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PENERAPAN ALGORITMA K-MEDOIDS CLUSETERING UNTUK REKOMENDASI MENU DAN STRATEGI STOK BAHAN BAKU

Authors

  • Rinci Kembang Hapsari Institut Teknologi Adhi Tama Surabaya
  • M Safi Anwar Anas Institut Teknologi Adhi Tama Surabaya
  • Reza Zulkifli Ferdiansyah Institut Teknologi Adhi Tama Surabaya
  • Hanif Prasetyo Institut Teknologi Adhi Tama Surabaya
  • Mochamad Muhajir Institut Teknologi Adhi Tama Surabaya

DOI:

https://doi.org/10.35316/jimi.v9i1.30-38

Keywords:

Clusterization,, Coffe Shop, K-Medoids, Sales Data

Abstract

A coffee shop is a place that provides coffee and other hot drinks. Many customers, especially young people, visit coffee shops to enjoy food and drinks while relaxing as the growth of coffee shops continues to increase due to modern places and affordable food and drink prices. In competing for coffee shop services to customers, many coffee shops have developed minimum variants/types of food sold in coffee shops. The large number of food and drink variants means that customers need a long time to choose a menu, making it difficult for the purchasing department to provide raw material stock. So, this research aims to group the menus in coffee shops into 2 clusters, namely the menu cluster that sells well and the menu cluster that sells quite well. In this research, sales data from coffee shop menus were grouped by implementing the k-medoids algorithm. Every member of Cluster 1 and every member of Cluster 2 were grouped. From the tests that have been carried out it can help customers and coffee shop entrepreneurs to support purchasing strategies. By looking at the cluster 1 menu it can be used as menu recommendation information so that it is easier for consumers to choose the drink and food menu at the coffee shop. It can also be used as a basis for purchasing raw food and beverage materials

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Author Biographies

M Safi Anwar Anas, Institut Teknologi Adhi Tama Surabaya

Teknik Informatika, Fakultas Teknik Elektro dan Teknologi Informasi

Mochamad Muhajir, Institut Teknologi Adhi Tama Surabaya

Teknik Informatika, Fakultas Teknik Elektro dan Teknologi Informasi

References

[1] W. Widyawati, W. L. Y. Saptomo, and Y. R. W. Utami, “Penerapan Agglomerative Hierarchical Clustering Untuk Segmentasi Pelanggan,” J. Ilm. SINUS, vol. 18, no. 1, p. 75, 2020, doi: 10.30646/sinus.v18i1.448.
[2] W. M. Baihaqi, K. Indartono, and S. Banat, “Penerapan Teknik Clustering Sebagai Strategi Pemasaran pada Penjualan Buku Di Tokopedia dan Shopee,” Paradig. - J. Komput. dan Inform., vol. 21, no. 2, pp. 243–248, 2019, doi: 10.31294/p.v21i2.6149.
[3] S. Sindi, W. R. O. Ningse, I. A. Sihombing, F. I. R.H.Zer, and D. Hartama, “Analisis Algoritma K-Medoids Clustering Dalam Pengelompokan Penyebaran Covid-19 Di Indonesia,” J. Teknol. Inf., vol. 4, no. 1, pp. 166–173, 2020, doi: 10.36294/jurti.v4i1.1296.
[4] B. Wira, A. E. Budianto, and A. S. Wiguna, “Implementasi Metode K-Medoids Clustering Untuk Mengetahui Pola Pemilihan Program Studi Mahasiwa Baru Tahun 2018 Di Universitas Kanjuruhan Malang,” RAINSTEK J. Terap. Sains Teknol., vol. 1, no. 3, pp. 53–68, 2019, doi: 10.21067/jtst.v1i3.3046.
[5] A. A. D. Sulistyawati and M. Sadikin, “Penerapan Algoritma K-Medoids Untuk Menentukan Segmentasi Pelanggan,” Sistemasi, vol. 10, no. 3, p. 516, 2021, doi: 10.32520/stmsi.v10i3.1332.
[6] D. Marlina, N. Lina, A. Fernando, and A. Ramadhan, “Implementasi Algoritma K-Medoids dan K-Means untuk Pengelompokkan Wilayah Sebaran Cacat pada Anak,” J. CoreIT J. Has. Penelit. Ilmu Komput. dan Teknol. Inf., vol. 4, no. 2, p. 64, 2018, doi: 10.24014/coreit.v4i2.4498.
[7] N. Pulungan, S. Suhada, and D. Suhendro, “Penerapan Algoritma K-Medoids Untuk Mengelompokkan Penduduk 15 Tahun Keatas Menurut Lapangan Pekerjaan Utama,” KOMIK (Konferensi Nas. Teknol. Inf. dan Komputer), vol. 3, no. 1, 2019, doi: 10.30865/komik.v3i1.1609.
[8] R. K. Hapsari, E. Purwanti, W. Widyanto, R. Gunawan, F. Nurlaily, and A. H. Salim, “Optimization Based Random Forest Algorithm Modification for Detecting Monkeypox Disease,” in 2023 Sixth International Conference on Vocational Education and Electrical Engineering (ICVEE), IEEE, 2023, pp. 340–346. doi: 10.1109/icvee59738.2023.10348223.
[9] I. Khan, A. Capozzoli, S. P. Corgnati, and T. Cerquitelli, “Fault detection analysis of building energy consumption using data mining techniques,” Energy Procedia, vol. 42, no. April 2014, pp. 557–566, 2013, doi: 10.1016/j.egypro.2013.11.057.
[10] C. Fan, M. Chen, X. Wang, J. Wang, and B. Huang, “A Review on Data Preprocessing Techniques Toward Efficient and Reliable Knowledge Discovery From Building Operational Data,” Front. Energy Res., vol. 9, no. March, pp. 1–17, 2021, doi: 10.3389/fenrg.2021.652801.
[11] R. K. Hapsari, A. H. Salim, B. D. Meilani, T. Indriyani, and A. Rachman, “Comparison of the Normalization Method of Data in Classifying Brain Tumors with the k-NN Algorithm,” in The 2nd International Conference on Neural Networks and Machine Learning, Atlantis Press International BV, 2023, pp. 21–29. doi: 10.2991/978-94-6463-174-6_3.
[12] D. D. C. Nugraha, Z. Naimah, M. Fahmi, and N. Setiani, “Klasterisasi Judul Buku dengan Menggunakan Metode K-Means,” Semin. Nas. Apl. Teknol. Inf. Yogyakarta, vol. 21, no. 1, pp. 1907–5022, 2014.
[13] B. Riyanto, “Penerapan Algoritma K-Medoids Clustering Untuk Pengelompokkan Penyebaran Diare Di Kota Medan (Studi Kasus: Kantor Dinas Kesehatan Kota Medan),” KOMIK (Konferensi Nas. Teknol. Inf. dan Komputer), vol. 3, no. 1, pp. 562–568, 2019, doi: 10.30865/komik.v3i1.1659.
[14] S. Akter, F. Reza, and M. Ahmed, “Convergence of Blockchain, k-medoids and homomorphic encryption for privacy preserving biomedical data classification,” Internet Things Cyber-Physical Syst., vol. 2, no. March, pp. 99–110, 2022, doi: 10.1016/j.iotcps.2022.05.006.

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Published

14-06-2024

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How to Cite

Hapsari, R. K., Anas, M. S. A., Ferdiansyah, R. Z., Prasetyo, H., & Muhajir, M. (2024). PENERAPAN ALGORITMA K-MEDOIDS CLUSETERING UNTUK REKOMENDASI MENU DAN STRATEGI STOK BAHAN BAKU. Jurnal Ilmiah Informatika, 9(1), 30–38. https://doi.org/10.35316/jimi.v9i1.30-38

Issue

Vol. 9 No. 1 (2024): Jurnal Ilmiah Informatika

Section

Articles

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