Marketing Strategy UMKM Dengan CRISP-DM Clustering & Promotion Mix Menggunakan Metode K-Medoids
Article Sidebar
Main Article Content
Abstract
The development of MSMEs is something that has a very large influence on the economy in Indonesia. The local MSME market is even able to compete globally as shown by the large number of incoming requests from abroad. Even so, MSMEs in Indonesia have experienced fluctuations due to the economic crisis. The fluctuations that occurred showed a decrease in the number of MSMEs by 0.003%. The Executive Director of the Institute For Economics (Indef) said that the fluctuations that occurred in MSMEs resulted in unstable economic development in the second quarter. From this problem, the CRISP-DM Clustering process was carried out to process existing MSME data to determine the right promotion strategy in developing MSMEs based on the type of business undertaken, turnover and assets owned. This research was conducted using the K-Medoids algorithm by forming 3 clusters for data processing. The data processed were 71 MSME data throughout Solo Raya, where in cluster 1 there were 25 MSMEs which were dominated by types of businesses in the fashion sector with an average turnover and assets between IDR 1,000,000 - IDR 5,000,000. Cluster 2 consists of 39 MSMEs which are dominated by types of businesses in the culinary field with average turnover and assets between IDR 1,000,000 - IDR 5,000,000 and in cluster 3 as many as 7 MSMEs which are dominated by types of businesses in other fields ( excluding 6 other types of business) with an average asset of ≥ Rp. 30,000,000 and a turnover between Rp. 21,000,000 - Rp. 25,000,000.
Article Details
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
[2] Astuti, D. (2019). Penentuan Strategi Promosi Usaha Mikro Kecil Dan Menengah (UMKM) Menggunakan Metode CRISP-DM dengan Algoritma K-Means Clustering. Journal of Informatics, Information System, Software Engineering and Applications (INISTA), 1(2), 60–72. https://doi.org/10.20895/inista.v1i2.71
[3] Rak, T., & Żyła, R. (2022). Using Data Mining Techniques for Detecting Dependencies in the Outcoming Data of a Web-Based System. Applied Sciences, 12(12), 6115. https://doi.org/10.3390/app12126115
[4] Ashish B., Sumit G., Shreyas K & Suhasini P. (2018). Study of Data Mining Concept. International Journal of New Innovations in Engineering and Technology. Volume 9 Issue 1. ISSN : 2319-6319.
[5] Sinaga, K. P., & Yang, M.-S. (2020). Unsupervised K-Means Clustering Algorithm. IEEE Access, 8, 80716–80727. https://doi.org/10.1109/access.2020.2988796
[6] Dinata, R. K., Retno, S., & Hasdyna, N. (2021). Minimization of the Number of Iterations in K-Medoids Clustering with Purity Algorithm. Revue d’Intelligence Artificielle, 35(3), 193–199. https://doi.org/10.18280/ria.350302
[7] Atmaja, E. H. S. (2019). Implementation of k-Medoids Clustering Algorithm to Cluster Crime Patterns in Yogyakarta. International Journal of Applied Sciences and Smart Technologies, 1(1), 33–44. https://doi.org/10.24071/ijasst.v1i1.1859
[8] Wang, T., Li, Q., Bucci, D. J., Liang, Y., Chen, B., & Varshney, P. K. (2019). K-Medoids Clustering of Data Sequences With Composite Distributions. IEEE Transactions on Signal Processing, 67(8), 2093–2106. https://doi.org/10.1109/tsp.2019.2901370
[9] Yerpude, A., & Dubey, S. (2012). Colour image segmentation using K-medoids clustering. Int J Comput Technol Appl, 3(1), 152-4.
[10] Krishnaswamy, V., Singh, N., Sharma, M., Verma, N., & Verma, A. (2022). Application of CRISP-DM methodology for managing human-wildlife conflicts: an empirical case study in India. Journal of Environmental Planning and Management, 1–27. https://doi.org/10.1080/09640568.2022.2070460
[11] Gunawan, G. (2021). Data Mining Using Crisp-Dm Process Framework On Official Statistics: A Case Study Of East Java Province. Jurnal Ekonomi Dan Pembangunan, 29(2), 183–198. Https://Doi.Org/10.14203/Jep.29.2.2021.183-198
[12] Wang, T., Li, Q., Bucci, D. J., Liang, Y., Chen, B., & Varshney, P. K. (2019). K-Medoids Clustering of Data Sequences With Composite Distributions. IEEE Transactions on Signal Processing, 67(8), 2093–2106. https://doi.org/10.1109/tsp.2019.2901370
[13] Yuda Irawan. (2019). Penerapan Data Mining Untuk Evaluasi Data Penjualan Menggunakan Metode Clustering Dan Algoritma Hirarki Divisive Di Perusahaan Media World Pekanbaru. Jurnal Teknologi Informasi Universitas Lambung Mangkurat (Jtiulm), 4(1), 13–20. Https://Doi.Org/10.20527/Jtiulm.V4i1.34
[14] Rahman, F., Ridho, I. I., Muflih, M., Pratama, S., Raharjo, M. R., & Windarto, A. P. (2020). Application of Data Mining Technique using K-Medoids in the case of Export of Crude Petroleum Materials to the Destination Country. IOP Conference Series: Materials Science and Engineering, 835(1), 012058. https://doi.org/10.1088/1757-899x/835/1/012058
[15] Alasadi, S. A., & Bhaya, W. S. (2017). Review of data preprocessing techniques in data mining. Journal of Engineering and Applied Sciences, 12(16), 4102-4107.
[16] Rahm, E., & Do, H. H. (2000). Data cleaning: Problems and current approaches. IEEE Data Eng. Bull., 23(4), 3-13.
[17] Patil, R. Y., & Kulkarni, R. V. (2012). A review of data cleaning algorithms for data warehouse systems. International Journal of Computer Science and Information Technologies, 3(5), 5212-5214.
[18] Li, Z., & Liu, L. (2022). Project Cost Management Information Solution Based on Data Mining Technology. 2022 3rd Asia-Pacific Conference on Image Processing, Electronics and Computers. https://doi.org/10.1145/3544109.3544363
[19] Sinsomboonthong, S. (2021). Efficiency Comparison in Prediction of Normalization with Data Mining Classification. Advances in Science, Technology and Engineering Systems Journal, 6(4), 130–137. https://doi.org/10.25046/aj060415
[20] Hudaib, A., Khanafseh, M., & Surakhi, O. (2018). An Improved Version of K-medoid Algorithm using CRO. Modern Applied Science, 12(2), 116. https://doi.org/10.5539/mas.v12n2p116