Implementasi Data Mining Untuk Prediksi Stok Penjualan Keramik dengan Metode K-Means
- Ferdian Arya Dinata Universitas Islam Negeri Sultan Syarif Kasim Riau, Pekanbaru, Indonesia
-
Alwis Nazir
*
Universitas Islam Negeri Sultan Syarif Kasim Riau, Pekanbaru,
Indonesia
- Muhammad Fikry Universitas Islam Negeri Sultan Syarif Kasim Riau, Pekanbaru, Indonesia
- Iis Afrianty Universitas Islam Negeri Sultan Syarif Kasim Riau, Pekanbaru, Indonesia (*) Corresponding Author
Abstract
Ceramics has become one goods that consumers show interest in every year, so many companies are interested in selling ceramics. However, ceramic sales must meet and balance changing customer needs as well as problems found regarding ceramic products and customers, such as a lack of stock of ceramic products which results in customers not placing orders and product sales not meeting targets. So it is necessary to group ceramics to anticipate the risks that the company will accept by utilizing the data mining process using past data. This research uses the K-Means method found in data mining. The objective of this research is to group determine sales of brands that have potential for additional stock in the future and to test the data using the DBI (Davies Bouldin Index) which is carried out by testing the distance values between clusters through a series of experiments. This research uses data for the last 1 year from January 2022 to December 2022 with a total of 156 data using 9 attributes, namely brand, item code (FT, WT) and size (40x40, 25x25, 50x50, 25x40, 60x60, 20x40). The results of the research using the K-Means method, the best-selling brand is cluster 2, the best-selling brand is cluster 1 and the best-selling brand is cluster 0. The best-selling brand is HRM, the best-selling brand is VALENSIA and the best-selling brand is MCC. Test results using the DBI method with a validity of 01.013 show that the best cluster is obtained at k=3 using the elbow method. It is hoped that this research will contribute to related companies as support for decision making.
Downloads
References
C. Purnama, W. Witanti, and P. N. Sabrina, “KLASTERISASI PENJUALAN PAKAIAN UNTUK MENINGKATKAN STRATEGI PENJUALAN BARANG MENGGUNAKAN K-MEANS,” JOINT (Journal of Information Technology), vol. 04, no. 01, pp. 35-38, Mar. 2022.
D. Handoko, H. Satria Tambunan, and J. T. Hardinata, “Analisis Penjualan Produk Paket Kuota Internet Dengan Metode K-Nearest Neighbor,” Jurnal Riset Sistem Informasi Dan Teknik Informatika (JURASIK), vol. 6, pp. 111–119, 2021, [Online]. Available: https://tunasbangsa.ac.id/ejurnal/index.php/jurasik
M. Richie and dan Halim Agung, “Implementation of K-Means Algorithm For Ceramic Selection and Potential Customer at CV. Jaya Tunggal Keramik,” Jurnal Algoritma, Logika dan Komputasi , vol. III, no. 2, 2020, doi: 10.30813/j-alu.v2i2.2157.
W. W. Kristianto and C. Rudianto, “Penerapan Data Mining Pada Penjualan Produk Menggunakan Metode K-Means Clustering (Studi Kasus Toko Sepatu Kakikaki),” JUKANTI (Junal Pendidikan Teknologi Informasi), vol. 5, no. 2, Nov. 2022.
J. Jupriyanto and S. Nurlela, “KERANGKA PENGAMBILAN KEPUTUSAN UNTUK PEMASARAN PRESISI MENGGUNAKAN METODE RFM, ALGORITMA K-MEANS DAN DECISION TREE,” Jurnal Pilar Nusa Mandiri, vol. 15, no. 2, pp. 227–234, Sep. 2019, doi: 10.33480/pilar.v15i2.618.
F. Hadi and Y. Diana, “PENGKLUSTERAN PENJUALAN BAHAN BANGUNAN MENGGUNAKAN ALGORITMA K-MEANS,” JOISIE Journal Of Information System And Informatics Engineering, vol. 4, no. 1, pp. 22–28, 2020.
Izzah Lailil and Jananto Arief, “Penerapan Algoritma K-Means Clustering untuk Perencanaan kebutuhan Obat di Klinik Citra,” Jurnal Ilmiah Komputer, vol.18, no. 1, Feb. 2022.
Rahmah S Aulia, “KLASTERISASI POLA PENJUALAN PESTISIDA MENGGUNAKAN METODE K-MEANS CLUSTERING (STUDI KASUS DI TOKO JUANDA TANI KECAMATAN HUTABAYU RAJA),” Djtechno : Journal of Information Technology Research, vol.1, no. 1, 2020.
Muni Abdul, “Analisis Algoritma K-MeansClustering Untuk Menentukan Strategi Promosi Penjualan Sepeda Motor Studi Kasus PT. Alfa Scorpii,” JUTI-UNISI (Jurnal Tenik Industri UNISI), vol.4, no. 1, Jun. 2020.
S. Butsianto and N. T. Mayangwulan, “Penerapan Data Mining Untuk Prediksi Penjualan Mobil Menggunakan Metode K-Means Clustering,” 2020.
R. S. Wicaksana, D. Heksaputra, T. A. Syah, and F. F. Nur’aini, “Pendekatan K-Means Clustering Metode Elbow Pada Analisis Motivasi Pengunjung Festival Halal JHF#2,” Jurnal Ilmiah Ekonomi Islam, vol. 9, no. 3, p. 4162, Nov. 2023, doi: 10.29040/jiei.v9i3.10591.
Negara I Setiawan, Purwono and Ashari I Ahmad, “Analisa Cluster Data Transaksi Penjualan Minimarket Selama Pandemi Covid-19 dengan Algoritma K-means,” vol. 7, no. 1, 2018.
R. Mayang Sari, V. Tasril, and Y. M. Apridonal, “Prediksi Jumlah APBD Kota Payakumbuh dengan metode K-Means,” JURNAL IPTEKS TERAPAN, 2020, doi: 10.22216/jit.2020.v14i1.5323.
Indriyani Fintri and Irfiani Eni, “Clustering Data Penjualan pada Toko Perlengkapan Outdoor Menggunakan Metode K-Means,” JUITA: Jurnal Informatika, vol. 7, no. 2, nov. 2019.
F. Nurdiyansyah and I. Akbar, “Implementasi Algoritma K-Means untuk Menentukan Persediaan Barang pada Poultry Shop Article,” Jurnal Teknologi dan Manajemen Informatika, vol. 7, no. 2, pp. 86–94, 2021, [Online]. Available: http://http://jurnal.unmer.ac.id/index.php/jtmi
A. Wahid, A. Nazir, S. Kurnia Gusti, F. Syafria “Pengelompokan Keberhasilan Produksi Peternak Ayam Broiler di Riau Berdasarkan Index Performance Menggunakan K-Means Clustering,” Techno.COM, vol. 22, no. 1, Feb. 2023.
S. W. Harjono et al., “Klasterisasi Tingkat Penjualan pada Startup Panak.id dengan Algoritma K-Means,” Jurnal Ilmiah Teknologi Informasi Asia, vol. 17, no. 1, 2023.
W. Tities Pambudi and A. Witanti, “PENERAPAN ALGORITMA K-MEANS UNTUK MENGANALISIS DATA PENJUALAN PADA TOKO AYU COLLECTION BERASIS WEB APPLICATION OF K-MEANS ALGORITHM TO ANALYZE SALES DATA AT WEB-BASED AYU COLLECTION STORE,” Jurnal Sistem Informasi Dan Bisnis Cerdas, vol. 15, no. 1. Februari 2022.
W. Warisa and N. Nurahman, “Perbandingan Performa Cluster Model Algoritma K-Means Dalam Mengelompokkan Penerima Bantuan Program Keluarga Harapan,” J. Sistem Info. Bisnis, vol. 13, no. 1, pp. 20–28, Jun. 2023, doi: 10.21456/vol13iss1pp20-28.
H. Fitriyah, E. M. Safitri, N. Muna, M. Khasanah, D. A. Aprilia, and D. Nurdiansyah, “IMPLEMENTASI ALGORITMA CLUSTERING DENGAN MODIFIKASI METODE ELBOW UNTUK MENDUKUNG STRATEGI PEMERATAAN BANTUAN SOSIAL DI KABUPATEN BOJONEGORO,” Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistika, vol. 4, no. 3, pp. 1598–1607, Dec. 2023, doi: 10.46306/lb.v4i3.453.
Bila bermanfaat silahkan share artikel ini
Berikan Komentar Anda terhadap artikel Implementasi Data Mining Untuk Prediksi Stok Penjualan Keramik dengan Metode K-Means
Pages: 701-708
Copyright (c) 2024 Ferdian Arya Dinata, Alwis Nazir, Muhammad Fikry, Iis Afrianty
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under Creative Commons Attribution 4.0 International License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (Refer to The Effect of Open Access).
