Judul: Integrasi Internet of Things dan Machine Learning untuk Logistik 4.0 pada Agroindustri Mangga Segar
Abstrak: Agroindustry serves as a vital economic driver by connecting farming, fresh handling and processing industries, and end consumers through an interconnected value chain. The advancements in agroindustrial engineering have expanded markets, thereby amplifying the logistics activities. However, maintaining the quality of perishable goods, like fresh fruit, during long-distance transportation remains a significant challenge in Indonesia due to inadequate postharvest handling and transportation conditions. Mangoes, a key horticultural product in Indonesia, are traded both domestically and internationally. Enhancing the quality assurance of these mangoes is crucial for increasing their market competitiveness. Real-time quality monitoring using Information and Communication Technology (ICT) and Internet of Things (IoT) has been identified as a solution to this issue. Moreover, machine learning can predict product quality and provide critical data for stakeholders to make informed logistics and quality-related decisions. The integration of IoT and machine learning can develop an accurate real-time quality monitoring system, boosting the competitiveness of Indonesia’s mango industry and reducing postharvest losses. This study aims to integrate IoT architecture with machine learning techniques to develop an intelligent system for the long-distance transportation of fresh mangoes, supporting the implementation of Logistics 4.0. The specific objectives are: (1) to investigate the respiration pattern and quality characteristics of mangoes during storage, (2) to model and classify mango maturity based on quality characteristics affected by storage conditions and duration, (3) to develop machine learning model for mango maturity prediction, (4) to create IoT architecture for real-time monitoring of long-distance mango transportation, and (5) to design an IoT-based information system for mango transportation. The proposed solutions include developing real-time monitoring of transportation conditions and real-time maturity prediction. This involves creating an information system, machine learning algorithms, IoT architecture and device, along with a model for maturity changes. Machine learning algorithm, IoT device, and information system’s performances were measured to ensure scalability. The research begins with system identification, followed by simultaneous modeling of maturity levels, IoT device engineering, and application development. A Deep Learning model is trained using datasets from laboratory observations of mango quality changes during storage and IoT-monitored environmental conditions. Transportation simulations validate the system's performance in both land and inter-island transportation. Findings indicate that mangoes with 85% maturity are optimal for long-distance transportation. A classification model for mango maturity based on quality attributes is developed. This model, utilizing changes in physicochemical parameters during storage, achieves over 95% accuracy in predicting maturity level changes during transportation. IoT devices successfully collect, record, and transfer transportation data to the database and information system. The information system provides accessible transportation information and maturity predictions to stakeholders via a web-based application. This dissertation exemplifies a compelling Artificial Intelligence of Things (AIoT) use case by integrating IoT architecture with machine learning techniques to tackle the logistics and transportation challenges of perishable goods. The IoT component involves sensors and devices that monitor environmental conditions, collecting real-time data transmitted to a central database. Machine learning models then analyze this data to predict mango maturity and quality changes during transportation, providing timely alerts to stakeholders for informed decision-making. This AIoT integration not only enhances quality monitoring accuracy
Keyword: Internet of Things (IoT), machine learning, Mangga, Agroindustri, logistik