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.

The aim of this study was to assess the challenges of rural landholding rights of women in Boloso sore Woreda. The population that used as source of data were sample womenfrom four kebeles,Kebele land administration committee members,Woreda women,youth and children office head,Woreda women’s association president,Woreda agriculture office head and Woreda agriculture office rural land administration desk experts.four kebeles from 28 rural kebeles selected by using systematic random sampling. Data gathered using questionnaire were analyzed using SPSS where descriptive and inferential were used for the purpose. Secondary data were collected from different relevant literatures such as reports, research results documents and publications. As to the findings,women landholding trend in the study area was highly contrasts legally ensured equal holding and using rights of women with men.The community including women themselves perceive women independent landholding as taboo and prohibits it.Even if they hold by different means,the plot of land they got or held was small in size and not conducive for agriculture and house construction. The awareness of women on rural land registration and certification benefit was also poor. Thus,rural women should be initiated to organize and struggle for their equal landholding and administering rights.

With the advancement of modernization, commoditization and grassroots governance have become important terms. Community governance not only promotes modern democracy but plays a key role in improving community governance capabilities and modernizing the governance system, which is receiving much attention. Despite the expanding number of articles on community governance, few evaluations investigate its evolution, tactics, and future goals. As a result, the particular goal of this study is to provide the findings of a thematic analysis of community governance research. Investigating the skills and procedures needed for practice-based community government. Data for this study were gathered through a thematic assessment of 66 papers published between 2018 and 2023. The pattern required by the researchers was provided by the ATLS.ti23 code used to record the review outcomes. This study proposes six central themes: 1) rural advancement, 2) community (social) capital, 3) public health and order governance, 4) governance technology, 5) sustainable development, and 6) governance model. The research results show that the research trend of community governance should focus on rural advancement, taking rural community governance as the starting point, the dilemma and adjustment of the governance model, community public health and order governance, and digital governance. It will yield new insights into new community governance standards and research trends.

The problem of flooding in the capital is still classified as a classic problem, but this problem still continues to emerge and becomes a trending problem during the rainy season in urban weather. This research aims to analyze the effectiveness of governance collaboration in overcoming the Jakarta flood problem. This research uses qualitative analysis and a content analysis approach. This research found that flood management using a collaborative governance approach was running optimally, the involvement of the private sector and the community was a good and rare synergy. support from international funding sources is used with effective management with the aim of using the budget on target. In the end, this research concludes that collaborative governance in Jakarta flood management is carried out optimally but requires sustainable collaborative efforts. This research has limitations in reaching the involvement of personal actors as a source of supporting information in disaster mitigation studies. Further research requires a more comprehensive discussion by reviewing the involvement of important actors in flood disaster mitigation.

Cooperatives have become significant contributors to the realization of the Sustainable Development Goals No. 1: No Poverty. Transitioning associations to cooperatives is crucial for promoting sustainable economic development, empowering communities, and enhancing collective well-being. This research assessed the readiness of Small-Scale Fisheries (SSF) communities in the Global South to form a cooperative. This research employed an exploratory research approach in six coastal Barangays of Batad, situated in the 5th District of Iloilo Province. The findings indicated that respondents have a slight level of awareness with regard to the advantages and economic advantages associated with becoming part of a cooperative. On the other hand, there was a clear difference in members’ perceptions of the benefits and financial returns that comes with belonging to a cooperative. According to the study, females are more likely to support the association’s move towards a cooperative structure, especially younger individuals. The main issue highlighted was the lack of skilled officers and inadequate resources and training for association members. A lecture on Cooperative Awareness and capability trainings on financial management, bookkeeping, and credit management should be organized in order to increase associations readiness to be a cooperative.

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.