Perancangan Sistem Pendeteksi Stok Berbasis Machine Learning dan Mikrokontroler Untuk Digitalisasi Usaha Mikro Kecil dan Menengah

Abstract

Improving operational efficiency and inventory management is a major challenge for micro, small and medium enterprises (MSMEs) in the digital era. This research develops a digital model that integrates a microcontroller-based Internet of Things (IoT) and machine learning to improve inventory management for MSMEs. The aim is to explore how digital technologies can improve the operational efficiency of MSMEs, with a particular focus on inventory management. The methodology employed includes prototyping, using IoT and deep learning techniques for remote detection of product inventory levels. The findings show that the synergistic integration of IoT sensors and machine learning algorithms can significantly improve the efficiency of inventory management, by enabling real-time detection of product stock levels and providing accurate inventory data. The adoption of IoT and machine learning offers significant potential to improve the operational efficiency and business growth of MSMEs through more efficient inventory management. This study contributes to the understanding of the application of digital technologies in MSME inventory management,