The Effect of Limited Internet and Electricity Infrastructure on the Utilization of AI in Environmental Data Collection in Remote Areas.
Keywords:
Limited Internet, Electricity Infrastructure, AI, Environmental Data Collection, Remote AreasAbstract
Limited technological infrastructure, especially internet access and electricity supply, is still a serious challenge for remote areas in collecting environmental data effectively. This condition has a direct impact on the region's ability to utilize artificial intelligence (AI) systems that require quality and up-to-date data. This study aims to analyze the influence of limited internet and electricity infrastructure on the effectiveness of the use of AI in the process of collecting environmental data in remote areas using quantitative methods with mediation analysis through the Partial Least Squares Structural Equation Modeling (PLS-SEM) technique. Data collection was carried out on community populations and field officers involved in environmental monitoring in remote areas, with a sample of 55 respondents. Respondents were selected using purposive sampling techniques to ensure that all participants had experience or direct involvement in environmental data collection activities. The research instrument is in the form of a questionnaire that includes indicators of infrastructure quality, technology support, and the effectiveness of AI-based data collection. The results show that the limitations of the internet and electricity have a significant negative effect on the effectiveness of data collection required by AI systems. The findings also show that technology support plays a role as a mediating variable that weakens the negative impact of limited infrastructure. In addition, increased technology support has proven to be able to improve the smooth process of data acquisition and delivery, even though the infrastructure is still minimal. In conclusion, limited infrastructure is a major obstacle to maximizing the use of AI in remote areas, but strengthening technology support can reduce this impact. The implications of this study emphasize the need to develop digital infrastructure and increase technological capacity to optimize the use of AI in environmental monitoring.
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