Penerapan Data Mining Pengelompokan Menu Makanan dan Minuman Berdasarkan Tingkat Penjualan Menggunakan Metode K-Means

  • Genta Triyandana Universitas Singaperbangsa Karawang
  • Lala Aprianti Putri Universitas Singaperbangsa Karawang
  • Yuyun Umaidah Universitas Singaperbangsa Karawang
Keywords: Data Mining, Clustering, K-Means, Tingkat Penjualan

Abstract

Data mining can be used to find solutions in making sales decisions to increase sales. Sales data storage stores many sales transaction records, where each document provides products purchased by customers in each sales transaction. A problem began to arise with an excess stockpiling of materials. The number of fluctuating sales causes the stock of available materials to be unstable and can directly impact consumers. Mistakes in predicting sales caused the coffee shop to buy large quantities of material stock, which were not widely used or sold out, so the supply of these materials swelled in the warehouse. One way to be implemented is by applying data mining because there are ways and methods to meet needs, one of which is the need for extensive information, then the information that we can use to determine quality in determining a decision. Therefore, it is hoped that this research can help Dpom Coffee minimize material stock inventory management cases such as shortages and excesses and make policies to increase sales by grouping menus based on sales levels using the K-means algorithm. Based on the results of processing the sales dataset at Dpom Coffee, it produces 3 clusters, namely Cluster 1 with eight menus with low sales levels, cluster 2 with 40 menus with moderate sales levels, and cluster 3 with seven menus with high sales levels. The accuracy or performance of the k-means algorithm results in a Davies Bouldin index value of 0.457.

Downloads

Download data is not yet available.

References

D. Kharisma1 and N. Nurkomalasari, “Penerapan Strategi Bauran Pemasaran Di Umkm Katering Di Semarang Barat,” Gemawisata: Jurnal Ilmiah Pariwisata, vol. 17, no. 2, Art. no. 2, May 2021, Accessed: May 09, 2022. [Online]. Available: http://stiepari.greenfrog-ts.co.id/jurnal/index.php/JT/article/view/250

S. Nurajizah, “Analisa Transaksi Penjualan Obat menggunakan Algoritma Apriori | Nurajizah | INOVTEK Polbeng - Seri Informatika,” vol. 4, no. 1, pp. 35–44, 2019, doi: https://doi.org/10.35314/isi.v4i1.938.

Normah, I. Yulianti, D. Novianti, M. N. Winnarto, A. Zumarniansyah, and S. Linawati, “Comparison of Classification C4.5 Algorithms and Naïve Bayes Classifier in Determining Merchant Acceptance on Sponsorship Program,” J. Phys.: Conf. Ser., vol. 1641, no. 1, p. 012006, Nov. 2020, doi: 10.1088/1742-6596/1641/1/012006.

D. E. Kurniawan and A. Fatulloh, “Clustering of Social Conditions in Batam, Indonesia Using K-Means Algorithm and Geographic Information System,” International Journal of Earth Sciences and Engineering (IJEE), vol. 10, no. 5, pp. 1076–1080, 2017.

I. S. M. Negara, P. Purwono, and I. A. Ashari, “Analisa Cluster Data Transaksi Penjualan Minimarket Selama Pandemi Covid-19 dengan Algoritma K-means,” JOINTECS (Journal of Information Technology and Computer Science), vol. 6, no. 3, Art. no. 3, Sep. 2021, doi: 10.31328/jointecs.v6i3.2693.

R. Kesuma Dinata, H. Novriando, N. Hasdyna, and S. Retno, “Reduksi Atribut Menggunakan Information Gain untuk Optimasi Cluster Algoritma K-Means | Dinata | JEPIN (Jurnal Edukasi dan Penelitian Informatika).” https://jurnal.untan.ac.id/index.php/jepin/article/view/37606 (accessed May 09, 2022).

A. S. Mardani, “Strategi Marketing dalam Pengembangan UMKM Kopi Jetak Mentari Menurut Perspektif Syariah (Studi Kasus pada UMKM Kopi Jetak Mentari di Desa Kedungdowo),” skripsi, IAIN KUDUS, 2020. Accessed: May 09, 2022. [Online]. Available: http://repository.iainkudus.ac.id/4417/

H. Halma, “Analisis Penjualan Minuman Thai Tea Pada Toko IL.SHOP18 Di Sangatta:,” Madani Accounting And Management Journal, vol. 7, no. 2, Art. no. 2, Sep. 2021, doi: 10.51882/jamm.v7i2.40.

J. Han, J. Pei, and M. Kamber, Data mining: concepts and techniques. Elsevier, 2011.

A. Sumiah and N. Mirantika, “Perbandingan Metode K-Nearest Neighbor dan Naive Bayes untuk Rekomendasi Penentuan Mahasiswa Penerima Beasiswa pada Universitas Kuningan,” Buffer Informatika, vol. 6, no. 1, pp. 1–14, 2020.

A. A. Fajrin and A. Maulana, “Penerapan Data Mining Untuk Analisis Pola Pembelian Konsumen Dengan Algoritma Fp-Growth Pada Data Transaksi Penjualan Spare Part Motor,” Kumpulan Jurnal Ilmu Komputer (KLIK), vol. 5, no. 01, pp. 1–10, 2018.

P. Prasetyawan, I. Ahmad, R. I. Borman, Y. A. Pahlevi, and D. E. Kurniawan, “Classification of the Period Undergraduate Study Using Back-propagation Neural Network,” 2018, pp. 1–5.

E. Fammaldo and L. Hakim, “Penerapan Algoritma K-Means Clustering Untuk Pengelompokan Tingkat Kesejahteraan Keluarga Untuk Program Kartu Indonesia Pintar,” Jurnal Ilmiah Teknologi Infomasi Terapan, vol. 5, no. 1, pp. 23–31, 2018.

F. A. Bramasta and R. Halilintar, “Penerapan Data Mining Untuk Menentukan Strategi Penjualan Toko Sepatu,” 2021, vol. 5, no. 2, pp. 236–241.

M. N. V. Waworuntu and M. F. Amin, “Penerapan Metode K-Means Untuk Pemetaan Calon Penerima Jamkesda,” KLIK-Kumpulan Jurnal Ilmu Komputer, vol. 5, no. 2, pp. 190–200, 2018.

R. A. Farissa, R. Mayasari, and Y. Umaidah, “Perbandingan Algoritma K-Means dan K-Medoids Untuk Pengelompokkan Data Obat dengan Silhouette Coefficient di Puskesmas Karangsambung,” Journal of Applied Informatics and Computing, vol. 5, no. 2, Art. no. 2, Oct. 2021, doi: 10.30871/jaic.v5i1.3237.

M. Nishom, “Perbandingan Akurasi Euclidean Distance, Minkowski Distance, dan Manhattan Distance pada Algoritma K-Means Clustering berbasis Chi-Square,” Jurnal Informatika, vol. 4, no. 01, pp. 20–24, 2019.

F. Mahmuda, M. A. R. Sitorus, H. Widyastuti, and D. E. Kurniawan, “Clustering Profil Pengunjung Perpustakaan Menggunakan Algoritma K-Means,” Journal of Applied Informatics and Computing, vol. 1, no. 1, Art. no. 1, 2017, doi: 10.30871/jaic.v1i1.476.

I. F. Ashari, R. Banjarnahor, D. R. Farida, S. P. Aisyah, A. P. Dewi, and N. Humaya, “Application of Data Mining with the K-Means Clustering Method and Davies Bouldin Index for Grouping IMDB Movies,” Journal of Applied Informatics and Computing, vol. 6, no. 1, Art. no. 1, Jul. 2022, doi: 10.30871/jaic.v6i1.3485.

Y. P. Sari, A. Primajaya, and A. S. Y. Irawan, “Implementasi Algoritma K-Means untuk Clustering Penyebaran Tuberkulosis di Kabupaten Karawang,” INOVTEK Polbeng-Seri Informatika, vol. 5, no. 2, pp. 229–239, 2020.

Published
2022-05-09
How to Cite
[1]
G. Triyandana, L. Putri, and Y. Umaidah, “Penerapan Data Mining Pengelompokan Menu Makanan dan Minuman Berdasarkan Tingkat Penjualan Menggunakan Metode K-Means”, JAIC, vol. 6, no. 1, pp. 40-46, May 2022.
Section
Articles