K-Means Clustering dalam Dunia Konveksi: Pengelompokan Cerdas untuk Optimalisasi Stok
DOI:
https://doi.org/10.55606/isaintek.v5i02.231Keywords:
K-Means Clustering Algorithm, Goods Classification, Business Process Management, Inventory ManagementAbstract
The garment industry faces challenges in grouping diverse goods based on their characteristics, which can affect the efficiency of the production process and inventory management. This study aims to apply the K-Means Clustering algorithm in garment goods classification to improve business process management and optimization. The K-Means algorithm, as one of the popular clustering methods, is used to group garment goods data based on features such as size, color, fabric type, and product model. This method begins with the selection of relevant features from the dataset obtained from the garment industry. Furthermore, the K-Means algorithm is implemented to determine the optimal number of clusters using the elbow score and silhouette methods. The clustering results are analyzed to evaluate the extent to which the algorithm can form homogeneous and business-relevant groups of goods. The results of this study indicate that the K-Means Clustering algorithm is effective in grouping garment goods into several categories that are consistent with business patterns and needs. The application of this method results in a better understanding of goods grouping that can improve production efficiency and facilitate inventory management. This study contributes to the best practices in the use of the K-Means algorithm in the convection sector and shows the potential of this method in supporting data-driven decision making.
Keywords:,
References
Abdillah, G., Putra, F. A., Renaldi, F., Informatika, P. S., Jenderal, U., Yani, A., & Cimahi, K. (2016). Penerapan data mining pemakaian air pelanggan untuk menentukan klasifikasi potensi pemakaian air pelanggan baru di PDAM Tirta Raharja menggunakan algoritma K-Means. Sentika 2016, 2016(Sentika), 18–19.
Annur, H. (2019). Penerapan data mining menentukan strategi penjualan variasi mobil menggunakan metode K-Means clustering. Jurnal Informatika Upgris, 5(1). https://doi.org/10.26877/jiu.v5i1.3091
Dewi, M. (2019). Analisis statistik keterpakaian database online Science Direct (Januari-Juni 2018) dalam mengambil kebijakan pengadaan bahan pustaka elektronik pada Perpustakaan Universitas Islam Indonesia. Al-Kuttab: Jurnal Kajian Perpustakaan, Informasi Dan Kearsipan, 1(1), 23. https://doi.org/10.24952/ktb.v1i1.1028
Hutabarat, J. T. (2018). Penerapan algoritma K-Means untuk menentukan lokasi promosi produk minuman penurun kolesterol Nutrive Benecol pada PT Perkasa/Kalbe Nutritionals. Pelita Informatika, 17, 465–472.
Issa, J. (2020). Handbook of medical image computing and computer assisted intervention. 8(5), 55.
Ketherin, B. E., Arifiyanti, A. A., Sodik, A., & Institut Teknologi Adhi Tama Surabaya. (2018). Analisa segmentasi konsumen menggunakan algoritma K-Means clustering. Seminar Nasional Sains Dan Teknologi Terapan VI 2018.
Membara, E. P., Yulianti, L., & Kanedi, I. (2014). Sistem informasi akademik SMP Negeri 2 Talang Empat berbasis web. Media Informatika, 10(1), 72–80.
Muningsih, E., & Kiswati, S. (2018). Penerapan metode K-Means untuk clustering produk online shop dalam penentuan stok barang. Jurnal Bianglala Informatika, 3(1), 10–17.
Panjaitan, B., Almi, S., Informatika, J. T., Teknik, F., Satya, U., Indonesia, N., & Potensial, P. (2019). Implementasi algoritma K-Means untuk pengelompokan data customer pada toko. Jurnal Teknologi Informasi, 227–233.
Parlina, I., Windarto, A. P., & Wanto, A. (2018). Memanfaatkan algoritma K-Means dalam menentukan pegawai yang layak mengikuti assessment center. Jurnal Ilmu Komputer, 3(1), 87–93.
Satwika, I. K. S., & Semadi, K. N. (2020). Perbandingan performansi web server Apache dan Nginx dengan menggunakan IPv6. SCAN: Jurnal Teknologi Informasi Dan Komunikasi, 15(1), 10–15. https://doi.org/10.33005/scan.v15i1.1847
Siregar, M. H. (2018). Klasterisasi penjualan alat-alat bangunan menggunakan metode K-Means (Studi kasus di Toko Adi Bangunan). Jurnal Ilmu Komputer, 1(2), 83.
