Eksplorasi dan Visualisasi Data Transaksi Online Retail Untuk Mendukung Pengambilan Keputusan Bisnis
DOI:
https://doi.org/10.57119/litdig.v4i1.195Keywords:
exploratory data analysis, data visualization, interactive dashboard, retail transactionsAbstract
The growth of e-commerce generates large and complex transaction data volumes requiring in-depth analysis for business decision-making support. This research applies Exploratory Data Analysis (EDA) approach to the Online Retail UCI dataset (541,909 transactions, 2010-2011) using Python on Google Colab for data cleaning, yielding 392,692 valid records. Analysis focuses on time trends of transactions and revenue, top 7 products by quantity and revenue, and sales distribution across regions through interactive Looker Studio dashboard. Findings reveal seasonal transaction patterns, differences between high-volume vs high-revenue products, and United Kingdom dominance (80% revenue). The interactive dashboard with time and country filters enables flexible data exploration for promotion strategy, inventory management, and market expansion decisions. This research proves data visualization effectiveness as a retail online business decision support system.
Downloads
References
[1] M. Abdur Rasyid Asruddin, “Komparasi model klasifikasi data online retail dengan seleksi fitur,” unpublished manuscript, n.d. [Online]. Available: http://archive.ics.uci.edu/ml/datasets/Online+Retail
[2] I. Agus and M. Hasibuan, “Customer segmentation using an enhanced RFM-K-means framework on the online retail dataset,” International Journal of Informatics and Information Systems, vol. 8, no. 4, pp. 221–233, 2025, doi: 10.47738/ijiis.v8i4.289.
[3] A. K. Mishra, M. Sinha, and S. K. Jha, “Data analytics for visualization of business insights for an online retail store using Python,” International Journal of Management & Entrepreneurship Research, vol. 6, no. 10, pp. 3314–3320, 2024, doi: 10.51594/ijmer.v6i10.1636.
[4] D. A. Awaliyah, B. Prasetiyo, R. Muzayanah, and A. D. Lestari, “Optimizing customer segmentation in online retail transactions through the implementation of the K-means clustering algorithm,” Scientific Journal of Informatics, vol. 11, no. 2, pp. 539–548, 2024, doi: 10.15294/sji.v11i2.6137.
[5] S. Chakma, G. Chakma, and R. Chakma, “Statistical insights and exploratory data analysis for e-commerce sales data: A collaborative filtering and recency-based recommendation system,” International Journal of Business and Management, vol. 20, no. 5, p. 258, 2025, doi: 10.5539/ijbm.v20n5p258.
[6] V. V. Dandasi, “Data warehousing and business intelligence technologies in retail: A comparative analysis of predictive models for performance optimization,” Journal of Business Intelligence and Data Analytics, vol. 2, no. 2, pp. 1–8, 2025, doi: 10.55124/jbid.v2i2.251.
[7] I. F. Fakhrana and A. Voutama, “Analisis tren penjualan dan strategi harga di e-commerce menggunakan exploratory data analysis,” Jurnal Ilmu Komputer dan Bisnis, vol. 16, no. 1, pp. 154–164, 2025, doi: 10.47927/jikb.v16i1.976.
[8] J. Faris Fadil, M. Firmansyah, W. Asriyani, and M. F. Fadil, “Klasterisasi pola transaksi online retail menggunakan metode self-organizing map,” Journal of Innovative and Creativity, vol. 5, no. 3, n.d.
[9] A. Lutfi et al., “Drivers and impact of big data analytic adoption in the retail industry: A quantitative investigation applying structural equation modeling,” Journal of Retailing and Consumer Services, vol. 70, 2023, doi: 10.1016/j.jretconser.2022.103129.
[10] D. Chen, “Online retail,” UCI Machine Learning Repository, 2015, doi: 10.24432/C5BW33.
[11] F. A. Maresti, G. M. Anugraheni, R. A. Hargiyanto, and K. Mustaqim, “Penerapan exploratory data analysis (EDA) dan analisis recency, frequency, and monetary (RFM) untuk segmentasi pelanggan e-commerce,” unpublished manuscript, n.d. [Online]. Available: http://ejurnal.ulbi.ac.id/index.php/competitive
[12] Y. Martua, L. Sianipar, R. A. M. Yulianto, and E. Rilvani, “Study literature review application of market basket analysis on UCI online retail dataset for business insights,” Kohesi: Jurnal Multidisiplin Saintek, vol. 1, no. 2, n.d., doi: 10.8734/Kohesi.v1i2.365.
[13] M. K. Najib and E. M. Stefany, “Visualisasi data penjualan supermarket dengan Microsoft Power BI untuk menghasilkan insight dan rekomendasi,” Jurnal Sistem Informasi (TEKNOFILE), vol. 2, pp. 921–928, 2024.
[14] D. Sapitri, “Visualisasi data e-commerce menggunakan Power BI dalam meningkatkan strategi pemasaran,” Jurnal Sistem Informasi (TEKNOFILE), vol. 3, pp. 749–759, 2025.
[15] S. S. Nuraini, A. M. Romadhoni, M. A. F. Taqiyuddin, and M. Wicaksono, “Pemanfaatan visualisasi data dalam business intelligence untuk strategi bisnis perusahaan retail,” Jurnal Manuhara: Pusat Penelitian Ilmu Manajemen dan Bisnis, vol. 3, no. 3, pp. 234–243, 2025, doi: 10.61132/manuhara.v3i3.1992.
[16] G. B. Sulistyo, N. Hasan, S. Kiswati, F. Natalia, and E. Muningsih, “Segmentasi pelanggan dan optimalisasi penjualan pada data retail online berbasis model RFM,” Computer and Network Technology, vol. 5, no. 1, pp. 16–22, 2025. [Online]. Available: http://jurnal.bsi.ac.id/index.php/conten16
[17] A. Tipnis, “Framework for data analysis of retail market using machine learning algorithms,” International Journal for Research in Applied Science and Engineering Technology, vol. 14, no. 1, pp. 703–713, 2026, doi: 10.22214/ijraset.2026.76800.
[18] B. Turkmen, “Customer segmentation with machine learning for online retail industry,” The European Journal of Social and Behavioural Sciences, vol. 31, no. 2, pp. 111–136, 2022, doi: 10.15405/ejsbs.316.
[19] F. S. Utomo and Z. Lubna, “Visualisasi data penjualan untuk menentukan business insight menggunakan Metabase pada perusahaan retail global,” Sistemasi: Jurnal Sistem Informasi, vol. 13, no. 4, n.d. [Online]. Available: http://sistemasi.ftik.unisi.ac.id
[20] J. D. Velásquez-Henao, “Emerging trends in retail analytics: A bibliometric analysis of the last decade,” DYNA (Colombia), vol. 92, no. 237, pp. 16–29, 2025, doi: 10.15446/dyna.v92n237.116880.
[21] L. Wang, T. R. A. L. Pertheban, T. Li, and L. Zhao, “Application of business intelligence based on big data in e-commerce data evaluation,” Heliyon, vol. 10, no. 21, 2024, doi: 10.1016/j.heliyon.2024.e38768.
[22] W. Hastomo, A. S. B. Karno, Sudjiran, D. Arif, and E. S. Moreta, “Exploratory data analysis untuk data belanja pelanggan dan pendapatan bisnis,” Infotekmesin, vol. 13, no. 2, pp. 314–321, 2022, doi: 10.35970/infotekmesin.v13i2.1547.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2026 Dzaki Al Hafiz, Muhammad Haris Hermanto, Anton Setiawan, Muhammad Said Hasibuan
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
