Optimization of Kebaya Product Grouping Using K-Means  Algorithm for Marketing Strategy of Rental Services at  Gifaattire Store

Nuraeni; Martanto; Arif Rinaldi  Dikananda; Ahmad Rifai

doi:10.59934/jaiea.v4i3.776

Authors

Nuraeni STMIK IKMI Cirebon
Martanto STMIK IKMI Cirebon
Arif Rinaldi Dikananda STMIK IKMI Cirebon
Ahmad Rifai STMIK IKMI Cirebon

DOI:

https://doi.org/10.59934/jaiea.v4i3.776

Keywords:

K-Means algorithm, clustering, marketing strategy, stock management, data mining

Abstract

This study aims to implement the K-Means algorithm to improve the kebaya clustering model to support the rental marketing strategy at Gifaattire Store. The K-Means algorithm was used to analyze eight months of historical kebaya rental data, focusing on the attributes of kebaya type and color. Using the Knowledge Discovery in Database (KDD) approach, the research conducted data selection, preprocessing, transformation, data mining, and evaluation of clustering results. Davies-Bouldin Index (DBI) was utilized to assess the quality of clustering, resulting in an optimal value of 6 clusters with a DBI of 0.580. The results showed that each cluster has unique characteristics that reflect customer demand patterns. Cluster 0, the largest cluster, indicates kebayas with high demand but limited color variations. In contrast, Cluster 1 indicates kebayas with a wide variety of colors but specific demand. This information enables Gifaattire Store to design more targeted data-driven marketing strategies and improve stock management efficiency. The research contributes to the development of literature on the application of K-Means in the fashion rental sector and offers practical insights into understanding customer preferences.

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References

R. Samsudin, M. Martanto, and U. Hayati, “Optimalisasi Stok Barang Melalui Algoritma K-Means Clustering Analisis Untuk Manajemen Persediaan Dalam Konteks Bisnis Modern,” JATI (Jurnal Mhs. Tek. Inform., vol. 8, no. 3, pp. 3572–3580, 2024, doi: 10.36040/jati.v8i3.9742.

Nelly and Irsan, “Analisis Persediaan Barang Dagang Terhadap Penjualan Celana Casual Pada Pt Multi Garmenjaya Cabang Matahari Department Store Internasional Plaza Palembang,” J. Ilm. Akunt. Rahmaniyah, vol. 6, no. 1, p. 84, 2023, doi: 10.51877/jiar.v6i1.261.

N. Susi, S. Sugiana, and B. Musty, “Analisis Data Sistem Informasi Monitoring Marketing; Tools Pengambilan Keputusan Strategic,” Jutisi J. Ilm. Tek. Inform. dan Sist. Inf., vol. 12, no. 2, pp. 696–708, 2023.

Hakim, M. Abdurrahman, A. B. Prasetijo, and D. Eridani, “PENERAPAN DATA MINING DENGAN ALGORITMA K-MEANS CLUSTERING PENYEWAAN ALAT - ALAT EVENT PADA STUDI KASUS CV . DIPO RENTAL CREATIVINDO IMPLEMENTATION OF DATA MINING USING THE K-MEANS CLUSTERING EVENT RENTAL,” vol. 1, no. 4, pp. 148–155, 2023, doi: 10.14710/jtk.v1i4.37011.

M. R. Tetlageni and A. Solichin, “Klasterisasi Penyewaan Kendaraan Menggunakan Metode K-Means Pada PT. Mardika Daya Tribuana,” Bit (Fakultas Teknol. Inf. Univ. Budi Luhur), vol. 20, no. 2, p. 141, 2023, doi: 10.36080/bit.v20i2.2496.

A. Ardiansah, A. Razak, and B. H. Harto, “Adaptasi dan Inovasi dalam Manajemen Inventori Pada E-Commerce Lazada,” Innov. J. Soc. Sci. Res., vol. 4, no. 3, pp. 10335–10350, 2024.

Sambharakreshna, Yudhanta, F. Kusumawati, and A. Wulandari, “Dampak Pengelolaan Keuangan dengan Pendekatan Kebebasan Finansial , Teknologi Keuangan , Dan Modal Sosial Terhadap Pendapatan Usaha The Impact of Financial Management with A Focus on Financial Freedom , Financial Technology , And Social Capital on Busines,” vol. 5, no. 02, pp. 175–192, 2024.

G. Aprilianur and E. L. Hadisaputro, “Penerapan Data Mining Menggunakan Metode K-MeansClustering Untuk Analisa Penjualan Toko Myam HijabPenajam,” Jupiter, vol. 14, no. 1, pp. 161–170, 2022.