This study aimed to determine the socio-economic poverty status of those living in rural areas using data surveys obtained from household expenditure and income. Machine learning-based classification and clustering models were proven to provide an overview of efforts to determine similarities in poverty characteristics. Efforts to address poverty classification and clustering typically involve comprehensive strategies that aim to improve socio-economic conditions in the affected areas. This research focuses on the combined application of machine learning classification and clustering techniques to analyze poverty. It aims to investigate whether the integration of classification and clustering algorithms can enhance the accuracy of poverty analysis by identifying distinct poverty classes or clusters based on multidimensional indicators. The results showed the superiority of machine learning in mapping poverty in rural areas; therefore, it can be adopted in the private sector and government domains. It is important to have access to relevant and reliable data to apply these machine learning techniques effectively. Data sources may include household surveys, census data, administrative records, satellite imagery, and other socioeconomic indicators. Machine learning classification and clustering analyses are used as a decision support tool to gain an understanding of poverty data from each village. These strategies are also used to describe the profile of poverty clusters in the community in terms of significant socio-economic indicators present in the data. Village clusters based on an analysis of existing poverty indicators are grouped into high, moderate, and low poverty levels. Machine learning can be a valuable tool for analyzing and understanding poverty by classifying individuals or households into different poverty categories and identifying patterns and clusters of poverty. These insights can inform targeted interventions, policy decisions, and resource allocation for poverty reduction programs.

This study conducted a systematic literature review on current and emerging trends in the use of artificial intelligence (AI) for community surveillance, using the PRISMA methodology and the paifal.ai tool for the selection and analysis of relevant sources. Five main thematic areas were identified: AI technologies, specific applications, societal impact, regulations and public policy. Our findings revealed exponential growth in the development and implementation of AI technologies, with applications ranging from public safety to environmental monitoring. However, this advancement poses significant challenges related to privacy, ethics and governance, driving a debate on the need for appropriate regulations. The analysis also highlighted the disparity in the adoption of these technologies among different communities, suggesting a need for inclusive policies to ensure equitable benefits. This study contributes to the understanding of the current scenario of AI in community policing, providing a solid foundation for future research and developments in the field.

Malaria is an infectious disease that poses a significant global health threat, particularly to children and pregnant women. Specifically, in 2020, Rampah Village, Kutambaru sub-district, Langkat Regency, North Sumatra Province, Indonesia, reported 22 malaria cases, accounting for 84% of the local cases. This study aims to develop a malaria prevention model by leveraging community capital in Rampah Village. A mixed-method sequential explanatory approach, combining quantitative and qualitative methods, was employed. Quantitative data were collected through questionnaires from a sample of 200 respondents and analyzed using structural equation modeling (SEM) with Smart PLS (Partial Least Squares) software. The qualitative component utilized a phenomenological design, gathering data through interviews. Quantitative findings indicate that natural capital significantly influences malaria prevention principles. There is also a positive and significant relationship between developmental capital and malaria prevention. Cultural capital shows a positive correlation with malaria prevention, as does social capital. The qualitative phase identified cultural capital within the Karo tribe, such as ‘Rakut si Telu,’ which signifies familial bonds fostering mutual aid and respect. The results of this study are crucial for formulating policies and redesigning community-capital-based malaria prevention programs. These programs can be effectively implemented through cross-sectoral collaboration among health departments, local government, and community members. Malaria is a communicable disease threatening global health, particularly affecting children and pregnant women. In 2020, there were 229 million cases of Malaria worldwide, resulting in 409,000 deaths. In Indonesia, specifically in North Sumatra’s Langkat Regency, Kutambaru District, Rampah Village had 22 cases (84%). The purpose of this research is to formulate a Malaria prevention model using community resources in Rampah Village, Kutambaru District, Langkat Regency. The study employed a mixed-methods sequential explanatory approach, combining quantitative and qualitative methods. Quantitative data was collected through questionnaires, with 200 respondents, and structural equation modeling (SEM) analysis using smart PLS (Partial Least Squares) software. Qualitative data was gathered through interviews. The research findings showed a positive relationship between cultural modalities and Malaria prevention (p = 0.000) with a path coefficient T-value of 12.500. The cultural modality and Malaria prevention relationship were significantly positive (p = 0.000) with a path coefficient T-value of 3.603. A positive and significant correlation also exists between development modalities and Malaria prevention (p = 0.011) with a path coefficient T-value of 2.555. Qualitative research revealed the Rakut si Telu cultural modality of the Karo tribe, meaning that family-based social connections create a sense of helping and respecting one another. The Orat si Waluh cultural modality represents daily life practices in the Karo tribe as a form of community-based Malaria prevention.

With the continuous development of network has also greatly developed, exploring the role of social network relationships and attachment emotions on consumer intention helps community managers to promote community purchases for more consumer. As another core component of social e-commerce, social media influencer also has a significant influence on consumer intention. This study systematically analyzed the effects of social network relationships and social media influencer characteristics on consumer purchase intentions. Introduced consumer attachment and perceived value as mediating variables to construct the research framework of this study. This article adopts quantitative analysis methods to test the research hypotheses proposed. This article collected 600 first-hand data in the form of a survey questionnaire and analyzed the data using AMOS and SPSS statistical software. The empirical analysis in this article confirms that social network relationships has a significant impact on consumer purchase intentions; social media influencer characteristics has a significant impact on consumer purchase intentions; consumer attachment has a significant impact on perceived value;  consumer attachment plays a mediating role in the effect of social network relationships on consumers purchase intentions; perceived value plays no mediating role in the effect of social media influencer characteristics on consumer purchase intentions; perceived value plays a mediating role in the effect of consumer attachment on consumer purchase intentions; consumer attachment and perceived value have a chain mediating role between social network relationships and consumer purchase intentions.