OPTIMIZATION PRODUCT RECOMMENDATION USING K-MEANS, AGGLOMERATIVE CLUSTERING AND FP-GROWTH ALGORITHM

Ratu Najmil Huda; Rifqi Fitriadi; Arief Wibowo

doi:10.52436/1.jutif.2024.5.4.1901

Ratu Najmil Huda Computer Science Master's Study Program, Faculty of Information Technology, Universitas Budi Luhur, Indonesia
Rifqi Fitriadi Computer Science Master's Study Program, Faculty of Information Technology, Universitas Budi Luhur, Indonesia
Arief Wibowo Information Systems Study Program, Faculty of Information Technology, Universitas Budi Luhur, Indonesia

DOI: https://doi.org/10.52436/1.jutif.2024.5.4.1901

Keywords: Agglomerative Clustering, FP-Growth, K-Means, Product Recommendations

Abstract

The growth of online business has been rising considerably in recent years. The growth is affected by technology advancement in Internet and smartphones and consumer behavior change for better online shopping experience. To anticipate this swift customer behavior, business owners need to have an excellent inventory management to be able to keep making profits. In data mining realm, the algorithm model that is known to be applied in this case is the association algorithm. This model will explicate customers’ purchasing patterns where is useful in calculating stock accurately. The aim of this research is to find an appropriate model in handling large data to obtain valid association rules that have minimum support value, confidence value, and high lift ratios. It is hoped that the results of this research can provide recommendations for online sellers to manage a large variety of goods and to keep making profits. Datasets that contain a large variety of goods are handled first by using a clustering algorithm to group similar items together. The dataset tested was divided into three groups, namely, dataset without clusters, k-means cluster, and agglomerative cluster. After forming three groups of datasets, FP-Growth was applied to each dataset. The result is that datasets with clusters, whether using k-means or agglomerative, have a minimum support value that is greater than datasets without clusters. Most association rules are obtained from the k-means cluster dataset. Based on the model applied in this research, the association itemset size only obtains one conclusion from one premise.

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References

A. Supriyanto and K. F. Hana, “Strategi Pengembangan Desa Digital Untuk Meningkatkan Produktivitas UMKM,” BISNIS : Jurnal Bisnis dan Manajemen Islam, vol. 8, no. 2, p. 199, Dec. 2020, doi: 10.21043/bisnis.v8i2.8640.

P. Iffa Rosada, M. Syifa Amin Widigdo, E. Syariah, F. Agama Islam, and U. Muhammadiyah Yogyakarta, “Analisis Perilaku Konsumen Muslim Dalam Belanja Melalui Shopee Saat Pandemi Covid-19 Berdasarkan Perspektif Islam,” Jurnal Ilmiah Ekonomi Islam, vol. 9(02), 2023, doi: 10.29040/jiei.v9i2.7123.

F. L. Dasri and E. Suwarni, “Analisis Disrupsi Media Digital Terhadap Industri Televisi Indonesia,” 2023.

L. Marlinah, “Mendorong Pertumbuhan Ekonomi Indonesia Melalui Penguatan Sektor Ekonomi Digitalpreneur Dan Creativepreneur,” 2019.

M. S. Hotana, “Industri E-Commerce Dalam Menciptakan Pasar Yang Kompetitif Berdasarkan Hukum Persaingan Usaha,” Jurnal Hukum Bisnis Bonum Commune, vol. 1, no. 1, 2018.

L. Sari Marita and I. Darwati, “Prediksi Persediaan Barang Menggunakan Metode Weighted Moving Average, Exponential Smoothing dan Simple Moving Average,” Jurnal TEKNO KOMPAK, vol. 16, no. 1, 2022.

R. P. Megatama, J. M. Maligan, and R. Septifani, “Studi Inventory Control Pada National Fullfillment Center Cikarang Di PT XYZ Indonesia,” PROSIDING SEMINAR NASIONAL INSTIPER, vol. 1, no. 1, pp. 314–322, Jul. 2022, doi: 10.55180/pro.v1i1.268.

A. Maulana and A. A. Fajrin, “Penerapan Data Mining Untuk Analisis Pola Pembelian Konsumen Dengan Algoritma FP-Growth Pada Data Transaksi Penjualan Spare Part Motor,” Kumpulan jurnaL Ilmu Komputer, vol. 5, no. 1, 2018.

I. Nurrohmat, O. Nurdiawan, and A. Bahtiar, “Implementasi Algoritma FP-Growth Untuk Menunjang Keputusan Persedian Barang Di CV Indotech Jaya Sentosa Kota Cirebon,” Jurnal Sistem Informasi dan Manajemen , vol. 10, no. 2, 2022, [Online]. Available: https://ejournal.stmikgici.ac.id/

F. Nuraeni, D. Tresnawati, Y. Handoko Agustin, and G. Fauzi, “Optimization of Market Basket Analysis Using Centroid-Based Clustering Algorithm And FP-Growth Algorithm,” Jurnal Teknik Informatika (Jutif), vol. 3, no. 6, pp. 1581–1590, Dec. 2022, doi: 10.20884/1.jutif.2022.3.6.399.

I. Syukra, A. Hidayat, and M. Z. Fauzi, “Implementation of K-Medoids and FP-Growth Algorithms for Grouping and Product Offering Recommendations,” Indonesian Journal of Artificial Intelligence and Data Mining, vol. 2, no. 2, p. 107, Nov. 2019, doi: 10.24014/ijaidm.v2i2.8326.

S. M. Dewi, A. P. Windarto, I. S. Damanik, and H. Satria, Analisa Metode K-Means pada Pengelompokan Kriminalitas Menurut Wilayah. Seminar Nasional Sains & Teknologi Informasi (SENSASI), 2019. [Online]. Available: http://prosiding.seminar-id.com/index.php/sensasi/issue/archivePage|620

E. Widodo, P. Ermayani, L. N. Laila, and A. T. Madani, “Pengelompokkan Provinsi di Indonesia Berdasarkan Tingkat Kemiskinan Menggunakan Analisis Hierarchical Agglomerative Clustering (Indonesian Province Grouping Based on Poverty Level Using Hierarchical Agglomerative Clustering Analysis),” 2021.

Vijaya, S. Sharma, and N. Batra, Comparative Study of Single Linkage, Complete Linkage, and Ward Method of Agglomerative Clustering. 2019.

F. Nuraeni, D. Tresnawati, Y. Handoko Agustin, and G. Fauzi, “Optimization of Market Basket Analysis Using Centroid-Based Clustering Algorithm and FP-Growth Algorithm,” Jurnal Teknik Informatika (Jutif), vol. 3, no. 6, pp. 1581–1590, Dec. 2022, doi: 10.20884/1.jutif.2022.3.6.399.

B. Septia Pranata and D. Putro Utomo, “Penerapan Data Mining Algoritma FP-Growth Untuk Persediaan Sparepart Pada Bengkel Motor (Study Kasus Bengkel Sinar Service),” Bulletin of Information Technology (BIT), vol. 1, no. 2, pp. 83–91, 2020.