Monitoring and Controlling System for Mango Logistics Based on Machine Learning

  • Buyung Hardyansyah IPB University
  • Heru Sukoco IPB University
  • Sony Hartono Wijaya IPB University
Keywords: decision tree, LSTM, machine learning, mango logistics


Fruits are highly perishable goods, which means that they have a short shelf life and can pose significant challenges in trade. A lengthy supply chain can trigger the process of fruit spoilage. The logistics environment, both internal and external, can also affect the decline in the quality of goods. One common issue faced by producers is the variability in consumer demand for fruit quality. To address this problem, a machine learning-based logistics monitoring and recommendation system can be developed, utilizing the Long Short-Term Memory (LSTM) and Decision Tree algorithms. By utilizing machine learning algorithms, the system can analyze data from the Internet of Things (IoT) device-equipped sensors, such as temperature sensors and humidity sensors to identify potential issues in the supply chain and provide recommendations for optimizing logistics operations. In this study, a machine learning-based monitoring system is developed for monitoring the shelf-life of perishable goods, with a specific focus on mango fruit. The system utilizes LSTM for predicting mango ripeness and decision tree algorithms for recommending fruit ripeness. The objective is to provide producers with recommendations that optimize the logistics process for high-quality mangoes and fulfil consumer demands for fruit quality. The implementation of a logistics monitoring and recommendation system based on machine learning can provide significant benefits for mango producers. By leveraging advanced technologies such as LSTM and Decision Tree algorithms, producers can optimize their logistics operations, improve fruit quality, reduce waste, and enhance customer satisfaction.


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How to Cite
Hardyansyah, B., Heru Sukoco, & Sony Hartono Wijaya. (2024). Monitoring and Controlling System for Mango Logistics Based on Machine Learning. Jurnal RESTI (Rekayasa Sistem Dan Teknologi Informasi), 8(1), 150 - 159.
Information Systems Engineering Articles