Strategic insights from clustering analysis of essential oil sales in UMKM: A comprehensive study on product types, sizes, couriers, and distribution across Indonesian Provinces
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Published:
Feb 28, 2024
Keywords:
Clustering, Courier, Essential oil, Principal Component Analysis, Silhouette Coefficient
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Decision Support System
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Abstract
This study explores the sales transactions of a Micro, Small, and Medium Enterprise (UMKM) that sells over 40 types of essential oils, totaling 2305 items sold in 2023. The products, packaged in small bottles (10-50 ml), were distributed to almost every province in Indonesia. The main objective is to cluster the data based on variables such as oil type, bottle size, courier company, and destination province. The elbow method determined an optimal number of clusters (k=4), and the Silhouette Coefficient validated the effectiveness of the clustering (0.7614). To simplify the complex clustering results, Principal Component Analysis (PCA) was used for visualization, providing a clear representation of 5 variables and 4 clusters. This study offers valuable insights for informed decision-making in UMKM's service enhancement and development
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Faizah, N. M., Erawati, D., Shirlyani, S., Fabrianto, L. . and Prihandayani, T. W. . (2024) “Strategic insights from clustering analysis of essential oil sales in UMKM: A comprehensive study on product types, sizes, couriers, and distribution across Indonesian Provinces”, Jurnal Mantik, 7(4), pp. 3316-3325. doi: 10.35335/mantik.v7i4.4694.
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SAPUTRA, D. M., SAPUTRA, D., & OSWARI, L. D. (2020). Effect of Distance Metrics in Determining K-Value in K-Means Clustering Using Elbow and Silhouette Method. 172, 341–346. https://doi.org/10.2991/AISR.K.200424.051
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Surohman, S., Fabrianto, L., Riza, F., & Faizah, N. M. (2021). Korelasi Antara Profil dan Nilai Akademis Siswa dengan Menggunakan Algoritma K-Means. Jurnal Teknologi Informasi Dan Ilmu Komputer, 8(4), 845. https://doi.org/10.25126/jtiik.2021843034
Suwaryo, N., Rahman, A., Marini, D., Atmaja, U., & Basri, A. (2023). Klasterisasi Stok Produk Retail Untuk Menetukan Pergerakan Kebutuhan Konsumen Dengan Algoritma K-Means. Bulletin of Information Technology (BIT), 4(3), 306–312. https://doi.org/10.47065/BIT.V4I3.736
Teichgraeber, H., & Brandt, A. R. (2019). Clustering methods to find representative periods for the optimization of energy systems: An initial framework and comparison. Applied Energy, 239, 1283–1293. https://doi.org/10.1016/J.APENERGY.2019.02.012
Teknik, J., Medicom, I. C. I. T., Ramadani, S., & Hidayat, S. (2023). Application of data mining on inventory grouping using clustering method. 15(5), 228–239.
Tony Cai, T., Han, X., & Pan, G. (2020). Limiting laws for divergent spiked eigenvalues and largest nonspiked eigenvalue of sample covariance matrices. Https://Doi.Org/10.1214/18-AOS1798, 48(3), 1255–1280. https://doi.org/10.1214/18-AOS1798
Umargono, E., Suseno, J. E., & Gunawan, S. . V. (2020). K-Means Clustering Optimization Using the Elbow Method and Early Centroid Determination Based on Mean and Median Formula. 121–129. https://doi.org/10.2991/ASSEHR.K.201010.019
Abid, A., Zhang, M. J., Bagaria, V. K., & Zou, J. (n.d.). Exploring patterns enriched in a dataset with contrastive principal component analysis. https://doi.org/10.1038/s41467-018-04608-8
Bandyopadhyay, S., Thakur, S. S., & Mandal, J. K. (2021). Product recommendation for e-commerce business by applying principal component analysis (PCA) and K-means clustering: benefit for the society. Innovations in Systems and Software Engineering, 17(1), 45–52. https://doi.org/10.1007/S11334-020-00372-5/METRICS
Björklund, M. (2019). Be careful with your principal components. Evolution, 73(10), 2151–2158. https://doi.org/10.1111/EVO.13835
Breiding, P., Sottile, • F, Woodcock, • J, Breiding, B. P., Sottile, F., & Woodcock, J. (2022). Euclidean Distance Degree and Mixed Volume. Foundations of Computational Mathematics, 22, 1743–1765. https://doi.org/10.1007/s10208-021-09534-8
Geladi, P., & Linderholm, J. (2020). 2.03 - Principal Component Analysis. Comprehensive Chemometrics: Chemical and Biochemical Data Analysis, Second Edition: Four Volume Set, 2, 17–37. https://doi.org/10.1016/B978-0-12-409547-2.14892-9
Gewers, F. L., Ferreira, G. R., De Arruda, H. F., Comin, C. H., Amancio, D. R., Da, L., & Costa, F. (2021). Principal Component Analysis: A Natural Approach to Data Exploration. https://doi.org/10.1145/3447755
Habib Romadhon, C., & Tanti Kustiari, dan. (2020). PENGEMBANGAN USAHA MINYAK ATSIRI KABUPATEN JEMBER DENGAN METODE DECISION SUPPORT SYSTEM (DSS). Prosiding Seminar Nasional Terapan Riset Inovatif (SENTRINOV), 6(2), 122–130. https://proceeding.isas.or.id/index.php/sentrinov/article/view/430
imron, M., Hasanah, U., & Humaidi, B. (2020). Analysis of Data Mining Using K-Means Clustering Algorithm for Product Grouping. International Journal of Informatics and Information Systems, 3(1), 12–22. http://ijiis.org/index.php/IJIIS/article/view/3
Mantik, J., Fabrianto, L., Prasetyo, J. H., Faizah, N. M., & Solichatun, S. (2023). Inventory management for essential oil UMKM: enhancing business performance with data mining. Jurnal Mantik, 7(2), 702–711. https://doi.org/10.35335/MANTIK.V7I2.3909
Mehta, H., Kanani, P., & Lande, P. (2019). Google Maps. Google Maps Article in International Journal of Computer Applications, 178(8), 975–8887. https://doi.org/10.5120/ijca2019918791
Mohammed, B., Hasan, S., & Mohsin Abdulazeez, A. (2021). A Review of Principal Component Analysis Algorithm for Dimensionality Reduction. Journal of Soft Computing and Data Mining, 2(1), 20–30. https://doi.org/10.30880/jscdm.2021.02.01.003
Nainggolan, R., Perangin-Angin, R., Simarmata, E., & Tarigan, A. F. (2019). Improved the Performance of the K-Means Cluster Using the Sum of Squared Error (SSE) optimized by using the Elbow Method. Journal of Physics: Conference Series, 1361(1). https://doi.org/10.1088/1742-6596/1361/1/012015
Practical Guide To Principal Component Methods in R: PCA, M(CA), FAMD, MFA ... - Alboukadel KASSAMBARA - Google Books. (n.d.). Retrieved January 4, 2024, from https://books.google.co.id/books?hl=en&lr=&id=eFEyDwAAQBAJ&oi=fnd&pg=PR5&dq=Practical+Guide+to+Principal+Component+Methods+in+R&ots=reV_kNjDPB&sig=jmZmoxWflxkjURdEgZ6S6bLp-OM&redir_esc=y#v=onepage&q=Practical Guide to Principal Component Methods in R&f=false
Pratama, R. A. R. J., Ningtiyas, A. F., Mauludin, H., Larosa, M. C., & Thaariq, K. A. (2022). Meningkatkan Potensi Desa Pagerwangi Melalui Produktivitas dengan Pengembangan UMKM sebagai Wujud Ekonomi Kreatif Desa. Jurnal Manajemen Dan Bisnis Performa, 19(2), 92–98. https://doi.org/10.29313/performa.v19i2.10703
Principal Component Analysis PROF XIAOHUI XIE SPRING 2019 CS 273P Machine Learning and Data Mining. (n.d.).
Sahoo, K., Samal, A. K., Pramanik, J., & Pani, S. K. (2019). Exploratory data analysis using python. International Journal of Innovative Technology and Exploring Engineering, 8(12), 4727–4735. https://doi.org/10.35940/ijitee.L3591.1081219
SAPUTRA, D. M., SAPUTRA, D., & OSWARI, L. D. (2020). Effect of Distance Metrics in Determining K-Value in K-Means Clustering Using Elbow and Silhouette Method. 172, 341–346. https://doi.org/10.2991/AISR.K.200424.051
Shah, C. (n.d.). A Hands-On Introduction to Data Science Preface About the Author A ckn owledgm ents.
Shahapure, K. R., & Nicholas, C. (2020). Cluster quality analysis using silhouette score. Proceedings - 2020 IEEE 7th International Conference on Data Science and Advanced Analytics, DSAA 2020, 747–748. https://doi.org/10.1109/DSAA49011.2020.00096
Surohman, S., Fabrianto, L., Riza, F., & Faizah, N. M. (2021). Korelasi Antara Profil dan Nilai Akademis Siswa dengan Menggunakan Algoritma K-Means. Jurnal Teknologi Informasi Dan Ilmu Komputer, 8(4), 845. https://doi.org/10.25126/jtiik.2021843034
Suwaryo, N., Rahman, A., Marini, D., Atmaja, U., & Basri, A. (2023). Klasterisasi Stok Produk Retail Untuk Menetukan Pergerakan Kebutuhan Konsumen Dengan Algoritma K-Means. Bulletin of Information Technology (BIT), 4(3), 306–312. https://doi.org/10.47065/BIT.V4I3.736
Teichgraeber, H., & Brandt, A. R. (2019). Clustering methods to find representative periods for the optimization of energy systems: An initial framework and comparison. Applied Energy, 239, 1283–1293. https://doi.org/10.1016/J.APENERGY.2019.02.012
Teknik, J., Medicom, I. C. I. T., Ramadani, S., & Hidayat, S. (2023). Application of data mining on inventory grouping using clustering method. 15(5), 228–239.
Tony Cai, T., Han, X., & Pan, G. (2020). Limiting laws for divergent spiked eigenvalues and largest nonspiked eigenvalue of sample covariance matrices. Https://Doi.Org/10.1214/18-AOS1798, 48(3), 1255–1280. https://doi.org/10.1214/18-AOS1798
Umargono, E., Suseno, J. E., & Gunawan, S. . V. (2020). K-Means Clustering Optimization Using the Elbow Method and Early Centroid Determination Based on Mean and Median Formula. 121–129. https://doi.org/10.2991/ASSEHR.K.201010.019
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