Abstract

One of the efforts to accelerate the achievement of the Sustainable Development Goals (SDGs) in Indonesia is carried out by accelerating village development. The digital village is one of the real development programs that is increasingly being carried out by Indonesia. The digital village is a driving force for strengthening smart village development. The Indonesian government fully supports village development through the issuance of the Village SDGs which were initiated due to the increasing enthusiasm of local governments in building smart villages. Because rural areas in Indonesia are dominated by agricultural activities, this research is focused on smart villages based on Agriculture Data Analytic (ADA). This study aims to analyze Indonesia's readiness in ADA-based smart village development through optimization of 2019 Village Potential data. The methods used in this study were descriptive analysis and multiple regression analysis and clustering. The variables used in the multiple regression analysis are the implementers of ICT management, implementers of agricultural based business activities, and implementers of renewable energy management in the village which are linked to existing sources of funds in the village. The variables used for clustering consist of 25 variables in 60.195  village potential data 2019, related to the condition of ICT infrastructure, ICT management, transportation, agricultural businesses and activities related to renewable energy. The results of multiple regressions show that the sources of regional budget revenue (APBD), Village Original Revenue (PAD),  self-subsistent (Swadaya) and other funds give a very significant contribution to the implementation of ICT management activities, agriculture, and transportation with an Adjusted R2 value of more than 0,9. However, different results show that the sources of Swadaya funds and PAD are less contributing to implementing renewable energy activities in the village (Adjusted R2 value is less than 0.6). The results of the clustering show that there are 5 village clusters with 5 levels of smart village potential. The clustering model has good validity with SSb value of 546.077,3. A quantitative approach of village potential data in Indonesia to assess the readiness of smart village development has not been widely used. Therefore, this research can be an alternative recommendation for stakeholders in formulating policies related to acceleration of village development in the context of regional development in Indonesia.