Monitoring and Controlling System for Mango Logistics Based on Machine Learning

Buyung Hardyansyah; Heru Sukoco; Sony Hartono Wijaya

doi:10.29207/resti.v8i1.5226

Buyung Hardyansyah IPB University
Heru Sukoco IPB University
Sony Hartono Wijaya IPB University

DOI: https://doi.org/10.29207/resti.v8i1.5226

Keywords: decision tree, LSTM, machine learning, mango logistics

Abstract

Fruits are highly perishable goods, which means they have a short shelf life and can pose significant challenges in trade. A long supply chain can trigger the process of fruit spoilage. The logistics environment, both internal and external, can also affect the decrease in quality of goods. One common issue facing 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. Using machine learning algorithms, the system can analyze data from devices equipped with the Internet of Things (IoT), such as temperature and humidity sensors, to identify potential issues in the supply chain and provide recommendations to optimize logistics operations. In this study, a machine learning-based monitoring system is developed to monitor the shelf life of perishable goods, with a specific focus on mango fruit. The system utilizes LSTM to predict mango ripeness and decision tree algorithms to recommend fruit ripeness. The objective is to provide producers with recommendations that optimize the logistics process for high-quality mangoes and meet the consumer demands for quality fruit. The implementation of a machine learning-based logistics monitoring and recommendation system can provide significant benefits to mango producers. Using advanced technologies, such as LSTM and Decision Tree algorithms, producers can optimize their logistics operations, improve fruit quality, reduce waste, and improve customer satisfaction.

Downloads

Download data is not yet available.

References

S. Plazzotta, L. Manzocco, dan M. C. Nicoli, “Fruit and vegetable waste management and the challenge of fresh-cut salad,” Trends Food Sci Technol, vol. 63, hlm. 51–59, Mei 2017, doi: 10.1016/J.TIFS.2017.02.013.

W. P. Rahayu dan W. Adhi, “Penerapan Good Logistic Practices Sebagai Penunjang Ekspor Buah Tropis,” JURNAL MANAJEMEN TRANSPORTASI DAN LOGISTIK, vol. 2, no. 1, hlm. 93, Jul 2017, doi: 10.25292/J.MTL.V2I1.133.

D. Rodríguez Sáenz, C. Cruz, dan F. Lam, “AGRIBUSINESS SERIES E x p o r t H a n d b o o k s Export Logistics for Fresh and Processed Products Inter-American Program for the Promotion of Trade, Agribusiness and Food Safety,” 2009, Diakses: 22 Maret 2022. [Daring]. Tersedia pada: www.iica.int.

D. De Venuto dan G. Mezzina, “Spatio-Temporal Optimization of Perishable Goods` Shelf Life by a Pro-Active WSN-Based Architecture,” Sensors (Switzerland), vol. 18, no. 7, Jul 2018, doi: 10.3390/S18072126.

R. Torres-Sánchez, M. T. Martínez-Zafra, N. Castillejo, A. Guillamón-Frutos, dan F. Artés-Hernández, “Real-time monitoring system for shelf life estimation of fruit and vegetables,” Sensors (Switzerland), vol. 20, no. 7, Apr 2020, doi: 10.3390/S20071860.

G. Alfian, M. Syafrudin, dan J. Rhee, “Real-Time Monitoring System Using Smartphone-Based Sensors and NoSQL Database for Perishable Supply Chain,” Sustainability 2017, Vol. 9, Page 2073, vol. 9, no. 11, hlm. 2073, Nov 2017, doi: 10.3390/SU9112073.

R. Rivera-Lopez, J. Canul-Reich, E. Mezura-Montes, dan M. A. Cruz-Chávez, “Induction of decision trees as classification models through metaheuristics,” Swarm Evol Comput, vol. 69, hlm. 101006, Mar 2022, doi: 10.1016/J.SWEVO.2021.101006.

M. Hamdi, I. Hilali-Jaghdam, B. E. Elnaim, dan A. A. Elhag, “Forecasting and classification of new cases of COVID 19 before vaccination using decision trees and Gaussian mixture model,” Alexandria Engineering Journal, vol. 62, hlm. 327–333, Jan 2022, doi: 10.1016/J.AEJ.2022.07.011.

W. Sullivan, Machine learning Beginners Guide Algorithms: Supervised & Unsupervised learning, Decision Tree & Random Forest Introduction. 2017.

T. S. Tamir dkk., “Traffic Congestion Prediction using Decision Tree, Logistic Regression and Neural Networks,” IFAC-PapersOnLine, vol. 53, no. 5, hlm. 512–517, Jan 2020, doi: 10.1016/J.IFACOL.2021.04.138.

D. Chen, Q. Song, Y. Zhang, L. Li, dan Z. Yang, “Identification of Network Traffic Intrusion Using Decision Tree,” J Sens, vol. 2023, 2023, doi: 10.1155/2023/5997304.

R. M. Aguilar-Chinea, I. Castilla Rodriguez, C. Expósito, B. Melian-Batista, dan J. M. Moreno-Vega, “Using a decision tree algorithm to predict the robustness of a transshipment schedule,” Procedia Comput Sci, vol. 149, hlm. 529–536, 2019, doi: 10.1016/j.procs.2019.01.172.

M. Yaqub, H. Asif, S. Kim, dan W. Lee, “Modeling of a full-scale sewage treatment plant to predict the nutrient removal efficiency using a long short-term memory (LSTM) neural network,” Journal of Water Process Engineering, vol. 37, hlm. 101388, Okt 2020, doi: 10.1016/J.JWPE.2020.101388.

R. Navares dan J. L. Aznarte, “Predicting air quality with deep learning LSTM: Towards comprehensive models,” Ecol Inform, vol. 55, Jan 2020, doi: 10.1016/J.ECOINF.2019.101019.

A. Yadav, C. K. Jha, dan A. Sharan, “Optimizing LSTM for time series prediction in Indian stock market,” Procedia Comput Sci, vol. 167, hlm. 2091–2100, Jan 2020, doi: 10.1016/J.PROCS.2020.03.257.

S. Liu, X. Liu, Q. Lyu, dan F. Li, “Comprehensive system based on a DNN and LSTM for predicting sinter composition,” Appl Soft Comput, vol. 95, hlm. 106574, Okt 2020, doi: 10.1016/J.ASOC.2020.106574.

A. Tasnim, M. Saiduzzaman, M. A. Rahman, J. Akhter, A. Sayed, dan M. M. Rahaman, “Performance Evaluation of Multiple Classifiers for Predicting Fake News,” Journal of Computer and Communications, vol. 10, 2022, doi: 10.4236/jcc.2022.109001.

W. Zhang dkk., “LSTM-Based Analysis of Industrial IoT Equipment,” IEEE Access, vol. 6, hlm. 23551–23560, Apr 2018, doi: 10.1109/ACCESS.2018.2825538.

J. Satzinger, R. Jackson, dan S. Burd, SYSTEMS ANALYSIS AND DESIGN In a Changing World Seventh Edition. 2016.

R. S. Pressman dan B. R. Maxim, Software engineering : a practitioner’s approach. 2020.

B. Gupta, P. Uttarakhand, dan I. A. Rawat, “Analysis of Various Decision Tree Algorithms for Classification in Data Mining,” Int J Comput Appl, vol. 163, no. 8, hlm. 975–8887, 2017.

W. Sudjatha dan W. N. Wisaniyasa, Fisiologi dan Teknologi Pascapanen (Buah dan Sayuran). UDAYANA UNIVERSITY PRESS, 2017.

M. Massé, REST API Design Rulebook. O’Reilly Media, Inc., 2012.