Implementation of the K-Means Method in Grouping Merchandise Locations at the Market Service

Authors

  • syawaludin pohan STIKOM Tunas Bangsa

DOI:

https://doi.org/10.53842/jaiea.v1i2.74

Keywords:

Location, Datamining, K-Means Algorithm, Interests

Abstract

One of the strategies to increase sales in traditional markets is the strategy of placing the selling location. This is done so that the products marketed are in accordance with the type so that consumers will feel comfortable with the ease of shopping. In this study, observations were made on the traditional market of Horas market in the market service area of ​​Pematangsiantar City. At this time the arrangement of selling locations has not been well organized so that there is little interest in the community to shop. This of course will affect the economic turnover of traders. These problems still occur today and there is no solution because market managers do not have a model that can be simulated. One of the computer science approaches to this problem is the K-Means algorithm data mining so that it is hoped that this research can help the market department in classifying merchandise locations in order to attract people's interest to shop at traditional markets so that there is an increase in the community's economy

References

B. Baldassari, “Mining Software Engineering Datafor Useful Knowledge,” 2014.

R. W. Nasution, I. O. Kirana, I. Gunawan, and I. P. Sari, “Penerapan Data Mining Untuk Pengelompokan Minat Konsumen Terhadap Pengguna Jasa Pengiriman Pada PT . Jalur Nugraha Ekakurir ( JNE ) Pematangsiantar,” vol. 1, no. 4, pp. 274–281, 2021.

F. Gullo, “From Patterns in Data to Knowledge Discovery: What Data Mining Can Do,” Phys. Procedia, vol. 62, pp. 18–22, Dec. 2015, doi: 10.1016/j.phpro.2015.02.005.

U. Ependi and A. Putra, “Solusi Prediksi Persediaan Barang dengan Menggunakan Algoritma Apriori (Studi Kasus: Regional Part Depo Auto 2000 Palembang),” J. Edukasi dan Penelit. Inform., vol. 5, p. 139, Aug. 2019, doi: 10.26418/jp.v5i2.32648.

A. E. Lubis and P. M. Hasugian, “Implementation Of Data Mining On Suzuki Motorcycle Sales In Gemilang Motor Prosperous With Apriori Algorithm Method,” J. Comput. Networks, Archit. High Perform. Comput., vol. 2, no. 1, pp. 23–29, 2020, doi: 10.47709/cnapc.v2i1.353.

G. Ontario, “E-COMMERCE : PURCHASING Why E-Commerce for Small Businesses ?,” E-bus. Toolkit, p. 1, 2013.

S. Budiman, D. Safitri, and D. Ispriyanti, “Perbandingan Metode K-Means Dan Metode Dbscan Pada Pengelompokan Rumah Kost Mahasiswa Di Kelurahan Tembalang Semarang,” None, vol. 5, no. 4, pp. 757–762, 2016.

A. Ali Hussein and A. Oluwaseun, “Data Mining Application Using Clustering Techniques (K-Means Algorithm) In The Analysis Of Student’s Result,” pp. 2458–2925, May 2019.

M. Omran, A. Engelbrecht, and A. Salman, “An overview of clustering methods,” Intell. Data Anal., vol. 11, pp. 583–605, Nov. 2007, doi: 10.3233/IDA-2007-11602.

O. Abu Abbas, “Comparisons Between Data Clustering Algorithms,” Int. Arab J. Inf. Technol., vol. 5, pp. 320–325, Jul. 2008.

R. Xu and D. Wunsch, “Survey of Clustering Algorithms,” Neural Networks, IEEE Trans., vol. 16, pp. 645–678, Jun. 2005, doi: 10.1109/TNN.2005.845141.

N. Wakhidah, “Clustering Menggunakan K-Means Algorithm,” J. Transform., vol. 8, no. 1, p. 33, 2010, doi: 10.26623/transformatika.v8i1.45.

A. M. H. Pardede et al., “Implementation of Data Mining to Classify the Consumer’s Complaints of Electricity Usage Based on Consumer’s Locations Using Clustering Method,” 2019, doi: 10.1088/1742-6596/1363/1/012079.

H. Alashwal, M. El Halaby, J. J. Crouse, A. Abdalla, and A. A. Moustafa, “The Application of Unsupervised Clustering Methods to Alzheimer’s Disease,” Front. Comput. Neurosci., vol. 13, p. 31, May 2019, doi: 10.3389/fncom.2019.00031.

A. Fahim, A.-B. M.Salem, F. Torkey, and M. Ramadan, “Efficient enhanced k-means clustering algorithm,” J. Zhejiang Univ. Sci. A, vol. 7, pp. 1626–1633, Jan. 2006, doi: 10.1631/jzus.2006.A1626.

A. Roni and R. Adrian, “Penerapan Metode K-Means Untuk Clustering Mahasiswa Berdasarkan Nilai Akademik Dengan Weka Interface Studi Kasus Pada Jurusan Teknik Informatika UMM Magelang (Implementation Method for K-Means Clustering Based Student Value with Weka Interface a Case Study of Department of Information UMM Magelang),” pp. 76–82, May 2015.

W. Dhuhita, “Clustering Menggunakan Metode K-Mean Untuk Menentukan Status Gizi Balita,” J. Inform. Darmajaya, vol. 15, no. 2, pp. 160–174, 2015.

Downloads

Published

2022-02-09

How to Cite

pohan, syawaludin. (2022). Implementation of the K-Means Method in Grouping Merchandise Locations at the Market Service. Journal of Artificial Intelligence and Engineering Applications (JAIEA), 1(2), 102–107. https://doi.org/10.53842/jaiea.v1i2.74