Performance Evaluation of Machine Learning Algorithms for Supply Chain Data Classification

Maniah

doi:10.56741/IISTR.esl.001524

Authors

Maniah
maniah@ulbi.ac.id
Universitas Logistik dan Bisnis Internasional https://orcid.org/0009-0005-5392-6581

Keywords:

Data Classification, K-Nearest Neighbours, Machine Learning, Naïve Bayes, Predictive Analytics, Supply Chain

Abstract

Forecasting systems that are data-driven are of great importance in streamlining industrial and business processes during the digital transformation age. Supply chain management (SCM) is among the most significant processes for enhancing operational efficiency and supporting strategic decision-making. This study seeks to evaluate the performance of two machine learning-based classification algorithms, namely Naive Bayes and the k-Nearest Neighbours (K-NN) algorithm, using data in the supply chain. Some of the most valuable operational attributes, including payment method, customer segment, shipment status, profit per transaction, and customer location, are stored in the database. The data were first cleaned and then normalised and label-encoded, after which they were split into training and test sets with a ratio of 80:20. The performance of the two algorithms was assessed using accuracy, precision, recall, and F1-score. The findings of the research indicate that Naive Bayes is the most promising algorithm; its accuracy and precision are 99.75%, and its recall rate is close to 100% in the majority of the classes. These findings show that Naive Bayes is a probabilistic algorithm that better fits the data distribution than a distance-based K-NN algorithm.

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Author Biography

Maniah, Universitas Logistik dan Bisnis Internasional

Department of Magister Logistic Management

References

A. Belhadi, S. Kamble, A. Gunasekaran, and V. Mani, “Analyzing the Mediating Role of Organizational Ambidexterity and Digital Business Transformation on Industry 4.0 Capabilities and Sustainable Supply Chain Performance,” Supply Chain Management: An International Journal, vol. 27, no. 6, pp. 696–711, Nov. 2022, doi: 10.1108/SCM-04-2021-0152. DOI: https://doi.org/10.1108/SCM-04-2021-0152

L. V. Lerman, G. B. Benitez, J. M. Müller, P. R. de Sousa, and A. G. Frank, “Smart Green Supply Chain Management: A Configurational Approach to Enhance Green Performance through Digital Transformation,” Supply Chain Management: An International Journal, vol. 27, no. 7, pp. 147–176, Dec. 2022, doi: 10.1108/SCM-02-2022-0059. DOI: https://doi.org/10.1108/SCM-02-2022-0059

G. Wilson, O. Johnson, and W. Brown, “The Role of Machine Learning in Predictive Analytics for Supply Chain Management,” Aug. 05, 2024. doi: 10.20944/preprints202408.0343.v1. DOI: https://doi.org/10.20944/preprints202408.0343.v1

S. Munambar, A. W. Yuniasih, and A. Prayoga, “Design and Implementation of Functional Drink Product Inventory Applications at Kulon Progo MSMEs,” AJARCDE (Asian Journal of Applied Research for Community Development and Empowerment), pp. 228–235, Sep. 2024, doi: 10.29165/ajarcde.v8i3.501. DOI: https://doi.org/10.29165/ajarcde.v8i3.501

D. S. Manik, Nazaruddin, and N. Panjaitan, “Literatur Review: Penggunaan Algoritma Machine Learning untuk Optimalisasi Rantai Pasokan,” in Talenta Conference Series: Energy and Engineering (EE), 2025, pp. 1038–1047. doi: https://doi.org/10.32734/ee.v8i1.2671.

A. Asrul et al., “Pemanfaatan Big Data Analytics dalam Proses Manajemen Teknologi untuk Prediksi Permintaan Pasar,” Jurnal Minfo Polgan, vol. 13, no. 2, pp. 2433–2438, Jan. 2025, doi: 10.33395/jmp.v13i2.14516. DOI: https://doi.org/10.33395/jmp.v13i2.14516

V. Pasupuleti, B. Thuraka, C. S. Kodete, and S. Malisetty, “Enhancing Supply Chain Agility and Sustainability through Machine Learning: Optimization Techniques for Logistics and Inventory Management,” Logistics, vol. 8, no. 3, p. 73, Jul. 2024, doi: 10.3390/logistics8030073. DOI: https://doi.org/10.3390/logistics8030073

R. Dubey, A. Gunasekaran, and S. J. Childe, “Big data analytics capability in supply chain agility,” Management Decision, vol. 57, no. 8, pp. 2092–2112, Sep. 2019, doi: 10.1108/MD-01-2018-0119. DOI: https://doi.org/10.1108/MD-01-2018-0119

S. Andradóttir and J. S. Lee, “Pareto set estimation with guaranteed probability of correct selection,” Eur J Oper Res, vol. 292, no. 1, pp. 286–298, Jul. 2021, doi: 10.1016/j.ejor.2020.10.021. DOI: https://doi.org/10.1016/j.ejor.2020.10.021

D. Sari, N. Harahap, Y. Sasmita, S. A. Sitepu, and Arsyadona, “Manajemen Risiko dalam Rantai Pasok Global pada Masa Pandemi: Studi Kasus Ekspor Produk UMKM di Indonesia,” Jurnal Rumpun Manajemen dan Ekonomi, vol. 2, no. 4, pp. 115–123, 2025, doi: https://doi.org/10.61722/jrme.v2i4.5331.

G. Ambrogio, L. Filice, F. Longo, and A. Padovano, “Workforce and supply chain disruption as a digital and technological innovation opportunity for resilient manufacturing systems in the COVID-19 pandemic,” Comput Ind Eng, vol. 169, p. 108158, Jul. 2022, doi: 10.1016/j.cie.2022.108158. DOI: https://doi.org/10.1016/j.cie.2022.108158

D. Teh and T. Rana, “The Use of Internet of Things, Big Data Analytics and Artificial Intelligence for Attaining UN’s SDGs,” in Handbook of Big Data and Analytics in Accounting and Auditing, Singapore: Springer Nature Singapore, 2023, pp. 235–253. doi: 10.1007/978-981-19-4460-4_11. DOI: https://doi.org/10.1007/978-981-19-4460-4_11

H. Wu, J. Liu, and B. Liang, “AI-Driven Supply Chain Transformation in Industry 5.0: Enhancing Resilience and Sustainability,” Journal of the Knowledge Economy, vol. 16, no. 1, pp. 3826–3868, Jun. 2024, doi: 10.1007/s13132-024-01999-6. DOI: https://doi.org/10.1007/s13132-024-01999-6

E. Nurmiati and A. Nashikha, “OPTIMALISASI E-CRM PADA STARTUP DIGITAL UNTUK MENINGKATKAN RETENSI PELANGGAN: SYSTEMATIC LITERATURE REVIEW,” JURNAL PERANGKAT LUNAK, vol. 7, no. 2, pp. 133–143, Jun. 2025, doi: 10.32520/jupel.v7i2.4126. DOI: https://doi.org/10.32520/jupel.v7i2.4126

D. Nasien et al., “Perbandingan Implementasi Machine Learning Menggunakan Metode KNN, Naive Bayes, dan Logistik Regression Untuk Mengklasifikasi Penyakit Diabetes,” JEKIN - Jurnal Teknik Informatika, vol. 4, no. 1, pp. 10–17, Feb. 2024, doi: 10.58794/jekin.v4i1.640. DOI: https://doi.org/10.58794/jekin.v4i1.640

S. Sahar, “Analisis Perbandingan Metode K-Nearest Neighbor dan Naïve Bayes Clasiffier Pada Dataset Penyakit Jantung,” Indonesian Journal of Data and Science, vol. 1, no. 3, Dec. 2020, doi: 10.33096/ijodas.v1i3.20. DOI: https://doi.org/10.33096/ijodas.v1i3.20

P. D. Rinanda, B. Delvika, S. Nurhidayarnis, N. Abror, and A. Hidayat, “Perbandingan Klasifikasi Antara Naive Bayes dan K-Nearest Neighbor Terhadap Resiko Diabetes pada Ibu Hamil,” MALCOM: Indonesian Journal of Machine Learning and Computer Science, vol. 2, no. 2, pp. 68–75, Sep. 2022, doi: 10.57152/malcom.v2i2.432. DOI: https://doi.org/10.57152/malcom.v2i2.432

S. Baadel, K. Attai, B. Bassey, E. Attai, A. Majeed, and F.-M. Uzoka, “Comparative Analysis of Machine Learning Classifiers for Yellow Fever Diagnosis Using Causative Data: Evaluating Naïve Bayes, KNN, RIPPER, and PART,” 2025, pp. 160–169. doi: 10.1007/978-3-031-92178-0_13. DOI: https://doi.org/10.1007/978-3-031-92178-0_13

S. Ganesh and R. Baskar, “Comparative analysis of early detection of lung cancer through breath analysis by comparing K-nearest neighbours (KNN) with Naive Bayes,” 2025, p. 020169. doi: 10.1063/5.0262284. DOI: https://doi.org/10.1063/5.0262284

S. Saberi, M. Kouhizadeh, J. Sarkis, and L. Shen, “Blockchain technology and its relationships to sustainable supply chain management,” Int J Prod Res, vol. 57, no. 7, pp. 2117–2135, Apr. 2019, doi: 10.1080/00207543.2018.1533261. DOI: https://doi.org/10.1080/00207543.2018.1533261

M. M. Queiroz, D. Ivanov, A. Dolgui, and S. F. Wamba, “Impacts of epidemic outbreaks on supply chains: mapping a research agenda amid the COVID-19 pandemic through a structured literature review,” Ann Oper Res, vol. 319, no. 1, pp. 1159–1196, Dec. 2022, doi: 10.1007/s10479-020-03685-7. DOI: https://doi.org/10.1007/s10479-020-03685-7

X. Yu, L. Tang, L. Long, and M. Sina, “Comparison of deep and conventional machine learning models for prediction of one supply chain management distribution cost,” Sci Rep, vol. 14, no. 1, p. 24195, Oct. 2024, doi: 10.1038/s41598-024-75114-9. DOI: https://doi.org/10.1038/s41598-024-75114-9

R. W. Ahmad, H. Hasan, I. Yaqoob, K. Salah, R. Jayaraman, and M. Omar, “Blockchain for aerospace and defense: Opportunities and open research challenges,” Comput Ind Eng, vol. 151, p. 106982, Jan. 2021, doi: 10.1016/j.cie.2020.106982. DOI: https://doi.org/10.1016/j.cie.2020.106982

S. A. Sushma and K. T. G. Keerthan, “Comparative Study of Naive Bayes, Gaussian Naive Bayes Classifier and Decision Tree Algorithms for Prediction of Heart Diseases,” International Journal for Research in Applied Science & Engineering Technology (IJRASET), vol. 9, no. 3, pp. 475–486, 2021, Accessed: Sep. 19, 2025. DOI: https://doi.org/10.22214/ijraset.2021.33228

P. Cunningham and S. J. Delany, “k-Nearest Neighbour Classifiers - A Tutorial,” ACM Comput Surv, vol. 54, no. 6, pp. 1–25, Jul. 2022, doi: 10.1145/3459665. DOI: https://doi.org/10.1145/3459665

L. M. Sinaga, Sawaluddin, and S. Suwilo, “Analysis of classification and Naïve Bayes algorithm k-nearest neighbor in data mining,” IOP Conf Ser Mater Sci Eng, vol. 725, no. 1, p. 012106, Jan. 2020, doi: 10.1088/1757-899X/725/1/012106. DOI: https://doi.org/10.1088/1757-899X/725/1/012106

G. Naidu, T. Zuva, and E. M. Sibanda, “A Review of Evaluation Metrics in Machine Learning Algorithms,” 2023, pp. 15–25. doi: 10.1007/978-3-031-35314-7_2. DOI: https://doi.org/10.1007/978-3-031-35314-7_2

A. R. Deore, N. Shafighi, and A. A. Hajibashi, “Impact of Machine Learning on Supply Chain Optimization,” Archives of Business Research, vol. 12, no. 12, pp. 112–126, Dec. 2024, doi: 10.14738/abr.1212.18101. DOI: https://doi.org/10.14738/abr.1212.18101

A. S. Ahmed and H. A. Salah, “A comparative study of classification techniques in data mining algorithms used for medical diagnosis based on DSS,” Bulletin of Electrical Engineering and Informatics, vol. 12, no. 5, pp. 2964–2977, Oct. 2023, doi: 10.11591/eei.v12i5.4804. DOI: https://doi.org/10.11591/eei.v12i5.4804

F. Iseri, H. Iseri, N. J. Chrisandina, E. Iakovou, and E. N. Pistikopoulos, “AI-based predictive analytics for enhancing data-driven supply chain optimization,” Journal of Global Optimization, Jul. 2025, doi: 10.1007/s10898-025-01509-1. DOI: https://doi.org/10.1007/s10898-025-01509-1

S. Borrohou, R. Fissoune, and H. Badir, “Data cleaning survey and challenges – improving outlier detection algorithm in machine learning,” Journal of Smart Cities and Society, vol. 2, no. 3, pp. 125–140, Oct. 2023, doi: 10.3233/SCS-230008. DOI: https://doi.org/10.3233/SCS-230008

S. Bu, “Logistics engineering optimization based on machine learning and artificial intelligence technology,” Journal of Intelligent & Fuzzy Systems, vol. 40, no. 2, pp. 2505–2516, Feb. 2021, doi: 10.3233/JIFS-189244. DOI: https://doi.org/10.3233/JIFS-189244

M. B. Saputro and A. Alamsyah, “Comparison of Naive Bayes Classifier and K-Nearest Neighbor Algorithms with Information Gain and Adaptive Boosting for Sentiment Analysis of Spotify App Reviews,” Recursive Journal of Informatics, vol. 2, no. 1, pp. 37–44, Mar. 2024, doi: 10.15294/rji.v2i1.68551. DOI: https://doi.org/10.15294/jkrk0n56

R. Fauzan Mu’taz and A. Rosita, “Comparison of KNN and Naïve Bayes Classification Algorithms for Predicting Stunting in Toddlers in Banjaran District,” Journal of Applied Informatics and Computing, vol. 9, no. 5, pp. 2711–2717, Oct. 2025, doi: 10.30871/jaic.v9i5.9703. DOI: https://doi.org/10.30871/jaic.v9i5.9703

M. Nasrullah, B. Surarso, and O. D. Nurhayati, “Analysis of Naïve Bayes and K-Nearest Neighbors Algorithms for Classifying Fishermen Aid Eligibility,” Jurnal Penelitian Pendidikan IPA, vol. 10, no. 10, pp. 7652–7664, Oct. 2024, doi: 10.29303/jppipa.v10i10.8818. DOI: https://doi.org/10.29303/jppipa.v10i10.8818

M. C. Ramadhan, J. Wiratama, and A. A. Permana, “A Prototype Model on Development of Web-Based Decision Support System for Employee Performance Assessments with Simple Additive Weighting Method,” JSiI (Jurnal Sistem Informasi), vol. 10, no. 1, pp. 25–32, Mar. 2023, doi: 10.30656/jsii.v10i1.6137. DOI: https://doi.org/10.30656/jsii.v10i1.6137