Monitoring and Controlling System for Mango Logistics Based on Machine Learning

Monitoring and Controlling System for Mango Logistics Based on Machine Learning

decision tree; LSTM; machine learning; mango logistics

Fruits are highly perishable goods, which meansthey have a short shelf life and can pose significant challenges in trade. A longsupply chain can trigger the process of fruit spoilage. The logistics environment, both internal and external, can also affect the decreaseinquality of goods. One common issue facingproducers is the variability in consumer demandfor 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. Usingmachine learning algorithms, the system can analyze data from devices equipped withthe Internet of Things(IoT),such as temperatureand humidity sensors,to identify potential issues in the supply chain and provide recommendations to optimizelogistics operations. In this study, a machine learning-based monitoring system is developed to monitorthe shelf lifeof perishable goods, with a specific focus on mango fruit. The system utilizes LSTM to predictmango ripeness and decision tree algorithms to recommendfruit ripeness. The objective is to provide producers with recommendations that optimize the logistics process for high-quality mangoes and meet theconsumer demands for quality fruit.The implementation of a machine learning-basedlogistics monitoring and recommendation systemcan provide significant benefits tomango producers. Usingadvanced technologies,such as LSTM and Decision Tree algorithms, producers can optimize their logistics operations, improve fruit quality, reduce waste,and improvecustomer satisfaction

Buyung Achmad Hardiansyah1, Heru Sukoco2, Sony Hartono Wijaya

https://jurnal.iaii.or.id/index.php/RESTI/article/view/5226/904

Departmentof Computer Science, IPB University, Bogor, Indonesia

19-02-2025

FAJAR BAGUS W

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