Analysis of Food Distribution Efficiency Clustering Based on Cost and Delivery Time Using the K-Means Algorithm
DOI:
https://doi.org/10.47065/bulletincsr.v6i6.1345Keywords:
Food Distribution; Distribution Efficiency; K-Means; Clustering; Transportation Cost; Delivery TimeAbstract
Food distribution efficiency is an important factor in maintaining supply chain performance, particularly in controlling transportation costs and ensuring timely delivery. Inefficiencies in distribution may lead to increased operational expenses and delays in delivering food products to their destinations. This study aims to analyze food distribution efficiency by grouping distribution activities based on transportation costs and delivery time using the K-Means clustering algorithm. The research stages consist of data collection, data preprocessing, feature selection, data standardization, determination of the optimal number of clusters, implementation of the K-Means algorithm, and cluster evaluation. Transportation cost and delivery time are used as the main variables to identify distribution patterns and classify distribution activities into efficiency groups. The resulting clusters are intended to distinguish distribution activities with relatively efficient, moderate, and less efficient characteristics, providing a basis for identifying operational patterns that require improvement. Data visualization is also used to support the interpretation of clustering results and facilitate comparisons among groups. The experimental results, based on 330 simulated food distribution records, produced three clusters. Cluster 1 consisted of 110 records, with an average distribution cost of IDR 176,363 and an average delivery time of 2.83 hours. Cluster 2 contained 111 records, with an average distribution cost of IDR 338,472 and an average delivery time of 5.83 hours. Cluster 3 comprised 109 records, with an average distribution cost of IDR 535,296 and an average delivery time of 9.49 hours. The clustering evaluation yielded a Silhouette Score of 0.679 and a Davies–Bouldin Index of 0.444. This study is expected to provide an analytical framework for supporting data-driven decisions in food distribution management. The proposed approach can assist distribution managers in evaluating cost and delivery-time patterns and identifying opportunities to improve operational efficiency. The validity of the findings depends on the quality of the operational data and the evaluation of the resulting clusters.
Downloads
References
M. M. Maulana, A. I. Suroso, Y. Nurhadryani, And K. B. Seminar, “The Smart Governance Framework And Enterprise System’s Capability For Improving Bio-Business Licensing Services,” Informatics, Vol. 10, No. 2, Jun. 2023, Doi: 10.3390/Informatics10020053.
W. Cetera, W. Gogo?ek, A. ?o?nierski, And D. Jaruga, “Potential For The Use Of Large Unstructured Data Resources By Public Innovation Support Institutions,” J. Big Data, Vol. 9, No. 1, Dec. 2022, Doi: 10.1186/S40537-022-00610-6.
M. Overton, S. Larson, L. J. Carlson, And S. Kleinschmit, “Public Data Primacy: The Changing Landscape Of Public Service Delivery As Big Data Gets Bigger,” Global Public Policy And Governance, Vol. 2, No. 4, Pp. 381–399, Dec. 2022, Doi: 10.1007/S43508-022-00052-Z.
W. Jin, “Challenges And Innovative Countermeasures Faced By Public Administration In The Context Of Big Data And Internet Of Things,” Math. Probl. Eng., Vol. 3 No.1 2022, 2022, Doi: 10.1155/2022/8949365.
E. & Erudisi, A. Zahara, M. I. Kabullah, And R. E. Putera, “Effectiveness Of The Ossrba (Online Single Submission Risk Based Approach) System In Business Licensing Services In Payakumbuh City Dpmptsp,” Jiee, Vol. 3, Pp. 22–40, 2023.
A. Alamsyah And F. Saviera, “A Comparison Of Indonesia’s E-Commerce Sentiment Analysis For Marketing Intelligence Effort (Case Study Of Bukalapak, Tokopedia And Elevenia),” Ustainable Collaboration In Business, Technology, Information And Innovation (Scbtii) Vol. 1, No. 1, Aug. 2022, Doi: 10.56397/Fms.2022.08.01.
L. Cao, “On Machine Learning And Public Administration,” Frontiers In Management Science, Vol. 1, No. 1, Aug. 2022, Doi: 10.56397/Fms.2022.08.01.
I. Kadek, A. Sugianta, N. Kadek, And W. Patrianingsih, “Evaluasi Naïve Bayes Dan K-Nearest Neighbor Dalam Klasifikasi Sentimen Ulasan Produk Skincare Msglow Di Tokopedia,” Journal Protekinfo |, Vol. 12, No. 1, 2025.
M. Rafli And B. Assegaf, “Penerapan Indobert Untuk Analisis Sentimen Berbasis Aspek Pada Evaluasi Aplikasi Layanan Publik,” Infotech Journal, Vol. 12, No. 1, Pp. 93–101, Apr. 2026, Doi: 10.31949/Infotech.V12i1.17555.
Y. C. Tapidingan And D. Paseru, “Comparative Analysis Of Classification Methods Of Knn And Naïve Bayes To Determine Stress Level Of Junior High School Student” Indonesian Journal Of Information Systems, Vol 2, No 2, Pp. 2020.
A. T. Talmera, M. Wardhana, And V. Ratnasari, “Analysis Of The Impact Of Ui/Ux Elements On User Satisfaction And Loyalty In E-Commerce Platforms: An Empirical Study On The Tokopedia Platform,” Journal Of Social Research, 2025, [Online]. Available: Http://Ijsr.Internationaljournallabs.Com/Index.Php/Ijsr
Z. Sitorus, D. Prayogi, M. A. Rizko, A. G. Suteja, And M. R. Harahap, “Implementation Of The Insertion Sort Algorithm To Sort Positive Integers In Ascending Order Using Flowgorithm,” Journal Of Information Technology, Computer Science And Electrical Engineering (Jitcse), Vol. 1, No. 3, Pp. 323–328, 2024, Doi: 10.30596/Jitcse.
M. S. Novelan And S. Aryza, “Optimization Cvrp With Machine Learning For Improved Classification Of Imbalanced Data Food Distribution,” Jitk (Jurnal Ilmu Pengetahuan Dan Teknologi Komputer), Vol. 10, No. 4, Pp. 917–925, Jun. 2025, Doi: 10.33480/Jitk.V10i4.6467.
N. Wulandari, Y. Cahyana, And H. Hikmayanti Handayani, “Sentiment Analysis On The Relocation Of The National Capital (Ikn) On Social Media X Using Naive Bayes And K-Nearest Neighbor (Knn) Methods,” Journal Of Applied Informatics And Computing 2025. [Online]. Available: Http://Jurnal.Polibatam.Ac.Id/Index.Php/Jaic
K. Khairul, A. H. Nasyuha, A. Ikhwan, M. H. Aly, And A. Ahyanuardi, “Implementation Of Multiple Linear Regression To Estimate Profit On Sales Of Screen Printing Equipment,” Journal Infotel, Vol. 15, No. 2, Pp. 55–61, Jun. 2023, Doi: 10.20895/Infotel.V15i2.934.
H. W. Dhany, F. Izhari, And S. Komputer, “Prediction Analysis Condition Animal Use Algorithm (Svm+Knn), Jurnal Ict?: Information And Communication Technologies, 15 (2) (2024) 48-54, 2024. [Online]. Available: Www.Ejournal.Marqchainstitute.Or.Id/Index.Php/Jict
A. S. Rizkia, W. Wufron, And F. F. Roji, “Analisis Sentimen Coretax: Perbandingan Pelabelan Data Manual, Transformers-Based, Dan Lexicon-Based Pada Performa Indobert,” Malcom: Indonesian Journal Of Machine Learning And Computer Science, Vol. 5, No. 3, Jul. 2025, Doi: 10.57152/Malcom.V5i3.2151.
R. Farta Wijaya, F. Kurniawan, R. R. Putrai, A. Alvin, F. Sains, And D. Teknologi, “Optimizing The Management Of Village Activities Through Information Systems For Transparency And Accountability In Pertumbukan Village, Wampu District,” Prosiding Universitas Dharmawangsa, Vol. 4, No 1, Pp. 387-398, 2024.
L. Von Rueden Et Al., “Informed Machine Learning - A Taxonomy And Survey Of Integrating Prior Knowledge Into Learning Systems,” Ieee Trans. Knowl. Data Eng., Vol. 35, No. 1, Pp. 614–633, Jan. 2023, Doi: 10.1109/Tkde.2021.3079836.
A. Putera Utama Siahaan, N. Azizah Harahap, R. Yuni Simanullang, And P. Wanny, “Analysis Of Inpatient Data Using Cluster Analysis On Simulation Dataset,” Bulletin Of Information Technology (Bit), Vol. 6, No. 1, Pp. 33–39, 2025, Doi: 10.47065/Bit.V5i2.1830.
M. Iqbal And S. Efendi, “Data-Driven Approach For Credit Risk Analysis Using C4.5 Algorithm,” Comtech: Computer, Mathematics And Engineering Applications, Vol. 14, No. 1, Pp. 11–20, May 2023, Doi: 10.21512/Comtech.V14i1.8243.
Bila bermanfaat silahkan share artikel ini
Berikan Komentar Anda terhadap artikel Analysis of Food Distribution Efficiency Clustering Based on Cost and Delivery Time Using the K-Means Algorithm
ARTICLE HISTORY
How to Cite
Issue
Section
Copyright (c) 2026 Muhammad Syahputra Novelan, Solly Aryza, Puspita Wanny
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under Creative Commons Attribution 4.0 International License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (Refer to The Effect of Open Access).
