The expansion of tourism locations that are both creative and of high quality is a significant contributor to the expansion of the economy. The stages of tourism development that are influenced by the progression of information technology are represented by the term "smart tourism" in the context of the ecosystem of smart villages. Integrating micro-enterprises with tourist practices is one of the ways that may be utilised to speed up the development of villages. By implementing the concept of smart tourism, tourism integrated with information and communication technology (ICT) can potentially improve both the economics and the services provided by the tourism industry. This research aims to analyse the clustering of smart tourism potential possibilities within the Kemang sub-district. These areas' clustering depends on some variables, including infrastructure (access for tourists), innovation, technology, local wisdom, distinctiveness, and economic conditions. The Fuzzy C-Means (FCM) clustering approach is utilised. A Geographic Information System (GIS) is utilised to facilitate the process of determining which villages are included in each cluster. This is done to describe potential areas better. The value of the cluster evaluation using the Davies Boulding Index (DBI) obtained is 0.3819, and the number of clusters with the best performance is 3. There is a very potential cluster in Cluster 3, comprising two villages (Kemang and Atangsanjaya). A potential cluster was also detected in Cluster 2, comprising three villages (Tegal, Pondokudik, and Parakanjaya). Furthermore, a fairly potential cluster was detected in Cluster 1, consisting of three villages (Jampang, Pabuaran, and Bojong). Specifically, in the Kemang sub-district, it is anticipated that the findings of this study will provide an overview of possible sites for implementing environmentally conscious tourism.

The construction of smart villages has begun in many Indonesian villages, along with the advancement of technology and local economic growth. Villagers must participate in constructing the smart economy-smart village by becoming familiar with the characteristics of the village's inhabitants using the citizen science model. This study intends to categorize villagers so that researchers can assess and decide their level of readiness for a smart economy in an ecosystem based on a smart village. Clustering is required to find communities of residents who are ready based on their traits. Using fuzzy C-Means with a Davied Bouldin Index value of 0.129, the data were divided into 4 clusters. The most important variables were chosen using information from the test's 300 responders, and the Kaiser Mayer Olkin assumption of 0.975 was used to validate the results. Our paper provides new information on how smart village readiness is assessed by the citizen science cluster. It was decided to divide residents into four groups: those who are less prepared (24.33%), those who are somewhat prepared (29.33%), those who are ready ( 25.67%) %), those who are ready (level of participatory knowledge), and those who are very ready for the smart economy (20.67%) based on the cluster model.

Unequal development is a major factor in measuring Indonesia's poverty problem, resulting in areas that are left behind. It is a primary concern of the government and stakeholders since it is unresolved and will lead to more problems in the community. Therefore, the government and stakeholders conduct various village developments to prosper the community. One is Batik village in Kemang, Bogor Regency, Indonesia. The Pakuan University of Bogor initiated the village development program. The collaboration forms a communication network and communication patterns to facilitate the delivery of messages. This study aims to identify and analyze the communication network pattern of stakeholders in the development of Kampung Batik in Kemang, Bogor Regency, Indonesia. This study is designed using a qualitative approach through interviews with relevant stakeholders. The communication network was analyzed using Sociometric Analysis through UCINET VI. The result indicates that the relationship between actors in the development of Kampung Batik is 75.5 percent and categorized as quite strong. Also, the communication pattern formed is an all-channel pattern with five actors who act as the star, bridge, opinion leader, liaison, cosmopolite, and isolate. This model supports the smart economy's potential to develop a smart village.

Indonesia is a country dominated by rural areas. Addressing rural poverty is a priority of the Indonesian government work program and an effort to achieve the Sustainable Development Goals (SDGs). One of the actual programs dealing with poverty is a digital village which is implemented in a smart village ecosystem. Since 2018, Indonesia has initiated various pilots of smart village projects. The success of a smart village is closely related to citizen science. The purpose of this research was to build a citizen science prospect model for a smart economy in a smart village ecosystem using Structural Equation Model – Partial Least Square (SEM-PLS) approach. This study proposes a novelty of measuring villagers' readiness to build a smart economy in a smart village ecosystem based on the strength of community support. We propose an assessment of the prospect of developing a smart economy in a smart village through the citizen science level that integrates exogenous variables of community support for the environment, citizen character, empowerment, entrepreneurship, innovation, and the smart economy. The citizen science model towards a smart economy showed a high level of predictive relevance, which was 87,2%. The citizen science model towards a smart economy can also explain empirical data with a GoF value of 0,488. This research showed that the indicators of Information Communication Technology (ICT), ICT literacy, access to education and research and development (R & D) facilitation, motivation for smart villages, and innovation in villages were driven by family participation. The collaboration with the private sector, local government, and communities drive the village's smart economy. The SEM PLS approach has not been widely used in research on the smart village component, especially the relationship between citizen science and the smart economy. Therefore, this research can fill the gap in smart village research, which is still dominated by a descriptive approach.