Sistem Penunjang Keputusan Pemasaran Produk X Menggunakan Metode K-Means
DOI:
https://doi.org/10.29080/systemic.v6i1.936Keywords:
Marketing, Clustering, K-Means, Data MiningAbstract
The problem that is currently in store is the difficulty to find out which product x is currently in high demand or is most widely used by consumers so that inventory of product x can be met according to customer demand and does not occur out of stock. Therefore, in the research grouping with the K-Means method for marketing product x is one way to determine customer choices for product x consumed. In this study grouping data from questionnaires or questionnaires that are distributed in stores, then the data are grouped into 2 groups using one of the clustering algorithms, K-Means. The data used are data collected by 366 respondents of store customers. After the data is processed using one of the data mining methods, the K-Means algorithm, shows that cluster 1 is a type A consumer group with a percentage of 33%, while cluster 2 is a type B consumer group with a percentage of 67%.
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References
R. Andrean, S. Fendy, and A. Nugroho, “Klasterisasi Pengendalian Persediaan Aki Menggunakan Metode K-Means,” JOINTECS (Journal Inf. Technol. Comput. Sci., vol. 4, no. 1, pp. 5–12, 2019.
N. Sapkota, A. Alsadoon, P. W. C. Prasad, A. Elchouemi, and A. K. Singh, “Data Summarization Using Clustering and Classification: Spectral Clustering Combined with k-Means Using NFPH,” in Proceedings of the International Conference on Machine Learning, Big Data, Cloud and Parallel Computing: Trends, Prespectives and Prospects, COMITCon 2019, 2019, pp. 146–151.
R. Molaei and S. Alizadeh, “Checking and Analysing Customers Buying Behavior with Clustering Algorithm,” Palma J., vol. 16, no. 2, pp. 486–492, 2017.
R. A. Siregar, “Seleksi Penyerang Utama Menggunakan K-Means Clustering Dan Sistem Pendukung Keputusan Metode Topsis,” Technomedia J., vol. 2, no. 1, pp. 37–48, 2017.
A. Masruro, Kusrini, and E. T. Luthfi, “SISTEM PENUNJANG KEPUTUSAN PENENTUAN LOKASI WISATA MENGGUNAKAN K-MEANS CLUSTERING DAN TOPSIS,” J. Ilm. DASI, vol. 15, no. 04, pp. 1–5, 2014.
M. E. Corapcioglu and H. Ogul, “A medical decision support system proposal supported by genomic analysis,” in 2017 25th Signal Processing and Communications Applications Conference, SIU 2017, 2017.
R. M. Esteves, T. Hacker, and C. Rong, “Competitive K-means: A new accurate and distributed K-means algorithm for large datasets,” in Proceedings of the International Conference on Cloud Computing Technology and Science, CloudCom, 2013, vol. 1, pp. 17–24.
S. Sharma and A. Bhagat, “Data preprocessing algorithm for Web Structure Mining,” in Proceedings on 5th International Conference on Eco-Friendly Computing and Communication Systems, ICECCS 2016, 2017, pp. 94–98.
S. Fong, R. Wong, and A. V. Vasilakos, “Accelerated PSO Swarm Search Feature Selection for Data Stream Mining Big Data,” IEEE Trans. Serv. Comput., vol. 9, no. 1, pp. 33–45, Jan. 2016.
S. K. Dwivedi and B. Rawat, “A review paper on data preprocessing: A critical phase in web usage mining process,” in Proceedings of the 2015 International Conference on Green Computing and Internet of Things, ICGCIoT 2015, 2016, pp. 506–510.
D. Triyansyah and D. Fitrianah, “Analisis Data Mining Menggunakan Algoritma K-Means Clustering Untuk Menentukan Strategi Marketing,” J. Telekomun. dan Komput., vol. 8, no. 3, pp. 163–182, 2018.
M. Capo, A. Perez, and J. A. A. Lozano, “An efficient Split-Merge re-start for the K-means algorithm,” IEEE Trans. Knowl. Data Eng., pp. 1–1, 2020.
R. Primartha, BELAJAR MACHINE LEARNING TEORI DAN PRAKTIK. Informatika Bandung, 2018.
A. Nugroho and T. M. Fahrudin, “Analisis dan Pemetaan Jumlah Penumpang Kereta Api di Indonesia Menggunakan Metode Statistik Deskriptif dan K-means Clustering,” J. Mantik, vol. 3, no. 9, pp. 1–9, 2019.
S. Kapil, M. Chawla, and M. D. Ansari, “On K-means data clustering algorithm with genetic algorithm,” in 2016 4th International Conference on Parallel, Distributed and Grid Computing, PDGC 2016, 2016, pp. 202–206.
