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Dengue Hemorrhagic Fever (DHF) remains a major public health problem in Indonesia, particularly in densely populated areas. Control efforts require accurate data and spatial analysis to understand disease distribution patterns. Geographic Information System (GIS) is an effective tool for visualizing case distribution and supporting surveillance and planning of control programs at the primary healthcare level. This study aims to describe the spatial distribution of Dengue cases based on medical record data and produce a geographic distribution map to support Dengue control efforts at the Puskesmas level. This study used a quantitative descriptive design with secondary data from medical records at Karangsari Health Center. The sample consisted of 255 DHF patients in 2025, selected using a total sampling technique. Data were processed through editing, geocoding patient addresses, and spatial analysis using QGIS software.The results showed 255 Dengue  cases in 2025 with fluctuating monthly trends, peaking in April and lowest in December. Case distribution was uneven and tended to cluster. High-risk areas accounted for 15.7%–21.2%, moderate-risk areas 9.8%–15.7%, and low-risk areas 7.1%–9.8%. Megu Cilik Village had the highest proportion of cases, while other villages were categorized as moderate and low risk. This pattern indicates that Dengue incidence is influenced by environmental conditions, vector density, host factors, rainfall, and Aedes aegypti presence. GIS provides clearer spatial visualization, helping identify high-risk areas and supporting targeted public health interventions.

Per capita expenditure is an important indicator of household welfare because it reflects the economic capacity and consumption patterns of the community, as explained in Engel's Law. In regions with diverse geographical characteristics such as Papua Province, spatial analysis is needed to understand the variations in expenditure between districts/cities and the differences between urban and rural areas. This study aims to analyze the spatial distribution of per capita expenditure percentages for food and non-food items in nine districts/cities in Papua Province during the 2022–2024 period. The research data was sourced from the National Socioeconomic Survey (Susenas). The methods used included quantile-based choropleth mapping using QGIS, attribute data merging through table joins, and Pearson's correlation test to evaluate the consistency of spending patterns between years. The analysis results show that food and non-food spending patterns were relatively stable during the observation period with high correlation values (r = 0,85–0,93), although spatial variations between regions were still apparent. Mamberamo Raya Regency consistently had the highest proportion of food spending (>68%), while Jayapura City showed the lowest proportion. These findings indicate spatial disparities related to urbanization levels and economic access. Spatial visualization proved effective in revealing regional disparity patterns that were not fully apparent through conventional statistical tables and has the potential to support the formulation of more evidence-based regional development policies.

Flooding is one of the most recurrent natural disasters in Central Java Province, particularly during the rainy season. Diverse geographical characteristics, high rainfall intensity, and rapid urban development contribute to the region’s high vulnerability to flood hazards. According to the Central Java Statistics Agency, a total of 414 flood events and 407,784 affected victims were recorded between 2019 and 2021. This study aims to develop a web-based Geographic Information System (GIS) capable of mapping the spatial distribution and impact levels of floods across Central Java. The methodology includes collecting flood event data from the Central Java Statistics Agency, processing spatial data such as administrative boundary shapefiles, performing attribute integration between spatial and non-spatial datasets, and creating thematic maps using QGIS. The visualization outputs were exported into an interactive web format using the qgis2web plugin and subsequently integrated into a website developed with HTML, CSS, and JavaScript. The results show that the GIS system successfully visualizes flood-prone areas, identifies regions with high flood intensity, and enables users to explore detailed information through interactive digital maps. Additional website features—such as historical flood data, statistical summaries, and descriptive impact indicators—enhance the system's usefulness for disaster analysis. This study demonstrates the crucial role of GIS in supporting disaster mitigation, spatial planning, and policy evaluation related to flood management. Future research is recommended to incorporate more recent datasets and additional non-spatial variables such as rainfall intensity and floodwater depth to improve the system’s analytical accuracy and comprehensiveness.

This study aims to design and build a population distribution application in Demak Regency in 2025 using a Geographic Information System (GIS) approach. The study focuses on three main variables: population, population density, and population growth rate per sub-district. The author used the research method of collecting data and references that can later strengthen the results of this study and the application design using the waterfall model. Non-spatial data, namely data in the form of population information, was obtained from the Central Statistics Agency of Demak Regency, while spatial data is data related to regional administrative boundaries. Data processing was carried out using QGIS 2.18 through the stages of joining attributes, classification using the Natural Breaks (Jenks) method, and thematic map creation. The results show that population distribution is uneven. Demak Kota, Karangtengah, and Sayung sub-districts have the highest number and density, while coastal sub-districts such as Wedung and Bonang have low densities. The highest population growth rate is in Karangtengah sub-district at 0.8%. The application of GIS has proven effective in visualizing population distribution and supporting spatial-based regional development planning.

Acute Respiratory Infection (ARI) is an infectious disease that often affects the upper and lower respiratory tract. This disease is one of the main causes of death in children under five, especially in areas with less favourable environmental conditions. This study aims to map the distribution of ARI in Central Java Province using the K-Means clustering method. Through data analysis that includes inputting, transforming, processing, and visualisation, this study successfully identified three clusters of areas with different levels of ARI distribution. Cluster 0 indicates areas with low risk, such as Demak and Semarang Regency, Cluster 1 indicates areas with medium risk, such as Klaten, Magelang Regency, Pati, while cluster 2 indicates areas with high risk, including Semarang City and Surakarta City. The results of this analysis are presented as a map using QGIS to spatially visualise the distribution of ARI across Central Java. Thus, local governments can design more effective and targeted ARI prevention and control strategies.

Using End User Computing Satisfaction (EUCS) and User Centered Design (UCD) methods, this study investigated user satisfaction with QGIS and CarryMap.  The research objective was to deploy elements such as content, accuracy, ease of use, and timeliness.  A survey of students and teachers showed that users were more satisfied with the latest versions of both applications.  The findings show that the user interface and functionality have evolved, which indicates the direction of future development of mapping applications.

Basil Mycobacterium Tuberculosis adalah penyebab utama penyakit menular yang dikenal sebagai Tuberkulosis Paru. RSUD dr. Adhyatma, MPH Semarang memiliki 125 pasien TB paru pada tahun 2021. Tujuan penelitian adalah menganalisis persebaran kasus TB di Kota Semarang menurut jenis kelamin, usia, per kecamatan. Penelitian ini adalah penelitian deskripsi melalui observasi data sekunder. Pembuatan peta menggunakan aplikasi (QGIS). Hasil menunjukkan Sebagian besar kasus TB Paru adalah pada laki – laki, kelompok usia dewasa dan anak, dan domisili di Kecamatan Ngaliyan, Semarang Barat, Tugu, dan Mijen. Faktor yang berhubungan dengan TB Paru adalah kebiasaan merokok, pola hidup kurang sehat, status gizi, kurang istirahat, rumah tidak sesuai standar, lingkungan tidak sehat, interaksi sosial, perbedaan tingkat pengetahuan, dan Pendidikan. Saran penyuluhan kesehatan tentang penyakit TB Paru sebaiknya dilakukan di Kecamatan dengan kasus tertinggi di Kota Semarang. Peta persebaran kasus TB dapat digunakan untuk mengetahui informasi dan memantau perkembangan persebaran kasus TB di Kota Semarang.

Pemetaan wilayah wilayah pemasaran menjadi salah satu alat yang dapat digunakan untuk menggambarkan suatu kegiatan usaha untuk mengetahui sasaran yang tepat dalam pelaksanaannya di wilayah kerja tertentu. Penelitian ini mencoba merancang pemetaan wilayah pemasaran, diharapkan dengan penelitian ini dapat meningkatkan rencana strategis pemasaran yang tepat sasaran. Sehingga dapat membantu manajemen Dealer X cabang demak dalam menentukan kebijakan pemasarannya. Dalam pemetaan ini menggunakan software QGis untuk pemetaan. Hasil dari penelitian ini adalah peta keluaran yang dapat digunakan sebagai bahan pertimbangan bagian pemasaran untuk memetakan wilayah pemasaran yang ada di wilayah yang ditentukan.