Elbow and Silhouette Methods for K Value Analysis of Ticket Sales Grouping on K-Means

Adityo Putro Wicaksono, Stephanus Widjaja, Mohamad Fajarianditya Nugroho, Christina Priscilla Putri

Abstract


Increasingly fierce business competition forces people to develop businesses to compete in selling by looking for leads to increase revenue from business sales. The development of the digital era affects the use of the internet for the process of searching for information and online purchases, such as purchasing tickets and digital products. Film is one of the industries in the entertainment world that brings a new atmosphere for people to watch films that can be watched anywhere and also online and offline. Ticket sales data that is being processed is clustered using K-Means. The clustering process and evaluation to determine the best K value in the clustering process uses the Elbow and Silhouette Score Methods. Grouping is obtained if the grouping results are not significantly different when the grouping process is carried out on the "ticket_sold" and "ticket_price" attributes. Based on the results of the Elbow Method, the result is that the value of k = 3 to the value of k = 5 is the optimal value for carrying out the data grouping process for the case of selling movie tickets. While the results of a good cluster distribution are at a value of k = 5 with a value of 0.60 using the Silhouette Score..

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N. Astuti, J. N. Utamajaya, and A. Pratama, “Penerapan Data Mining Pada Penjualan Produk Digital Konter Leppangeng Cell Menggunakan Metode K-Means Clustering,” J. Ris. Komput., vol. 9, no. 3, pp. 754–760, 2022, doi: 10.30865/jurikom.v9i3.4351.

Y. D. Yulianti, R. Nurcahyo, and M. Dachyar, “Strategi Penentuan Harga dan Penjualan Hotel Pada Online Travel Agency ( OTA ),” Semin. dan Konf. Nas. IDEC, no. 1, pp. 1–11, 2019.

A. Batubara and R. Hidayat, “Pengaruh Penetapan Harga dan Promosi terhadap Tingkat Penjualan Tiket pada PSA Mihin Lanka Airlines,” Ilman, vol. 4, no. 1, pp. 33–36, 2016.

M. A. Syakur, B. K. Khotimah, E. M. S. Rochman, and B. D. Satoto, “Integration K-Means Clustering Method and Elbow Method for Identification of the Best Customer Profile Cluster,” IOP Conf. Ser. Mater. Sci. Eng., vol. 336, no. 1, pp. 1–6, 2018, doi: 10.1088/1757-899X/336/1/012017.

A. Zakir, M. S. Bayudi, and N. Nurjamiah, “Sistem Pendukung Keputusan Evaluasi Loket Pemesanan Tiket Po Medan Jaya Menggunakan Metode K-Means,” J. Teknol. …, vol. 3, no. 48, pp. 485–488, 2020, [Online]. Available: http://jurnal.unprimdn.ac.id/index.php/JUTIKOMP/article/view/1376.

R. Pormes and D. H. F. Manongga, “Pemanfaatan Metode Clustering untuk melihat pola penjualan dan perilaku pembelian konsumen, pada penjualan tiket pesawat PT. Garuda Indonesia, Cabang Batam,” J. Tek. Inform. dan Sist. Inf., vol. 3, no. 3, pp. 483–493, 2017, doi: 10.28932/jutisi.v3i3.663.

F. D. S. Alhamdani, A. A. Dianti, and Y. Azhar, “Segmentasi Pelanggan Berdasarkan Perilaku Penggunaan Kartu Kredit Menggunakan Metode K-Means Clustering,” JISKA (Jurnal Inform. Sunan Kalijaga), vol. 6, no. 2, pp. 70–77, 2021, doi: 10.14421/jiska.2021.6.2.70-77.

N. H. Harani, C. Prianto, and F. A. Nugraha, “Segmentasi Pelanggan Produk Digital Service Indihome Menggunakan Algoritma K-Means Berbasis Python,” J. Manaj. Inform., vol. 10, no. 2, pp. 133–146, 2020, doi: 10.34010/jamika.v10i2.2683.

M. Billah, M. A. Zartesya, and D. S. Prasvita, “Penerapan Collaborative Filtering, PCA, dan K-Means dalam Pembangunan Sistem Rekomendasi Film,” Semin. Nas. Mhs. Ilmu Komput. dan Apl., pp. 579–587, 2021.

M. Billah, M. A. Zartesya, and D. S. Prasvita, “Penerapan Collaborative Filtering, PCA dan K-Means dalam Pembangunan Sistem Rekomendasi Ongoing dan Upcoming Film Animasi Jepang,” Senamika, no. April, pp. 579–587, 2021, [Online]. Available: https://conference.upnvj.ac.id/index.php/senamika/article/view/1343%0Ahttps://conference.upnvj.ac.id/index.php/senamika/article/download/1343/1091.

N. N. F. R, D. S. Anggraeni, and U. Enri, “Pengelompokkan Data Kemiskinan Provinsi Jawa Barat Menggunakan Algoritma K-Means dengan Silhouette Coefficient,” J. Teknol. Inf. Komun., vol. 5, no. 2, pp. 29–35, 2022.

J. Ha, M. Kamber, and J. Pei, Data Mining: Concepts and Techniques, 3th ed. United States of America: Elsevier, 2011.

R. Llet, M. C. Ortiz, L. A. Sarabia, and M. S. Sánchez, “Selecting variables for k -means cluster analysis by using a genetic algorithm that optimises the silhouettes,” Elsevier, vol. 515, pp. 87–100, 2004, doi: 10.1016/j.aca.2003.12.020.

P. Chapman et al., “CRISP-DM 1.0 Step-by-step Data Mining Guide,” SPSS inc, vol. 78, pp. 1–78, 2000, [Online]. Available: https://www.semanticscholar.org/paper/CRISP-DM-1.0%3A-Step-by-step-data-mining-guide-Chapman-Clinton/54bad20bbc7938991bf34f86dde0babfbd2d5a72%0Ahttp://www.crisp-dm.org/CRISPWP-0800.pdf.




DOI: https://doi.org/10.32520/stmsi.v13i1.2889

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