Classification of E-Commerce Shipping Timeliness Using Supervised Learning Algorithm
DOI:
https://doi.org/10.57152/predatecs.v3i1.1855Keywords:
Classification, E-Commerce, Machine Learning, Naïve Bayes Classifier, Support Vector MachineAbstract
Developments in the e-commerce sector have increased rapidly since the onset of COVID-19, which has changed consumers' shopping habits. The growth in the number of e-commerce consumers affects the demand for long-distance delivery of goods. The problem of late delivery of goods is one of the challenges that is often experienced, and this can affect the level of customer satisfaction. This study aims to analyze whether the delivery of goods has been carried out according to schedule or has experienced delays. By using e-commerce shipping datasets obtained through the website, this research applies five supervised learning algorithms in the classification process, namely Decision Tree, Naïve Bayes Classifier, K-Nearest Neighbors (K-NN), Random Forest, and Support Vector Machine (SVM). The evaluation results show that dataset sharing using the K-Fold Cross Validation technique provides the best performance at K=8. Support Vector Machine showed the highest level of accuracy of 66.35%, followed by precision of 69.31% and recall of 66.35%. In contrast, the Naïve Bayes Classifier algorithm recorded the lowest performance with accuracy 64.22%, 97.73% precision, and 42.67% recall. These results show that the SVM algorithm is better at classifying the timeliness of delivery compared to the other four algorithms.
References
Y. Oyama, D. Fukuda, N. Imura, and K. Nishinari, “Do people really want fast and precisely scheduled delivery? E-commerce customers’ valuations of home delivery timing,” J. Retail. Consum. Serv., vol. 78, no. January, p. 103711, 2024, doi: 10.1016/j.jretconser.2024.103711.
A. Aboelenein and J. Crispim, “The economic impact of crowd-shipping based on public transport in Egypt: a GA approach.,” Procedia Comput. Sci., vol. 237, pp. 12–19, 2024, doi: 10.1016/j.procs.2024.05.074.
L. Guo and Y. Shang, “Decision-Making of Cross-Border E-Commerce Platform Supply Chains Considering Information Sharing and Free Shipping,” Sustain., vol. 15, no. 4, 2023, doi: 10.3390/su15043350.
S. Kandula, S. Krishnamoorthy, and D. Roy, “A prescriptive analytics framework for efficient E-commerce order delivery,” Decis. Support Syst., vol. 147, no. November 2020, p. 113584, 2021, doi: 10.1016/j.dss.2021.113584.
N. Giuffrida, J. Fajardo-Calderin, A. D. Masegosa, F. Werner, M. Steudter, and F. Pilla, “Optimization and Machine Learning Applied to Last-Mile Logistics: A Review,” Sustain., vol. 14, no. 9, 2022, doi: 10.3390/su14095329.
A. Jagoda, T. Ko?akowski, J. Marcinkowski, K. Cheba, and M. Hajdas, “E-customer preferences on sustainable last mile deliveries in the e-commerce market: A cross-generational perspective,” Equilibrium. Q. J. Econ. Econ. Policy, vol. 18, no. 3, pp. 853–882, 2023, doi: 10.24136/eq.2023.027.
V. Vrhovac and S. Vasi, “Measuring E-Commerce User Experience in the Last-Mile Delivery,” vol. 11, pp. 1–21, 2023, doi: https://doi.org/10.3390/math11061482.
M. Kiba-Janiak, J. Marcinkowski, A. Jagoda, and A. Skowro?ska, “Sustainable last mile delivery on e-commerce market in cities from the perspective of various stakeholders. Literature review,” Sustain. Cities Soc., vol. 71, no. April, 2021, doi: 10.1016/j.scs.2021.102984.
O. Rossolov and Y. O. Susilo, “Are consumers ready to pay extra for crowd-shipping e-groceries and why? A hybrid choice analysis for developing economies,” Transp. Res. Part A Policy Pract., vol. 187, no. July, p. 104177, 2024, doi: 10.1016/j.tra.2024.104177.
C. Yaiprasert and A. N. Hidayanto, “AI-driven ensemble three machine learning to enhance digital marketing strategies in the food delivery business,” Intell. Syst. with Appl., vol. 18, no. April, p. 200235, 2023, doi: 10.1016/j.iswa.2023.200235.
U. Sadyk, M. Yerzhan, R. Baimukashev, and C. Turan, “Comparative analysis of machine learning approaches in Kazakh banknote classification,” Bull. Electr. Eng. Informatics, vol. 13, no. 5, pp. 3552–3567, 2024, doi: 10.11591/eei.v13i5.8004.
S. J, C. Gangadhar, R. K. Arora, P. N. Renjith, J. Bamini, and Y. devidas Chincholkar, “E-commerce customer churn prevention using machine learning-based business intelligence strategy,” Meas. Sensors, vol. 27, no. February, p. 100728, 2023, doi: 10.1016/j.measen.2023.100728.
M. Bagriacik and F. E. B. Otero, “Multiple fairness criteria in decision tree learning,” Appl. Soft Comput., vol. 167, no. PA, p. 112313, 2024, doi: 10.1016/j.asoc.2024.112313.
C. Yaiprasert and A. N. Hidayanto, “AI-powered ensemble machine learning to optimize cost strategies in logistics business,” Int. J. Inf. Manag. Data Insights, vol. 4, no. 1, p. 100209, 2024, doi: 10.1016/j.jjimei.2023.100209.
Y. Bengio, Y. Lecun, and G. Hinton, “Deep learning for AI,” Commun. ACM, vol. 64, no. 7, pp. 58–65, 2021, doi: 10.1145/3448250.
S. N. Alsubari et al., “Data analytics for the identification of fake reviews using supervised learning,” Comput. Mater. Contin., vol. 70, no. 2, pp. 3189–3204, 2022, doi: 10.32604/cmc.2022.019625.
I. N. V. D. Naveen, K. Manamohana, and R. Verma, “Detection of malicious URLs using machine learning techniques,” Int. J. Innov. Technol. Explor. Eng., vol. 8, no. 4S2, pp. 389–393, 2019.
Z. C. Dwinnie, L. Khairani, M. A. M. Putri, J. Adhiva, and M. I. F. Tsamarah, “Application of the Supervised Learning Algorithm for Classification of Pregnancy Risk Levels,” Public Res. J. Eng. Data Technol. Comput. Sci., vol. 1, no. 1, pp. 26–33, 2023, doi: 10.57152/predatecs.v1i1.806.
I. T. Akinola, Y. Sun, I. G. Adebayo, and Z. Wang, “Daily peak demand forecasting using Pelican Algorithm optimised Support Vector Machine (POA-SVM),” Energy Reports, vol. 12, no. June, pp. 4438–4448, 2024, doi: 10.1016/j.egyr.2024.10.017.
A. V. D. Sano, A. A. Stefanus, E. D. Madyatmadja, H. Nindito, A. Purnomo, and C. P. M. Sianipar, “Proposing a visualized comparative review analysis model on tourism domain using Naïve Bayes classifier,” Procedia Comput. Sci., vol. 227, pp. 482–489, 2023, doi: 10.1016/j.procs.2023.10.549.
A. Shehadeh, O. Alshboul, R. E. Al Mamlook, and O. Hamedat, “Machine learning models for predicting the residual value of heavy construction equipment: An evaluation of modified decision tree, LightGBM, and XGBoost regression,” Autom. Constr., vol. 129, no. November 2020, p. 103827, 2021, doi: 10.1016/j.autcon.2021.103827.
H. S. Le et al., “Predictive model for customer satisfaction analytics in E-commerce sector using machine learning and deep learning,” Int. J. Inf. Manag. Data Insights, vol. 4, no. 2, 2024, doi: 10.1016/j.jjimei.2024.100295.
R. Hadiantini, Silalahi, and H. Hendrayati, “The Importance of Consumer Satisfaction for E-Commerce Users: How That Affects Consumer Behavior on Consumer Satisfaction?,” Proc. First Int. Conf. Sci. Technol. Eng. Ind. Revolut. (ICSTEIR 2020), vol. 536, no. Icsteir 2020, pp. 96–104, 2021, doi: 10.2991/assehr.k.210312.016.
H. Sun, Q. Zeng, J. S. L. Lam, S. Pu, and C. Qu, “Sales strategy selection for liner companies under shipping e-commerce considering canvassing ability competition,” Eur. J. Oper. Res., vol. 320, no. 1, pp. 85–100, 2025, doi: 10.1016/j.ejor.2024.07.027.
C. A. González Chávez et al., “Advancing sustainability through digital servitization: An exploratory study in the maritime shipping industry,” J. Clean. Prod., vol. 436, no. November 2023, 2024, doi: 10.1016/j.jclepro.2023.140401.
Z. Zhang, “Application of E-commerce Platform in Dry Bulk Shipping: Supply Chain Perspective,” Highlights Business, Econ. Manag., vol. 23, pp. 1230–1238, 2023, doi: 10.54097/6a0yef10.
Humairoh and Mohammad Annas, “Shipping, Tracking, And Returned Goods: The Determinants of Sustainable E-Commerce Supply Chain and Logistics Operation,” J. Manaj., vol. 14, no. 3, pp. 471–491, 2023, doi: 10.32832/jm-uika.v14i3.14282.
A. Y. H. Chai, S. H. Lee, F. S. Tay, P. Bonnet, and A. Joly, “Beyond supervision: Harnessing self-supervised learning in unseen plant disease recognition,” Neurocomputing, vol. 610, no. July, p. 128608, 2024, doi: 10.1016/j.neucom.2024.128608.
O. Peretz, M. Koren, and O. Koren, “Naive Bayes classifier – An ensemble procedure for recall and precision enrichment,” Eng. Appl. Artif. Intell., vol. 136, no. PB, p. 108972, 2024, doi: 10.1016/j.engappai.2024.108972.
S. Saifullah, D. B. Prasetyo, Indahyani, R. Drezewski, and F. A. Dwiyanto, “Palm Oil Maturity Classification Using K-Nearest Neighbors Based on RGB and L*a*b Color Extraction,” Procedia Comput. Sci., vol. 225, pp. 3011–3020, 2023, doi: 10.1016/j.procs.2023.10.294.
Y. Shahedi, M. Zandi, and M. Bimakr, “A computer vision system and machine learning algorithms for prediction of physicochemical changes and classification of coated sweet cherry,” Heliyon, vol. 10, no. 20, 2024, doi: 10.1016/j.heliyon.2024.e39484.
A. Pushpa Athisaya Sakila Rani and N. Suresh Singh, "Classification and identification of pest, diseases, and nutrient deficiency in paddy using layer based EMD phase features with decision tree," Inf. Process. Agric., no. xxxx, 2024, doi: 10.1016/j.inpa.2024.09.003.
P. F. Pratama, D. Rahmadani, R. S. Nahampun, D. Harmutika, A. Rahmadeyan, and M. F. Evizal, “Random Forest Optimization Using Particle Swarm Optimization for Diabetes Classification,” Public Res. J. Eng. Data Technol. Comput. Sci., vol. 1, no. 1, pp. 41–46, 2023, doi: 10.57152/predatecs.v1i1.809.
I. F. Nurahmadan, A. Agusta, P. A. Winarno, B. H. Sazali, Y. Thurfah, and A. Rosaliah, “Perbandingan Algoritma Machine Learning Untuk Klasifikasi Denyut Jantung Janin,” Semin. Nas. Mhs. Ilmu Komput. dan Apl., vol. 02, no. 1, pp. 733–740, 2021, [Online]. Available: https://conference.upnvj.ac.id/index.php/senamika/article/view/1411
P. D. Rinanda and Mustakim, "Implementation of PNN, ANN, And K-NN Algorithms on Indonesian Marketplace Reviews on Google Play Store," 2024 ASU Int. Conf. Emerg. Technol. Sustain. Intell. Syst. ICETSIS 2024, pp. 1070–1074, 2024, doi: 10.1109/ICETSIS61505.2024.10459477.
P. Song et al., “Analyzing psychological resilience in college students: A decision tree model,” Heliyon, vol. 10, no. 11, p. e32583, 2024, doi: 10.1016/j.heliyon.2024.e32583.
G. Pagliarini, S. Scaboro, G. Serra, G. Sciavicco, and I. E. Stan, “Neural-symbolic temporal decision trees for multivariate time series classification,” Inf. Comput., vol. 301, p. 105209, 2024, doi: 10.1016/j.ic.2024.105209.
V. Jackins, S. Vimal, M. Kaliappan, and M. Y. Lee, “AI-based smart prediction of clinical disease using random forest classifier and Naive Bayes,” J. Supercomput., vol. 77, no. 5, pp. 5198–5219, 2021, doi: 10.1007/s11227-020-03481-x.
Hubert, P. Phoenix, R. Sudaryono, and D. Suhartono, “Classifying Promotion Images Using Optical Character Recognition and Naïve Bayes Classifier,” Procedia Comput. Sci., vol. 179, no. 2020, pp. 498–506, 2021, doi: 10.1016/j.procs.2021.01.033.
J. B. Awotunde, S. Misra, V. Katta, and O. C. Adebayo, “An Ensemble-Based Hotel Reviews System Using Naive Bayes Classifier,” C. - Comput. Model. Eng. Sci., vol. 137, no. 1, pp. 131–154, 2023, doi: 10.32604/cmes.2023.026812.
P. Kumar and R. S. Thakur, “Liver disorder detection using variable- neighbor weighted fuzzy K nearest neighbor approach,” Multimed. Tools Appl., vol. 80, no. 11, pp. 16515–16535, 2021, doi: 10.1007/s11042-019-07978-3.
A. X. Wang, S. S. Chukova, and B. P. Nguyen, “Ensemble k-nearest neighbors based on centroid displacement,” Inf. Sci. (Ny)., vol. 629, no. January, pp. 313–323, 2023, doi: 10.1016/j.ins.2023.02.004.
S. Wang, F. Xie, F. Yang, S. Qiu, C. Liu, and T. Li, “Diagnosis of Disc Space Variation Fault Degree of Transformer Winding Based on K-Nearest Neighbor Algorithm,” Energy Eng. J. Assoc. Energy Eng., vol. 120, no. 10, pp. 2273–2285, 2023, doi: 10.32604/ee.2023.030107.
R. Chintalapudi, G. N. Kannaiyan, B. Pappula, and S. Makgato, “The novel Vogel’s approximation method integrated with a random forest algorithm in the vibration analysis of a two-directional functionally graded taper porous beam: Assessment,” Sci. African, vol. 26, no. July, p. e02397, 2024, doi: 10.1016/j.sciaf.2024.e02397.
G. Samodra and F. Setia, “Artificial Intelligence in Geosciences Benchmarking data handling strategies for landslide susceptibility modeling using random forest workflows,” Artif. Intell. Geosci., vol. 5, no. January, p. 100093, 2024, doi: 10.1016/j.aiig.2024.100093.
Z. Jin, J. Shang, Q. Zhu, C. Ling, W. Xie, and B. Qiang, “RFRSF: Employee Turnover Prediction Based on Random Forests and Survival Analysis,” Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), vol. 12343 LNCS, pp. 503–515, 2020, doi: 10.1007/978-3-030-62008-0_35.
Y. Wang, W. Liao, H. Shen, Z. Jiang, and J. Zhou, “Some notes on the basic concepts of support vector machines,” J. Comput. Sci., vol. 82, no. July, 2024, doi: 10.1016/j.jocs.2024.102390.
S. A. Salleh et al., “Support Vector Machine (SVM) and Object Based Classification in Earth Linear Features Extraction: A Comparison,” Rev. Int. Géomatique, vol. 33, no. 1, pp. 183–199, 2024, doi: 10.32604/rig.2024.050723.
Y. tao Zhu, C. shi Gu, and M. A. Diaconeasa, “A missing data processing method for dam deformation monitoring data using spatiotemporal clustering and support vector machine model,” Water Sci. Eng., vol. 17, no. 4, pp. 417–424, 2024, doi: 10.1016/j.wse.2024.08.003.
F. Xie and W. Yu, “Logistics Cost Prediction Based on Random Forest Model,” Acad. J. Comput. Inf. Sci., vol. 7, no. 2, pp. 109–114, 2024, doi: 10.25236/ajcis.2024.070215.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 Novrian Pratama, Rifka Anrahvi, Ahmed Tambal, Aryanshi Singh
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Copyright © by Author; Published by Institut Riset dan Publikasi Indonesia (IRPI)
This Public Research Journal of Engineering, Data Technology and Computer Science is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
