Analisis Perbandingan Linkage Agglomerative Hierarchical Clustering Peningkatan Strategi Pemasaran Galeri Ulos Sianipar
DOI:
https://doi.org/10.54314/jssr.v9i4.6666Keywords:
Agglomerative Hierarchical Clustering, Linkage Method, Ulos, Strategi PemasaranAbstract
Keragaman harga, volume penjualan, frekuensi transaksi, dan persediaan produk Ulos menimbulkan kebutuhan akan strategi pemasaran yang berbeda sesuai karakteristik setiap produk. Analisis difokuskan pada perbandingan Single Linkage, Complete Linkage, Average Linkage, dan Ward.D2 dalam Agglomerative Hierarchical Clustering sebagai dasar segmentasi produk di Galeri Ulos Sianipar Medan. Sebanyak 161 produk dianalisis menggunakan atribut harga jual, jumlah terjual, frekuensi transaksi, dan stok akhir. Perbedaan skala atribut ditangani melalui standardisasi Z-score, sedangkan kedekatan antarproduk dihitung menggunakan Euclidean Distance. Keempat metode linkage diterapkan untuk membentuk tiga klaster dan dievaluasi berdasarkan Silhouette Score, Davies-Bouldin Index, distribusi anggota, serta kemudahan interpretasi bisnis. Single Linkage menghasilkan Silhouette Score sebesar 0,556650 dan Davies-Bouldin Index sebesar 0,286346, tetapi membentuk distribusi yang sangat timpang, yaitu 1-2-158. Average Linkage dan Complete Linkage juga menghasilkan satu klaster dominan dengan distribusi masing-masing 1-147-13 dan 21-119-21. Ward.D2 memperoleh Silhouette Score sebesar 0,401513 dan Davies-Bouldin Index sebesar 0,799404, tetapi menghasilkan distribusi yang lebih proporsional, yaitu 21, 82, dan 58 produk. Dengan mempertimbangkan keseimbangan anggota, kejelasan centroid, dan relevansi hasil bagi pengambilan keputusan, Ward.D2 ditetapkan sebagai model operasional yang paling sesuai. Klaster yang terbentuk merepresentasikan produk ekonomis berperputaran cepat, produk reguler potensial, dan produk premium dengan penjualan selektif. Segmentasi tersebut mendukung pengendalian persediaan dan bundling, penguatan promosi digital dan edukasi produk, serta pemasaran premium melalui personal selling dan sistem pre-order.
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References
Y. Putri, D. Aldo, and W. Ilham, “Retail Marketing Strategy Optimization: Customer Segmentation with Artificial Intelligence Integration and K-Means Clustering,” Sink. J. dan Penelit. Tek. Inform., vol. 8, no. 4, pp. 2155–2163, 2024, doi: 10.33395/sinkron.v8i4.14000.
K. Tabianan, S. Velu, and V. Ravi, “K-Means Clustering Approach for Intelligent Customer Segmentation Using Customer Purchase Behavior Data,” Sustainability, vol. 14, no. 12, p. 7243, 2022, doi: 10.3390/su14127243.
S. G. Muthmainah, A. I. Hadiana, and M. Melina, “Comparative Analysis of K-Means and K-Medoids Clustering in Retail Store Product Grouping,” Int. J. Quant. Res. Model., vol. 5, no. 3, pp. 280–294, 2024, doi: 10.46336/ijqrm.v5i3.753.
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, pp. 75–87, 2020, doi: 10.30646/sinus.v18i1.448.
N. Antonius Siagian and S. P. Sipayung, “Handling Data Imbalance Problem in Hybrid Resampling Approach to Improve Accuracy of K-Nearest Neighbors Algorithm,” Instal J. Komput., vol. 16, no. 02, pp. 78–87, Jun. 2024, doi: 10.54209/jurnalinstall.v16i02.207.
C. Wongoutong, “The Impact of Neglecting Feature Scaling in K-Means Clustering,” PLoS One, vol. 19, no. 12, p. e0310839, 2024, doi: 10.1371/journal.pone.0310839.
G. R. Suraya and A. W. Wijayanto, “Comparison of Hierarchical Clustering, K-Means, K-Medoids, and Fuzzy C-Means Methods in Grouping Provinces in Indonesia According to the Special Index for Handling Stunting,” Indones. J. Stat. Its Appl., vol. 6, no. 2, pp. 180–201, 2022, doi: 10.29244/ijsa.v6i2p180-201.
W. Usna and R. Aprilia, “Comparison of Agglomerative Hierarchical Clustering Algorithm and K-Means Algorithm in Poverty Data Clustering in North Sumatra,” Desimal J. Mat., vol. 7, no. 3, pp. 489–500, 2024, doi: 10.24042/djm.v7i3.24373.
R. H. Bhahari and K. Kusnawi, “Clustering Analysis of Socio-Economic Districts/Cities in East Java Province Using PCA and Hierarchical Clustering Methods,” Sink. J. dan Penelit. Tek. Inform., vol. 8, no. 4, pp. 2242–2251, 2024, doi: 10.33395/sinkron.v8i4.14078.
D. R. R. Putri, N. Ulinnuha, and P. K. Intan, “Comparison of Linkage Methods in Hierarchical Clustering for Grouping Districts/Cities in East Java Based on Stunting Determinants,” J. Appl. Informatics Comput., vol. 9, no. 5, pp. 2434–2442, 2025, doi: 10.30871/jaic.v9i5.10919.
F. Murtagh and P. Legendre, “Ward’s Hierarchical Clustering Method: Clustering Criterion and Agglomerative Algorithm,” J. Classif., vol. 31, no. 3, pp. 274–295, 2014, doi: 10.1007/s00357-014-9161-z.
P. J. Rousseeuw, “Silhouettes: A Graphical Aid to the Interpretation and Validation of Cluster Analysis,” J. Comput. Appl. Math., vol. 20, pp. 53–65, 1987, doi: 10.1016/0377-0427(87)90125-7.
I. F. Ashari, E. D. Nugroho, R. Baraku, I. N. Yanda, and R. Liwardana, “Analysis of Elbow, Silhouette, Davies-Bouldin, Calinski-Harabasz, and Rand-Index Evaluation on K-Means Algorithm for Classifying Flood-Affected Areas in Jakarta,” J. Appl. Informatics Comput., vol. 7, no. 1, pp. 89–97, 2023, doi: 10.30871/jaic.v7i1.4947.
D. L. Davies and D. W. Bouldin, “A Cluster Separation Measure,” IEEE Trans. Pattern Anal. Mach. Intell., vol. PAMI-1, no. 2, pp. 224–227, 1979, doi: 10.1109/TPAMI.1979.4766909.
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