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This study examines how effective leadership impacts increased employee productivity within the technical bureaucracy within the West Java Provincial Highways and Spatial Planning Agency. Effective leadership is understood as a leader's ability to provide direction, motivation, and empowerment to employees in order to optimally achieve organizational goals. The research method uses a quantitative approach through a survey of 50 ASN respondents working at the UPTD for Road Management Region III. The regression results show R = 0.58 and R² = 0.336, while the PLS analysis displays a path coefficient of β = 0.58 and a t-test of 5.91 (p < 0.05). These findings confirm that exemplary leadership, transparent communication, and employee empowerment can increase work productivity in the technical bureaucracy. This study also found that leadership that directly exemplifies behavior and decision-making can influence employee productivity levels in the public sector. Inspirational and participatory leadership, which involves employees in important decisions, shows a significant influence on increasing work morale and innovation. For example, employees who feel encouraged by their leaders to contribute more tend to show initiative in their tasks and take more responsibility for the work assigned.

Settlements are an important aspect in regional development, where settlements reflct human activities in fulfilling basic needs for housing. Wai Mhorock Village is one of the administrative villages located in Abepura District, Jayapura City. Based on Regional Regulation No. 18 of 2006 this study aims to analyze and examine the settlement patterns developing in this village and to identify whether population factors are the main influence on settlement distribution, or whether other factors play a role. The research method used is descriptive quantitative. Data collection was carried out through comprehensive field observation, documentation, in-depth interviews and data processing using applications such as ArcGIS, SPSS, or Excel.The results of this study show that the settlement distribution in Wai Mhorock Village is categorized as dispersed. This pattern occurs due to various factors, and based on regression results, it was found that population variables—such as those not yet working and those already employed—significantly influence the Y coefficient. From these findings, it is expected that policies can be formulated to enhance regional development and to address issues within Wai Mhorock Village.

Coastal areas are vital for ecosystems, social, and economic systems, yet they face challenges like erosion, land-use conversion, and weak community-based management. Serdang Bedagai Regency, located in North Sumatra, has significant ecological and socio-cultural potential, but this potential has not been sustainably managed. This study analyzes strategies for sustainable coastal land management by integrating local wisdom and village government roles. A mixed-method approach was used, including in-depth interviews, field observations, and surveys with 30 respondents from two coastal villages. The qualitative findings reveal that traditional practices, such as prohibiting mangrove cutting and communal conservation traditions, are still alive in the community. The village government plays a crucial role in strengthening institutions through village regulations (Perdes), environmental programs, and spatial monitoring. However, there is a gap between community understanding of ecological values and the implementation of regulations. Quantitative analysis using multiple linear regression shows that both local wisdom and the village government's role significantly influence sustainable coastal land management, with coefficients of 0.491 and 0.426, respectively (p < 0.05). Despite some heteroskedasticity, the model passed tests for multicollinearity and normality. These findings highlight the importance of combining traditional values with institutional support to balance coastal resource utilization and conservation. This study contributes to the development of a socio-ecological coastal governance model and offers practical recommendations for village governments, policymakers, and academics in creating participatory and sustainable coastal preservation programs.

This study aims to analyze the influence of domestic wastewater management and space utilization control on pests in river-equivalent areas in Lalat Regency. The main problem raised is the low effectiveness of environmental management in the corresponding area due to the increase in domestic activities and its weakness in spatial planning. The approach used is quantitative with multiple linear regression methods. Primary data were obtained through questionnaires to 200 respondents, and secondary data were obtained through documentation studies and field observations. The results of the analysis showed that simultaneously, the five independent variables (environmental management, space availability, community participation, government supervision, and environmental infrastructure) had a significant effect on the habitat in the corresponding area (p = 0.046). Partially, only waste management (p = 0.016) and community participation (p = 0.042) had a significant effect. A determination coefficient (R²) value of 7.5% indicates that the desires of a region are also influenced by other external factors. Based on these findings, it is recommended that the need to improve the household waste management system through the construction of communal WWTP and improve the quality of household septic tanks, as well as community empowerment through the establishment of environmental forums and sustainable sanitation education to support spatial control and poverty in commensurate areas.

The existence of Final Waste Processing Sites (TPA) in urban areas often presents a dilemma between the need for waste management infrastructure and its environmental and social impacts on surrounding communities. This study aims to examine the impact of the TPA Terjun in Medan City on patterns of land use change and the socioeconomic conditions of nearby residents. The method used is quantitative, involving a survey of 150 respondents, and multiple linear regression analysis to test the influence of two independent variables namely, the characteristics of the TPA and public perception and response on community perceptions of environmental impacts. The results indicate that both independent variables have a significant simultaneous effect on public perception, with a model significance value of 0.022. Partially, the physical characteristics of the TPA have a significantly positive influence, while community perceptions and responses also contribute meaningfully. However, the low coefficient of determination (R² = 0.009) suggests that many other factors outside the model also influence perceptions of environmental impact. This study highlights the importance of a holistic approach in spatial planning and urban environmental management, particularly in relation to public facilities such as TPAs. Community participation, infrastructure quality, and zoning certainty are crucial factors in maintaining the balance between the functional needs of the city and the desired quality of the microenvironment.

Slums are one of the biggest challenges in sustainable urban development. In the midst of the government's efforts to run a program to handle slum organizations, the issue of miscarriage after physical intervention often goes unnoticed. This study aims to stop the slum management program in Tegal Sari Mandala II Village, with a Mixed Methods approach that combines linear multiple quantitative regression analysis and a qualitative descriptive approach.The results show that community participation has a significant effect on poverty programs, while infra-structure quality has not shown a statistically strong relationship. Qualitative findings support these results, where residents feel more attached to the programs they have followed in the past. Meanwhile, a number of infrastructures that are not in accordance with needs are considered less effective in supporting sustainability. This study concludes that the desire of the program is not only determined by technical aspects, but also by social, institutional, and cultural factors that are intertwined. Strategic recommendations include strengthening participation, improving design based on local needs, and integration with spatial policies.

Sustainable management of coastal spaces is a major challenge in urban-coastal planning, especially in vulnerable areas such as Medan Belawan. This study aims to identify and analyze the influence of six independent variables on the sustainability of coastal space management. The variables studied included Microecological Inequality, Inclusivity of Coastal Asset Ownership, Adaptive Preparedness to Climate Risks, Inter-Functional Spatial Connectivity, Local Institutional Accountability, and Communal Social Resilience. The method used is a quantitative approach with a multiple linear regression model on primary data obtained through surveys, field observations, and in-depth interviews with 150 respondents.The results showed that five of the six variables had a significant effect on the sustainability of coastal space management. The most dominant variable was Local Institutional Accountability, followed by Asset Inclusivity, Ecological Inequality, Social Resilience, and Adaptive Readiness. Meanwhile, Connectivity between Spatial Functions does not have a significant impact. The regression model shows an R² value of 0.539 which means that 53.9% of the variation in coastal space management can be explained by these six variables.These findings imply the importance of strengthening participatory governance, equitable distribution of space access, and building community's adaptive capacity as a cornerstone for contextual and sustainable coastal spatial planning. Strategic recommendations are directed at institutional reform, community-based planning, and micro-ecological conservation in urban-coastal areas.

Slum settlements developing around Final Disposal Sites (TPA) represent a complex phenomenon that affects the quality of the urban living environment. In Karo Regency, the proximity of the TPA to residential areas has raised various issues such as air pollution, declining sanitation quality, and limited community participation in spatial planning processes. This study aims to analyze the influence of Distance to the TPA (X1), Length of Residence (X2), Community Participation (X3), Perception of TPA Impact (X4), and Household Income (X5) on the Quality of Slum Settlements (Y). A quantitative approach was applied using a multiple linear regression model, based on primary data from 200 respondents residing in areas affected by the TPA. The analysis revealed that, simultaneously, the four independent variables (X1–X4) significantly influence settlement quality, whereas household income does not show a significant effect. Partially, Distance to the TPA and Length of Residence have negative effects, while Perception of TPA Impact has a positive effect. Interestingly, Community Participation shows a negative influence, indicating ineffective engagement in environmental management. The R² value of 0.016 suggests that the model explains only a small portion of the variation, yet the F-test significance supports the overall model's relevance. Recommendations from this study include the importance of establishing a minimum 300-meter buffer zone between the TPA and residential areas, implementing sanitary landfill methods to replace open dumping systems, forming environmentally-based community forums with advocacy strength, and conducting community-based waste management training focusing on high-risk households. Strengthening participatory approaches through integration into village and sub-district spatial planning is essential for managing environmental impacts collaboratively and sustainably.

Changes in building functions in urban areas have become an inevitable phenomenon, especially in developing cities such as Binjai City. This study aims to analyze the factors influencing building function changes in the area, using a Mixed Methods approach that combines quantitative analysis through multiple linear regression and qualitative documentation based on field observations. Four independent variables were examined: inconsistency with the Spatial Plan (RTRW), accessibility, economic motivation, and law enforcement.The results show that economic motivation and accessibility have a significant influence on building function changes. Meanwhile, the variables of RTRW inconsistency and weak law enforcement did not show statistically significant effects. These findings are supported by visual field evidence indicating a massive conversion of building functions without effective spatial planning control. Photographic documentation reveals a tendency among the community to adapt building functions for economic activities in strategically accessible areas.The study concludes that building function changes in Binjai City are driven by economic needs and urban spatial dynamics that are not matched by supervision and enforcement of spatial planning regulations. Recommendations include strengthening spatial supervision, providing socio-economic-based incentives, simplifying permit processes, and implementing community-based public education. This research offers valuable contributions to adaptive urban planning while upholding the principles of spatial order and community aspirations.

Dengue Fever (DF) continues to be a major public health threat in Indonesia, especially in urban areas with high population density, such as Purwokerto City. This study aims to develop a predictive model to identify high-risk areas for DF outbreaks by integrating Machine Learning (ML) algorithms and Geographic Information Systems (GIS). The research utilizes historical dengue case data, meteorological parameters (rainfall, temperature, humidity), and population density as predictive variables. Three ML classification algorithms—Naïve Bayes, Logistic Regression, and Support Vector Machine (SVM)—were implemented to develop risk prediction models. Extensive data preprocessing, feature selection, and spatial integration were applied to ensure model robustness. The results show that the SVM model outperformed other methods, achieving the highest accuracy, precision, recall, and F1-score in classifying dengue risk zones. Risk maps generated through GIS visualization successfully identify priority areas for targeted interventions. The novelty of this research lies in the combination of local epidemiological data, multi-algorithm comparison, and geospatial mapping to improve early warning systems for DF in Purwokerto. This integrated approach is expected to support more effective prevention strategies and enhance public health preparedness.

This study aims to analyze the effect of retail layout on customer satisfaction levels at PT Trans Retail Medan Carrefour. In an increasingly competitive retail industry, store layout is one of the strategic factors that can affect comfort, shopping efficiency, and customer perceptions of service quality. This study uses a quantitative approach with descriptive and associative methods, involving 60 respondents who are active customers of Transmart Carrefour Medan. Data collection techniques were carried out through distributing questionnaires and direct observation. The results of multiple linear regression analysis show that retail layout has a significant effect on customer satisfaction, with a contribution of 75.9% to the variation in satisfaction levels. The layout factors that contribute the most include product placement, aisle design, lighting, signage, and ease of access between areas. These findings emphasize the importance of optimizing store layout as a strategy to improve the shopping experience and customer loyalty. Thus, retail management is advised to continue to evaluate and innovate in spatial planning in order to meet consumer expectations sustainably.  

The purpose of this study was to analyze the factors that influence the existence of slums in Tematta Dorf district, Medan. Slums are one of the serious problems in urban development that affect the quality of life of the community. The method used in this study is a quantitative method with a descriptive approach and multiple regression analysis. Respondents consisted of field surveys, interviews, data received through documents, and residents and relatives. The results of the study indicate that several main factors that influence the existence of slums in this area are income levels, population density, availability of basic infrastructure, and spatial politics. Limited infrastructure and low community income have the most important impacts of these factors. This study concludes that efforts to overcome regional slums require a multidimensional approach by improving infrastructure, spatial sustainability planning and strengthening the community economy.

Air quality in Western Java is highly dynamic and shaped by environmental changes influenced by intense human activities. Aerosols—tiny particulate matter that affects air quality, weather, and climate—can be quantified using Aerosol Optical Depth (AOD), which measures aerosol concentrations in the atmospheric column. This research uses spatial regression analysis to examine the spatial distribution of AOD from GEE’s platform (Google Earth Engine) and its relationship with rainfall and wind patterns during both the wet and dry seasons. The findings indicate that wind speed does not significantly impact AOD values, but wind direction does affect the distribution of rainfall and AOD, likely due to the monsoon system. During the wet season (December to March), high-intensity and widespread rainfall effectively cleanses the atmosphere of aerosols, leading to no significant effect on AOD (p-value > 0.05). In contrast, during the dry season, rainfall significantly influences AOD spatial patterns (p-value < 0.05). These results highlight the intricate interplay between meteorological factors and aerosol’s behavior, emphasizing the seasonal variability in their interactions.

Bali Province has a high population density, which has the potential to affect the distribution of infectious diseases such as diarrhea. However, mapping the distribution of diarrhea cases based on population density is still limited and does not meet good cartographic principles. Therefore, this study aims to present data on the distribution of diarrhea cases based on population density in Bali Province during the 2020-2022 period. This study used an analytic observational method with a cross-sectional design. The data used were secondary data from the Bali Province Health Profile and the Central Statistics Agency (BPS) for 2020-2022. Data analysis was carried out using geographic mapping with Geographic Information Systems (GIS) and statistical tests in the form of multiple linear regression and Spearman correlation. The results showed an increase in the strength of the relationship between population density and the number of diarrhea cases, as indicated by the rho value which increased from 0.1833 in 2020 to 0.6000 in 2022. However, this relationship was not statistically significant (p-value > 0.05), indicating that population density is not the only factor contributing to the increase in diarrhea cases. Other factors such as sanitation, access to clean water, and public awareness in maintaining hygiene also play a role in the spread of this disease. Although there is a trend that an increase in population density in Bali goes hand in hand with an increase in diarrhea cases, this relationship is not statistically strong enough. Therefore, a multidisciplinary approach is needed to address diarrhea cases, including improved sanitation facilities, public health education, as well as strengthening the spatial data-based disease monitoring system.

This research is grounded on the issue of employee performance seen from quality aspects of employees at the Public Works and Spatial Planning Office of Sikka Regency has not been maximized. This research aimed to: (1) determine the description of employee performance, human resource empowerment, and competence (2) analyze the influence of human resource empowerment and competency on employee performance either partially or simultaneously. The population in this research was 56 employees of the Public Works and Spatial Planning Office of Sikka Regency. Due to limited population, this research was conducted through census or saturated sampling. Data were collected through questionnaires and analyzed using descriptive methods and inferential statistics, namely multiple linear regression. Hypothesis testing was done through the F-test and t-test. The results of descriptive statistical analysis showed that the response perception and employee performance variables, human resources empowerment, and competency were categorized as good. The results of the t-test showed that partially the human resource empowerment variable had a positive and significant influence on employee performance. The results of the F test showed that the variables of human resource empowerment and competency simultaneously significantly affected employee performance. The results of the determination analysis showed that the two independent variables in this research were able to explain the fluctuation of employee performance at the Public Works and Spatial Planning Office of Sikka Regency by 31.2% .  

This research aims to: (1) determine the influence of office layout on employee work morale at the Tasikmadu District Office, Karanganyar Regency (2) determine the influence of communication on employee work morale at the Tasikmadu District Office, Karanganyar Regency (3) determine the influence of office layout and communication overall. together on the work spirit of employees at the Tasikmadu District Office, Karanganyar Regency. This research uses quantitative methods, the population in this research is all employees at the Tasikmadu District Office, Karanganyar Regency. The sampling technique was 37 at the Jaten District Office, Karanganyar Regency using saturated samples. The data collection technique uses a questionnaire technique. Data analysis techniques use the classic assumption test, t test, f test, multiple regression analysis. The results of this research are (1) there is an influence of office layout (layout) on the work spirit of employees at the Tasikmadu District Office, Karanganyar Regency. The t test shows that t_count is 3.429 > t_table 2.028, and the significance level is 0.002 < 0.05, so H_o is rejected and H_1 is accepted. (2) there is an influence of communication on the work spirit of employees at the Tasikmadu District Office, Karanganyar Regency. The t test shows that t_count is 3.928 > t_table 2.028, there is a significance level of 0.000 < 0.05, so H_o is rejected and H_2 is accepted. (3) there is an influence of office layout (layout) and communication on the work spirit of employees at the Tasikmadu District Office, Karanganyar Regency. The results of the F Test data processing show that F_(count ) 37.178 > F_(table ) 3.260, and the significance level is 0.000 < 0.05, so H_o is rejected and H_3 is accepted.    

Tropical diseases remain a serious public health challenge in Southeast Asia, particularly malaria, which has high morbidity and mortality rates. The complexity of their spread is influenced by various factors, including climate, environment, and population, requiring a spatially-based analytical approach to understand their distribution patterns. This study aims to develop a regression-based spatial model to predict the spread of tropical diseases and identify hotspots in high-risk areas. The data used include tropical disease case reports from national health agencies, climate data (temperature, rainfall, humidity) from BMKG and WorldClim, and population data (density and mobility) from  BPS and other official sources. The analysis was conducted using a Geographic Information System GIS for spatial mapping, as well as the application of spatial regression models, namely the Spatial Lag Model SLM and Spatial Error Model SEM. The results show that the developed model is able to predict disease distribution with a high level of accuracy, demonstrated by statistical validation through AIC, and Morans I. One of the main findings is the identification of malaria hotspots with a confidence level of 93, as well as the mapping of tropical disease risk predictions covering the Southeast Asian region. These results have significant implications for public health policy, particularly in resource allocation, prevention program planning, and priority area-based interventions. Furthermore, this study recommends the integration of big data and machine learning technologies to enrich predictive models and develop more adaptive early warning systems. Thus, this research contributes to strengthening tropical disease control strategies in Southeast Asia with a comprehensive spatial data-driven approach.

Neonatal mortality is a major issue in developing countries, particularly in Indonesia. Data reveals that Neonatal Mortality Rate (NMR) contributes to 59% of infant deaths in Indonesia. Infant mortality rates remain high in Indonesia, at 20 per 1,000 live births. West Java has recorded a significant decline in neonatal mortality rates, dropping from 9.9 per 1,000 live births in 2019 to 9 per 1,000 in 2021. Factors influencing neonatal mortality have been extensively studied, including through the Mixed Geographically Weighted Regression (MGWR) method. The MGWR model combines local and global models, generating parameter estimators that are both local and global according to the observation locations. This research uses secondary data from the health profile of West Java, with the dependent variable being the number of neonatal deaths in 27 districts/cities in the year 2020. MGWR analysis results indicate that congenital anomalies have a local impact, while low birth weight and complete neonatal visits affect the entire West Java region globally. This study offers vital insights into the factors contributing to neonatal mortality in West Java and can serve as a foundation for targeted policy improvements and healthcare interventions

Poverty is a complex problem that affects various aspects of life in various regions. In this study, we use the Geographically Weighted Regression (GWR) method to identify factors that cause poverty in North Sumatra Province. This approach allows a more detailed analysis of the relationship between independent variables and poverty levels in each geographic location. We use socio-economic and spatial data to build GWR models and identify possible hidden patterns in the distribution of poverty. The findings from this research provide valuable insights for stakeholders to formulate more effective policies in overcoming poverty in North Sumatra Province.

The quality of life in North Sumatra currently still needs improvement, where poverty rates and human development are still lagging. These problems can be reflected in the community welfare indicator, namely the Human Development Index (IPM). One of these problems can be solved by knowing the factors that determine the Human Development Index in North Sumatra. The Human Development Index is suspected to contain elements of spatial dependency, therefore in this study the spatial regression method will be used with a spatially dependent effect model. The results of the analysis and discussion obtained are that the SAR model is the appropriate model in the case of the Human Development Index in North Sumatra. Predictor variables that significantly influence the Human Development Index in North Sumatra are Pure Enrollment Rate (APM) at Senior High School level , Poverty Rate , Open Unemployment Rate , and PDRB on the basis of price .