Clustering Potato Seeds Using DBSCAN Algorithm in Optimizing Sales

##plugins.themes.academic_pro.article.main##

N P Dharshinni
Andre Putra Persada Tarigan

Abstract

Potato seed shop CV. ASAP sells various types of potato seeds which are quite trusted by potato farmers. The payment methods used so far are cash and credit payments, but the shop owner never has a balanced stock so that customer demand is always not in accordance with the stock of seeds, resulting in the shop is at a loss. Based on the problems experienced by potato farmers, it is necessary to group potato seed sales data using the DBSCAN clustering algorithm. The DBSCAN algorithm groups sales data into several cluster groups based on epsilon values and minimal points, besides that this algorithm can detect noise in data grouping so that the grouping results obtained are better than other algorithms. The purpose of this study was to apply the DBSCAN algorithm to potato seedling sales data to obtain results in knowing the types of potato seeds that were most purchased by customers from potato seed sales with cash payment methods and credit payments. The results of the application of the DBSCAN algorithm found that the most purchased by customers using the cash payment method were the type of spread seeds, class D potato seeds, class A potato seeds, and class C potato seeds with the test value of epsilon 1,2 and Minimum points 1 while the sale of seeds Potatoes, the most purchased by a customer using credit payment method are the type of potato produced by class C and potato spread seeds with the test value of Epsilon 2 and Minimum points 1.

##plugins.themes.academic_pro.article.details##

Author Biographies

N P Dharshinni, Universitas Prima Indonesia

Department of Informatics Engineering, Faculty of Technology and Computer Science, Universitas Prima Indonesia, Medan, Indonesia

Andre Putra Persada Tarigan, Universitas Prima Indonesia

Department of Informatics Engineering, Faculty of Technology and Computer Science, Universitas Prima Indonesia, Medan, Indonesia

How to Cite
N P Dharshinni, & Tarigan, A. P. P. (2021). Clustering Potato Seeds Using DBSCAN Algorithm in Optimizing Sales. INFOKUM, 10(1), 398-405. Retrieved from http://infor.seaninstitute.org/index.php/infokum/article/view/324

References

[1] Metisen. BM., & Sari. HL., "Analisis Clustering Menggunakan Metode K-Means Dalam Pengelompokkan Penjualan Produk Pada Swalayan Fadhila", Jurnal Media Infotama, 11(2), 110–8, 2015.
[2] Bu’ulolo. E., & Purba. B., "Algoritma Clustering Untuk Membentuk Cluster Zona Penyebaran Covid-19",Jurnal Teknologi Informasi dan Komunikasi, 12(1), 59–67, 2021.
[3] Izhari. F., "Analisis Algoritma Dbscan Dalam Menentukan Parameter Epsilon Pada Clustering Data Numerik", (Studi kasus menentukan parameter Epsilon pada Clustering), Seminar Nasional Teknologi Komputer Sains, 156–8, 2020.
[4] Hidayati. QR., & Surono. S., "Implementasi Algoritma Spectral Clustering
Untuk Analisis Sentimen", Jurnal Ilmiah Pendidikan Matematika, 9(1), 27, 2021.
[5] Zheng. H., et al., "Clustering algorithm based on characteristics of density distribution", Proc - 2nd IEEE Int Conf Adv Comput Control ICACC 2010, 2, 431–5, 2010.
[6] Huang. TQ., et al.,"Reckon the parameter of DBSCAN for multi-density data sets with constraints", Int Conf Artif Intell Comput Intell AICI 2009, 4(1996), 375–9, 2009.
[7] Ashari. BS., Otniel. SC., Rianto., "Perbandingan Kinerja K-Means Dengan DSCAN Untuk Metode Clustering Data Penjualan Online Retail". Jurnal Siliwangi [Internet], 5(2), 72–7, 2019.
[8] Devi. AS., et al., "Implementasi Metode Clustering DBSCAN pada Proses Pengambilan Keputusan", Lontar Komputer JITI, 6(3), 185, 2015.
[9] Indriyani. F., & Irfiani. E., "Clustering Data Penjualan pada Toko Perlengkapan Outdoor Menggunakan Metode K-Means" JUITA Jurnal Informatika, 7(2), 109, 2019.
[10] Jain. D., Singh. M., Sharma. AK., "Performance enhancement of DBSCAN density based clustering algorithm in data mining" Int Conf Energy, Commun Data Anal Soft Comput ICECDS 2017, 1559–64, 2018.
[11] Sari. BN., & Primajaya. A., "Penerapan Clustering Dbscan Untuk Pertanian Padi Di Kabupaten Karawang", JIKO Jurnal Informatika dan Komputer, 4(1), 28–34, 2019
[12] Jebari. S., Smiti. A., Louati. A., "AF-DBSCAN: An unsupervised Automatic Fuzzy Clustering method based on DBSCAN approach", IWOBI 2019 - IEEE Int Work Conf Bioinspired Intell Proc, 117–22, 2019.
[13] Zhu. Q., Tang. X., Liu. Z., "Revised DBSCAN Clustering Algorithm Based on Dual Grid. Proc 32nd Chinese Control Decis Conf CCDC", 3461–6, 2020.
[14] Safitri. D., Wuryandari. T., Rahmawati. R., "Metode Dbscan Untuk Pengelompokan Kabupaten / Kota Di Provinsi Jawa Tengah. Statistika", STATISTIKA, 5(1), 8–13, 2017.
[15] Isnarwaty. DP., & Irhamah., "Text clustering pada akun twitter layanan ekspedisi JNE , J&T, dan Pos Indonesia menggunakan metode Density-Based Spatial Clustering of Applications with Noise ( DBSCAN )", Jurnal Sains dan Seni, 8(2), 2–9, 2019
[16] M. MP., et al., "Comparison of DBSCAN and K-Means clustering for grouping the village status in Central Java 2020", Jurnal Matematika Statistika dan Komputasi, 17(Vol. 17 No.3 (2021), 394–404, 2021.
[17] Sharma. S., Sharma. AK., Soni. D., "Enhancing DBSCAN algorithm for data mining", International Conf Energy, Commun Data Anal Soft Comput ICECDS, 1634–8, 2018.
[18] Li. X., Zhang. P., Zhu. G., "DBSCan clustering algorithms for non-uniform density data and its application in urban rail passenger aggregation distribution", Energies, 12(19), 1–22, 2019.
[19] Lazarevic. A., et al., "Clustering-regression-ordering steps for knowledge discovery in spatial databases" Proc Int Jt Conf Neural Networks, 4, 2530–4, 2019.
[20] Li. H., Zhang. A., Pei. X., "Research on Thermal Error of CNC Machine Tool Based on DBSCAN Clustering and BP Neural Network Algorithm", Proc 2019 IEEE Int Conf Intell Appl Syst Eng ICIASE, (100), 294–6, 2019.
21. Mulyono D, Syah MJA, Sayekti AL, Hilman Y. Kelas Benih Kentang (Solanum tuberosum L.) Berdasarkan Pertumbuhan, Produksi, dan Mutu Produk. J Hortik. 2018;27(2):209.
22. Baharuddin MM, Azis H, Hasanuddin T. Analisis Performa Metode K-Nearest Neighbor Untuk Identifikasi Jenis Kaca. Ilk J Ilm. 2019;11(3):269–74.
23. Pingping H, Yu X, Yan Z, Kui L. Equivalent model of wind farm based on DBSCAN. 2017 IEEE Innov Smart Grid Technol - Asia Smart Grid Smart Community, ISGT-Asia 2017. 2018;1–6.
24. Yu H, Zhang W. Stabilizing System. 2013;1297–301.