Socioeconomics Transformation Through IoT and Deep Learning-Based Digitalization: Enhancing Investment Attractiveness in the Livestock Sector

Abstract

Digital transformation through the integration of Internet of Things (IoT) and deep learning technologies can revolutionize the livestock sector, particularly in the areas surrounding Indonesia's New Capital City (IKN). This study evaluates the role of IoT and deep learning in enhancing productivity and investment appeal within the livestock sector by adopting IoT-based monitoring systems and deep learning algorithms. The research employs a qualitative-descriptive approach with a case study method, involving 25 stakeholders, including farmers, government officials, technology developers, and investors. The findings demonstrate that implementing IoT-based monitoring systems and deep learning algorithms significantly improves operational efficiency by reducing manual labor, optimizing feeding schedules, and enabling real-time livestock health monitoring. These advancements have increased productivity, profit margins, and investor confidence. Digitalization fosters socioeconomic development by creating job opportunities, enhancing market access, and empowering local communities. The study concludes that integrating these advanced technologies transforms livestock farming practices and positions the sector as a strategic area for sustainable and inclusive investment. It is recommended that future policy frameworks prioritize the development of digital infrastructure and human resource training to ensure widespread adoption and long-term impact. This research underscores the importance of digital agriculture as a core pillar in advancing Indonesia's smart city agenda and rural economic transformation.