comparison K-Means and Fuzzy C-Means Methods to Grouping Human Development Index Indicators in Indonesia

The Human Development Index (HDI) is an important indicator to measure the success of efforts to improve people's quality of life. The increase in the human development index in Indonesia is not accompanied by an even distribution of the human development index in every district/city in Indonesia. To facilitate the government in making policies and plans in overcoming the uneven HDI in Indonesia, it is necessary to group districts/cities in Indonesia based on HDI indicators. This study discusses the use of the K-means and Fuzzy C-Means algorithms with a total of 4 clusters. The grouping results obtained summarize that most districts/cities in Papua Island have low HDI indicators. The achievement of the HDI indicator in the medium category on the K-Means and Fuzzy C-Means methods is the same, spread across all major islands in Indonesia. However, the Nusa Tenggara Islands generally have a medium HDI indicator achievement. The achievements of the HDI indicators with high categories in the K-Means and Fuzzy C-Means methods are mostly found on the islands of Sumatra, Java, Kalimantan, and Sulawesi. The achievement of the HDI indicator in the very high category in the K-Means and Fuzzy C-Means methods is found in provincial capitals in several provinces in Indonesia as well as in big cities in Indonesia. The results of this study indicate that the S_DBW index and C_index values of the Fuzzy c-means method are smaller than the K-Means method, namely 2.312 and 0.105.

Comparison of Ensemble Learning Method: Random Forest, Support Vector Machine, AdaBoost for Classification Human Development Index (HDI)

Classification in supervised learning is a way to find patterns in data base that the classes are already known. In the classification of machine learning, there is a term called ensemble classifier. The workings of the ensemble classifier aimed to improve model accuracy and optimize classification performance. This study aims to analyze the comparison of algorithms that work with ensemble learning , including Random Forest, Support Vector Machine (SVM), and AdaBoost. The data used is the Human Development Index ( HDI ) of districts/cities in Indonesia . O ther variables that are strongly related to human development are GRDP per capita, gross enrollment rate, n et enrollment rate, labor force participation rate, unemployment rate, poverty rate, poverty depth, poverty severity, and average consumption per capita. The reason for using HDI is that apart from being an important macroeconomic variable in describing the condition of human resources in Indonesia, HDI already has a n obvious classification according to the Badan Pusat Statistik (BPS) so that supervised learning can be applied . Comparison of model evaluation using accuracy, specificity, sensitivity, and kappa statistics . The analysis flow starts with data preprocessing , resampling and cross - validation , then modeling using the Random Forest, Support Vector Machine (SVM), and AdaBoost algorithm . T he final stage is the model evaluation by comparing the best models in the classification s of districts/ cities according to HDI. The results showed that the Random Forest model had the best performance compared to the Support Vector Machine (SVM) and AdaBoost models with an accuracy value of 85,23%, spe c ifi c it y of 71,63% , sensitivit y of 95,05% , and kappa coefficient of 0,7698 . From this research, the an ensemble classifier can be developed to help classify scores on the Human Development Index in Indonesia. Keywords: AdaBoost, Random Forest, Support Vector Machine , Ensemble Learning, Human Development Index

Analisis Pengaruh Distribusi Produk Domestik Regional Bruto dan Laju Pertumbuhan PDB per Tenaga Kerja terhadap Indeks Pembangunan Manusia di Indonesia

The purpose of this study was to analyse theeffect of the distribution of GRDP and the growth rate of GDP per labor on thehuman development index in Indonesia by using panel data analysis. The scope of this research covers 10 provinces in Indonesia over period of 10 years (2011-2020) obtained from the Central Statistics Agency. The regression tool in this study uses eviews 9. The result show that the variable GDP distribution has a significant effect on the human development index, the variable GDP growth rate per labor has no significant effect on the human development index in Indonesia. And the highest intercept result in the 10 Provinces that have been analysed is DI Jogjakarta Province with an intercept 75,16%.

Human Capital and How It Affects the Performance of an Organization

Many countries' economies collapsed in early 2020 due to the worldwide financial crisis and the Covid 19 pandemic. The economic stability of several nations has been disrupted by the outbreak. In this article, we will examine the many ways in which human capital contributes to a company's success. The administration is making significant efforts to boost economic growth in a more positive direction. The Human Development Index in Indonesia appears to have increased by 3.3% between 2015 and 2019, according to data from UNDP 2020. According to the Human Development Index (HDI), Indonesia would be ranked 107 out of 189 countries in 2020. The Indonesian government has taken many different ways and made many different kinds of attempts to help its people grow and develop their skills so that they can keep competing with other nations. The companies discussed here also played a role in spurring this expansion. Organizations in the business world also want to survive, thrive, and remain competitive on a global scale. Businesses have a responsibility and a potential remedy to their lack of global competitiveness: they can stimulate the growth of their resources. The best way for businesses to improve their performance is to invest wisely and train their employees further. The success of businesses can be directly attributed to their investment in human capital. Human capital models that explain why it matters for businesses to invest in their people have been backed up by this research. Keywords: Human Development Index, A Sustainable and Globally Competitive Economy