Poverty is still a difficult problem to solve. The Bali Provincial Government has made various efforts to alleviate poverty. However, many other factors also contribute to exacerbating poverty in Bali Province. This study aims to determine how the dependency ratio and UMK affect employment opportunities and poverty in Bali Province. This research uses quantitative research with an associative approach. This study uses secondary data with 108 observations. Secondary data is obtained from the statistical institute of Bali Province. The statistical techniques used are panel data and path analysis. The results of this study show that the dependency ratio and UMK affect employment opportunities and poverty in Bali Province both directly and indirectly.

This authorship was motivated by the problems of economic development in Sidoarjo Regency during the Covid-19 pandemic, especially those that afflicted MSME actors. The economy in Sidoarjo Regency during the Covid-19 pandemic experienced negative economic growth, regional disparities, and MSME marketing problems. The data used is secondary data. The secondary data was obtained through literature study. The analysis technique used is qualitative descriptive, while the method used is Science and Technology Diffusion. The result of this writing is that Porong District succeeded in creating an innovation "e-Catalog of MSMEs, Porong District, Sidoarjo Regency". This website-based application innovation has various advantages. Therefore, this application is expected to be used in supporting the implementation of data collection, updating data, marketing programs, and problemsolving of existing problems in encouraging regional economic growth in the context of economic recovery after the Covid-19 pandemic. In addition, it also contributes to the achievement of regional priority program targets that have been set in regional development planning documents.

The purpose of this study is to (1) analyze the joint effects of economic growth, investment, technology and minimum wage on employment opportunities in districts/cities in Bali; (2) Analysis of partial effects of economic growth, investment, technology and minimum wage on employment opportunities in districts/cities in Bali. (3) Analyze the order of impact of economic growth, investment, technology and minimum wage on employment opportunities in districts/cities of Bali. This study is an associative quantitative study using panel data of up to 72 observations. Data sources for this study are secondary data from the Bali Provincial Central Bureau of Statistics and primary data obtained from informants through in-depth interviews. The collected data were then analyzed using several linear regression analysis techniques. Based on the analysis results, it is shown that (1) economic growth, investment, technology and minimum wage simultaneously have a great impact on employment opportunities in provinces/cities in Bali. (2) Economic growth and minimum wages can have a significant positive impact on employment opportunities in provinces/cities of Bali Province. Investments and technology may not affect employment opportunities in districts and cities in Bali. (3) Minimum wage has the greatest impact, followed by economic growth, investment and technology

The purpose of this study was to determine the effect of poverty, minimum wage and education level on the number of cruise ship migrant workers from the province of Bali. This research uses secondary data from 2017-2022 obtained from BP2MI and BPS. The data collected was then analyzed using panel data regression techniques. The results of the study show that poverty has a positive and significant effect on the number of cruise ship migrant workers from the Province of Bali. The UMK has a negative and insignificant effect on the number of cruise ship migrant workers from the Province of Bali. The level of education has a negative and insignificant effect on the number of cruise ship migrant workers from the Province of Bali. The implication of this research is a consideration for the government to provide job training for the community so that apart from just passing formal education they are also trained to be ready to work after graduating from education. keyword: Cruise Ship, Poverty, UMK, Education Level.

The problem that most often arises in development is unemployment. Development will be hampered if unemployment increases because it triggers the emergence of new problems in the economic and social fields. According to Sukirno (2006), unemployment is a situation where a person included in the labor force wants to get a job but has not got it yet. The purpose of this study was to determine the effect of the Gross Regional Domestic Product (GDP), Total Population, Human Development Index (IPM), and the Covid-19 Pandemic on the unemployment rate in the Province of Bali. The data used is secondary data, namely unemployment data, Gross Regional Domestic Product (GDP), Population Projection, Human Development Index (IPM), and the Covid-19 Pandemic as a dummy variable. This research was conducted in 8 regencies and 1 city in Bali Province from 2017-2021. This study was tested using panel data regression analysis techniques with the selected estimation model, namely the common effect model using Eviews software. The results of the research conducted show that the Gross Regional Domestic Product (GDP) has a negative and significant effect on unemployment, total population has no significant effect on unemployment, the Human Development Index (IPM) and the Covid-19 Pandemic have a positive and significant effect on unemployment in the Province of Bali. keyword: Unemployment, GRDP, Total Population, HDI, and the Covid-19 Pandemic.

One of the factors hindering the development of the country is the high unemployment rate. This research was conducted in Bali. The research theme is COVID-19 related to population, economic growth, education, and unemployment. The data collection method in this study was indifferent observation. Data are secondary data used in this study. Data source is from Badampsat Statistics of Bali Province. The purpose of this study is to partially and simultaneously determine the effects of COVID-19 on population, economic growth, education and unemployment in Bali over the period 2010-2021. The results of the analysis also show that population, economic growth, education and COVID-19 have had a significant effect on Bali's unemployment rate at the same time. In part, population has a positive and significant effect on Bali's unemployment rate. Economic growth and education had a negative and significant effect on unemployment in Bali, and COVID-19 had a significant effect on unemployment in Bali. Keyword: Population, Economic Growth, Education, Covid-19 and Unemployment.

This study aims to analyze the influence of the contribution of the tourism sector and investment on economic growth with local revenue (PAD) as the interventing variable. This research was conducted in Badung Regency during the period 2013-2020. This data is secondary time series data with seven years of observational data using participant observation data collection method. The data were analyzed using the SEM-PLS technique. The results this study indicate that the contribution of the tourism sector has a positive effect on economic growth. Investment has a positive effect on economic growth. The contribution of the tourism sector has a positive effect on PAD. Investment has positive effect on PAD. PAD has positive effect on economic growth. PAD is able to intervening variable of the contribution of the tourism sector to economic growth. PAD is not able to be an interventing variable of the effect of investment on economic growth.

One of the recurring economic issues is the challenge of poverty faced by various countries worldwide, including Indonesia. Poverty is evident through a high number of unemployed individuals. Those living in poverty and those facing unemployment generally encounter difficulties in accessing job opportunities, limited business development, income disparities, and lack of workplace protections, especially for women. These issues also affect economically disadvantaged groups in society. This study aims to analyze the impact of job opportunities and minimum wage on unemployment and poverty rates in the Regencies/Cities of Bali Province. The research utilizes secondary data from the period 2012-2021. The study covers 9 Regencies/Cities within Bali Province. Path Analysis is the employed analytical technique. The findings of this research indicate that: 1) Job opportunities have a significant negative impact on unemployment, while minimum wage has a significant positive impact on unemployment. 2) Job opportunities have a positive but insignificant impact on the poverty rate, while minimum wage has a significant negative impact on the poverty rate. Unemployment, on the other hand, has a positive but insignificant impact on the poverty rate. 3) Job opportunities do not have a direct impact on the poverty rate through unemployment, whereas minimum wage directly affects the poverty rate through unemployment. Keyword: Employment Opportunity, Minimum Wage, Unemployment, Poverty Rate.

Poverty is a problem faced by every country, especially in developing countries like Indonesia. Efforts are always made to minimize poverty, and even eliminate it if possible. However, in reality, poverty continues to remain an integral part of various aspects of human life. The complexity of the poverty issue continues to be a recurring focus in every developmental phase across different regions. It's worth noting that Bali Province has experienced rapid economic growth, but the unemployment rate and the number of people living in poverty remain high. Addressing the issue of poverty remains a primary focus that requires additional attention. Therefore, government policies are needed to address this problem, particularly in the districts and cities of Bali Province. This study aims to analyze the per capita Gross Regional Domestic Product (GRDP), unemployment, health complaints, and population numbers in relation to poverty in the districts and cities of Bali Province. The research employs secondary data spanning a period of 10 years, from 2012 to 2021. The study is conducted in Bali Province, encompassing 9 districts and cities. The analytical technique used is multiple linear regression analysis. The results of this study indicate that per capita GRDP does not have a negative impact on poverty in the districts and cities of Bali Province. Meanwhile, unemployment, health complaints, and population growth have a positive impact on poverty in the districts and cities of Bali Province. Keywords: Poverty, GRDP Per Capita, Unemployment, Health Complaints, Population Growth

Crime is a social problem that can disturb security and comfort in society. From 2017 to 2021 the crime rate that occurred in Bali Province shows a downward trend, but this figure is still relatively high. The purpose of this study is to analyze the simultaneous and partial effects of education, unemployment, poverty, per capita income, number of police, and pandemic on crime rates in districts/cities of Bali Province. This study uses secondary data collected from the Bali Regional Police and the Bali Province Central Statistics Agency (BPS). The analysis technique in this study is using panel data regression which produces the best model, namely the Fixed Effect Model (FEM). The results of this study indicate that 1) simultaneously the variables of education, unemployment, poverty, per capita income, the number of police, and the dummy pandemic affect the crime rate in the districts/cities of the Province of Bali; 2) partially the variables of education, per capita income, and the pandemic dummy have an effect on the crime rate in the districts/cities of Bali province, while the variables of unemployment, poverty, and the number of police officers have no effect on the crime rate in the districts/cities of the province of Bali. Keywords: Crime, Education, Unemployment, Poverty, Per Capita Income, Number of Police, and Pandemic.?

This study aims to analyze the effect of education level, minimum wage, investment, and infrastructure development costs on the inequality of income distribution between districts / cities in Bali Province. The location of the study is in districts / cities in Bali Province with secondary data obtained from publications from the Central Statistics Agency, Public Works Office, Spatial Planning, Housing, and Settlement Areas of Bali Province. The number of observations of this study was 90 observations with a period of 10 years and involved 9 regencies/cities in Bali Province. Data collection method in this study through non-participant observation techniques. The analysis technique used is panel data regression analysis. The results of the panel data regression test showed that the most appropriate model was the random effect model (REM). The results of this study show that education level, minimum wage, investment, and infrastructure development costs simultaneously affect income distribution inequally. Partially, the variables of education level and minimum wage have a positive and significant effect, while investment and infrastructure development costs have a negative and insignificant effect on the inequality of income distribution between districts / cities of Bali Province.

The purpose of this study is to examine the effects of investment and minimum wages on economic growth and unemployment, and to examine whether economic growth can mediate the effects of investment and minimum wages on unemployment. This study uses secondary data for the period 2017-2021. The study location is in Bali and includes nine provinces and cities. The data obtained were tested using the path analysis method using Eviews 9 software. Research results show that variable investment and minimum wage can affect economic growth. The investment and minimum wage variables have no effect on the unemployment rate. Investment and minimum wage variables can affect unemployment through economic growth variables.

The purpose of this study was to determine the simultaneous and partial effects of number of tourist, investment, dan average length of schooling on the open unemployment rate in the Bali Province regencies/cities. This research was conducted in nine regencies/cities in Bali Province. The data used is secondary data obtained from the Bali Province Central Bureau of Statistics and Bali Province Department of Tourism from 2017-2021. The number of samples in this study was 45. Data collection was carried through non-participant observation. The analysis technique used is panel data regression. Based on the result of the study it was found that the number of tourist, investment, and average length of schooling have a simultaneous effect on the open unemployment rate. The number of tourist has a negative and significant effect on open unemployment rate. Investment has a positive and insignificant effect on open unemployment rate. The average length of schooling has a positive and significant effect on open unemployment rate.

This study aims to explore descriptively the development of coffee plants in Indonesia. This research uses literature study and secondary data. Data mainly obtained from Coffee Statistics published by Indonesian Statistic Center. The results showed that the People's Plantation is the largest coffee plantation, with the highest production and highest productivity. Meanwhile, large state-owned companies and large private companies tend to experience a decrease in both the area of coffee plantations, their production, and productivity. In Indonesia, the provinces with the largest areas and high production followed by productivity are the provinces of South Sumatra, Lampung, Aceh, North Sumatra, and Bengkulu.

‎ In this paper a new one parameter probability distribution “Finite Mixture of Gamma ‎Distributions” is suggested from three com-ponent mixture of Exponential (θ), Gamma (4, ‎θ) and Gamma (3,θ) with mixing proportions θ^2/(〖(θ〗^2+6θ+2)),6θ/(〖(θ〗‎‎^2+6θ+2)) and 2/(〖(θ〗^2+6θ+2)) respectively. Its first four moments and ‎expressions for skew-ness, kurtosis, and coefficient of variation have been given. Various ‎mathematical and statistical properties of the proposed distribu-tion, including hazard rate ‎function, mean residual life function, and stress strength reliability, have been discussed. ‎Estimation of the parameter has been discussed. The goodness of fit of the distribution has ‎been discussed with two real-life data set and second-ary data. The fit has been compared ‎with one-parameter life time distributions, including Exponential, Lindley, and Chris Jerry ‎distributions.

Hyperglycemia has increased arachidonic acid thromboxane which is an effective ingredient in platelet activation. Thrombocytopenia in COVID-19 patients occurs through a variety of mechanisms, such as uncontrolled production of cytokines destroying bone marrow progenitor cells, direct inhibition of hematopoiesis viral infection of the bone marrow, increase in autoantibodies and immune complexes that cause platelet destruction, and lung injury that causes platelet aggregation and consumption of platelets so that platelets in the circulation are reduced. Objectives: To analyzed the ratio of platelet counts in COVID-19-positive patients who had different blood glucose levels. Methods: This research used secondary data, with types of analytical observational research as well as comparative study approaches. Statistical tests were conducted using an independent samples t-test with a confidence level of 95%. Results: The average platelet count was normal, but a low platelet count of 3.30% was obtained with normal blood glucose levels and a high platelet count of 2.20% with normal blood glucose levels, with a significance value of platelet count with normal blood glucose levels of 0.790 and platelet counts with blood glucose levels above normal which is 0.791 with a confidence level of 95%. Conclusion: There was no significant difference between the platelet count in COVID-19 patients with normal and above normal blood glucose levelsKeywords: blood glucose, platelets, COVID-19

The neutrophil-to-lymphocyte ratio (NLR) and Procalcitonin (PCT) are biomarkers that represent inflammatory activity and immune status as predictors for the prognosis of COVID-19 patients. NLR values and PCT levels are positively correlated with the severity of COVID-19, but the relationship between the two biomarkers is unclear. Objectives: To analyzed the correlation between NLR values and PCT levels in COVID-19 patients in Jember Regency. Methods: This cross-sectional analytic observational study, conducted from March to April 2022, took data from 444 confirmed COVID-19 patients at the Jember Clinic Plantation Hospital. Secondary data were obtained from medical records of inpatients for the period January–December 2021. Results: The average NLR value was 8.58, while the average PCT level was 0.43 ng/mL. The results of the Spearman Correlation Analysis showed a positive correlation between NLR values and PCT levels in COVID-19 patients in the Jember Regency with a p-value = 0.00 and a moderate degree of correlation with an r count of 0.41. Conclusion: There is a positive correlation between NLR values and PCT levels for COVID-19 patients in Jember Regency. It is necessary to carry out routine NLR and PCT examinations to consider predictors of disease severity and prognosis in COVID-19 patients.Keywords:  COVID-19, NLR, PCT

Diabetes mellitus is a chronic metabolic disorder characterized by hyperglycemia. Patients with diabetes mellitus require optimal glycemic control. Poorly controlled diabetes mellitus is at risk for complications, such as diabetic nephropathy. Diabetic nephropathy is characterized by albuminuria and can lead to decreased Glomerular Filtration Rate (GFR). Objectives: To determined the relationship between albuminuria and GFR in a patient with poorly controlled type 2 diabetes mellitus. Methods: This research was conducted at the Pontianak Branch of Prodia Health Clinic Laboratory in March 2021, using secondary data of laboratory examination results from DM patients in the 2020 period. This research was observational analytics with a cross-sectional approach. The number of samples that comply with the research criteria was 273 people. Bivariate analysis using Spearman’s correlation test. Results: Spearman's correlation test results showed significance (p=0.001) and correlation coefficient (r=0.209). Conclusion: Albuminuria and GFR in a patient with poorly controlled type 2 diabetes mellitus are significantly and positively correlated.Keywords:  albuminuria, poorly controlled type 2 diabetes mellitus, glomerular filtration rate

Posttraumatic stress disorder (PTSD) is a debilitating psychiatric illness, experienced by approximately 10% of the population. Heterogeneous presentations that include heightened dissociation, comorbid anxiety and depression, and emotion dysregulation contribute to the severity of PTSD, in turn, creating barriers to recovery. There is an urgent need to use data-driven approaches to better characterize complex psychiatric presentations with the aim of improving treatment outcomes. We sought to determine if machine learning models could predict PTSD-related illness in a real-world treatment-seeking population using self-report clinical data. Secondary clinical data from 2017 to 2019 included pretreatment measures such as trauma-related symptoms, other mental health symptoms, functional impairment, and demographic information from adults admitted to an inpatient unit for PTSD in Canada (n = 393). We trained two nonlinear machine learning models (extremely randomized trees) to identify predictors of (a) PTSD symptom severity and (b) functional impairment. We assessed model performance based on predictions in novel subsets of patients. Approximately 43% of the variance in PTSD symptom severity (R²avg = .43, R²median = .44, p = .001) was predicted by symptoms of anxiety, dissociation, depression, negative trauma-related beliefs about others, and emotion dysregulation. In addition, 32% of the variance in functional impairment scores (R²avg = .32, R²median = .33, p = .001) was predicted by anxiety, PTSD symptom severity, cognitive dysfunction, dissociation, and depressive symptoms. Our results reinforce that dissociation, cooccurring anxiety and depressive symptoms, maladaptive trauma appraisals, cognitive dysfunction, and emotion dysregulation are critical targets for trauma-related interventions. Machine learning models can inform personalized medicine approaches to maximize trauma recovery in real-world inpatient populations. (PsycInfo Database Record (c) 2025 APA, all rights reserved).

Burden of alcohol use and inclusion of alcohol use disorder medications in the essential medicine lists across 132 countries: An observational study