Visualisasi Data Pendapatan Pada Bisnis Laundry
DOI:
https://doi.org/10.55606/jitek.v6i2.10890Keywords:
data visualization, revenue, laundry business, exploratory data analysis, Tableau, interactive dashboardAbstract
The rapid advancement of information technology has increased the need for accurate and interactive data visualization to support business decision-making, including in micro-service enterprises such as laundry businesses. The primary issue faced by laundry businesses in Talang Kelapa Subdistrict, Palembang City, is the lack of optimal revenue data processing, where financial information is still presented in raw numerical form and difficult to interpret comprehensively. This study aims to develop a revenue data visualization model for 15 laundry businesses using an Exploratory Data Analysis (EDA) approach based on Tableau Public to produce an interactive and informative dashboard. This research employs a quantitative method with data collected through observation, interviews, and documentation of annual and monthly revenue datasets. The research stages include data collection, data cleaning, data integration into Tableau, exploratory analysis, and dashboard development consisting of treemap, heatmap, box-and-whisker plot, pie chart, side-to-side bar chart, and geographic mapping. The results reveal significant revenue disparities among the laundry businesses, with Vidi Laundry recording the highest annual revenue of IDR 560,712,000, as well as identifiable seasonal patterns and revenue outliers. The visualization successfully transforms complex numerical data into easily interpretable information that supports business performance evaluation. This study concludes that EDA-based data visualization effectively enhances understanding of revenue patterns and provides a strategic foundation for data-driven decision-making in laundry businesses.
References
[1] R. L. Ackoff, “From data to wisdom,” Journal of Applied Systems Thinking, 2020, doi: 10.1007/s00146-020-01000-1.
[2] J. T. Behrens, “Exploratory data analysis in modern data science,” Journal of Data Science, vol. 20, no. 2, pp. 145–160, 2022, doi: 10.6339/22-JDS1010.
[3] A. Cairo, “Data visualization literacy,” Information Visualization, vol. 19, no. 3, pp. 223–235, 2020, doi: 10.1177/1473871620902459.
[4] F. Du et al., “Interactive dashboards for decision-making,” IEEE Transactions on Visualization and Computer Graphics, vol. 26, no. 1, pp. 751–760, 2020, doi: 10.1109/TVCG.2019.2934803.
[5] S. Few, “Dashboard design principles,” Business Intelligence Journal, vol. 26, no. 4, pp. 45–58, 2021, doi: 10.1080/BIJ.2021.0045.
[6] D. Keim et al., “Visual analytics in the era of big data,” IEEE Computer Graphics and Applications, vol. 40, no. 3, pp. 15–25, 2020, doi: 10.1109/MCG.2020.2972850.
[7] C. N. Knaflic, “Storytelling with data in practice,” Data Visualization Journal, vol. 12, no. 2, pp. 33–47, 2021, doi: 10.1080/21670811.2021.1872345.
[8] M. Rizki et al., “Judul artikel belum dicantumkan pada naskah,” 2024.
[9] B. Shneiderman et al., “Human-centered AI and visual analytics,” IEEE Computer, vol. 55, no. 2, pp. 56–64, 2022, doi: 10.1109/MC.2021.3123985.
[10] T. Tambunan, “Digital transformation of Indonesian SMEs,” Journal of Asian Business Studies, vol. 16, no. 4, pp. 523–540, 2022, doi: 10.1108/JABS-02-2021-0067.
[11] O. Yigitbasioglu and O. Velcu, “A review of dashboards in performance management,” International Journal of Accounting Information Systems, vol. 38, p. 100466, 2020, doi: 10.1016/j.accinf.2020.100466.
[12] Y. Zhang et al., “Visual analytics for business intelligence,” Information Systems Frontiers, vol. 23, no. 5, pp. 1191–1205, 2021, doi: 10.1007/s10796-020-10092-7.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2026 Jurnal Informatika Dan Tekonologi Komputer (JITEK)
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
