ANALISIS DESKRIPTIF DAN DETEKSI OUTLIER PADA DATA PENJUALAN PRODUK DI MARKETPLACE: STUDI KASUS TOKO XYZ

Authors

  • Alif Fazjrul Caesar Ifmaini Universitas Tarumanagara
  • Jap Tji Beng Universitas Tarumanagara
  • Novario Jaya Perdana Universitas Tarumanagara

DOI:

https://doi.org/10.31539/q7v1py77

Abstract

Penelitian ini menelaah karakteristik transaksi penjualan pada platform e-commerce melalui variabel Order Amount (harga yang dibayar oleh pembeli), Quantity (jumlah produk per transaksi), dan Shipping Fee (biaya pengiriman). Analisis deskriptif mencakup rata-rata, median, minimum, maksimum, dan standar deviasi, diikuti dengan penerapan metode Z-Score dan IQR untuk mendeteksi transaksi ekstrem. Hasil mengindikasikan bahwa sebagian besar transaksi berada di rentang harga menengah, namun ditemukan nilai ekstrem pada variabel harga dan ongkir yang signifikan. Temuan ini memberikan implikasi bagi pelaku usaha dalam mengoptimalkan strategi penetapan harga dan pengelolaan ongkir serta meningkatkan kualitas data transaksi.  

Kata Kunci: Analisis Deskriptif, Deteksi Outlier, E-Commerce, Order Amount, Quantity, Shipping Fee

References

Banaszek, A., & Lisaj, A. (2021). The Concept of Advanced Maritime Integrated Data Processing System with Use of Neural Network Methods. Procedia Computer Science, 192, 2450-2459.doi.org/10.1016/j.procs.2021.09.014

Beng, J. T., Nurkholiza, R., Tiatri, S., Hai, S. T., Dinatha, V. O. D., Margareta, M., Salsabila, T. M., Sefira, F. M., & Zahro, T. (2025). Datafikasi Melalui Fashion Modeling Di Universitas X Malaysia. Jutisi : Jurnal Ilmiah Teknik Informatika Dan Sistem Informasi, 14(2), 1386. https://doi.org/10.35889/jutisi.v14i2.2967

Ben-Gal, I. (2005). Outlier Detection. In O. Maimon & L. Rokach (Eds.), Data Mining and Knowledge Discovery Handbook (pp. 131–146). Springer US. https://doi.org/10.1007/0-387-25465-X_7

Chaudhary, N., & Chowdhury, D. R. (2019). Data preprocessing for evaluation of recommendation models in E-commerce. Data, 4(1). https://doi.org/10.3390/data4010023

Francesco, D., Beng, J. T., Perdana, N. J., Zabi­ta, P. D., & Sinambela, M. E. (2025). Perancangan sistem informasi inventori multi-lokasi berbasis web pada PT XYZ dengan metode Waterfall. INTECOMS: Journal of Information Technology and Computer Science, 8(6). https://doi.org/10.31539/mdyfwn12

Guo, Y., Hu, X., Zou, Y., Li, S., Cheng, W., & Li, Z. (2020). Maximizing E-Tailers’ Sales Volume through the Shipping-Fee Discount and Product Recommendation System. Discrete Dynamics in Nature and Society, 2020. https://doi.org/10.1155/2020/7349162

Han, J., Kamber, M., & Pei, J. (2011). Data Mining. Concepts and Techniques, 3rd Edition (The Morgan Kaufmann Series in Data Management Systems).

Hodge, V. J., & Austin, J. (2004). A Survey of Outlier Detection Methodologies. Artificial Intelligence Review, 22(2), 85–126. https://doi.org/10.1007/s10462-004-4304-y

Huy, P. Q., & Phuc, V. K. (2023). Big data in relation with business intelligence capabilities and e-commerce during COVID-19 pandemic in accountant’s perspective. Future Business Journal, 9(1). https://doi.org/10.1186/s43093-023-00221-4

Ibrahimy, S. M., & Ibrahimy, A. I. (2023). The Impact of Big Data Analytics on Business Intelligence in E-Commerce: A Review. Asian Journal of Electrical and Electronic Engineering, 3(2), 45–48. https://doi.org/10.69955/ajoeee.2023.v3i2.54

Jansen, B. J., & Schuster, S. (2011). BIDDING ON THE BUYING FUNNEL FOR SPONSORED SEARCH AND KEYWORD ADVERTISING. In Journal of Electronic Commerce Research (Vol. 12).

Mah, P. M., Skalna, I., & Pelech-Pilichowski, T. (2025). AI-driven anomaly detection in E-commerce services: a deep learning and NLP approach to the isolation forest algorithm trees. Journal of Theoretical and Applied Electronic Commerce Research, 20(3), 214.

Nguyen, T., & Nguyen, Q. (2015). An Empirical Study of Anomaly Detection in Online Games. IEEE.

Ramanda, G., Mulyawan, B., & Perdana, N. J. (2025). Perancangan sistem operasional indekos berbasis web untuk meningkatkan kemudahan pengelolaan dan pengalaman penghuni. Progresif: Jurnal Ilmiah Komputer

Rehman, F. U., Aslam, W., Aslam, F., Nazar, S., Khan, F. N., & Naseem, A. (2023). An efficient anomaly detection system for e-commerce pricing using machine learning techniques. Journal of Xi’an Shiyou University, Natural Science Edition, (ISSN 1673-064X).

Santoso, C., Arisandi, D., & Beng, J. T. (2024). Perancangan sistem informasi dalam penjualan pada Toko Furniture Bahagia berbasis web. Jurnal Ilmu Komputer dan Sistem Informasi, 12(1).

Saxena, M. (2025). Understanding Behaviour Of Consumer In E-Commerce: Trends, Influence,and Purchasing. International Journal of Research Publication and Reviews, 6, 6414–6418. https://doi.org/10.55248/gengpi.6.0625.2218

Tsay, R. S. (2010). Analysis of Financial Time Series, Third Edition (Wiley Series in Probability and Statistics).

Wang, H., Li, D., Jiang, C., & Zhang, Y. (2023). Exploring the Interactive Relationship between Retailers’ Free Shipping Decisions and Manufacturers’ Product Sales in Digital Retailing. Sustainability (Switzerland), 15(17). https://doi.org/10.3390/su151712762

Wickramasuriya, R., & Marchiori, D. (2019). Automated detection of business-relevant outliers in e-commerce conversion rate.

Yaro, A. S., Maly, F., & Prazak, P. (2023). Outlier Detection in Time-Series Receive Signal Strength Observation Using Z-Score Method with Sn Scale Estimator for Indoor Localization. Applied Sciences (Switzerland), 13(6). https://doi.org/10.3390/app13063900

Yuniawati, E. I., Beng, J. T., & Tiatri, S. (2025). Digital Citizenship Guna Mencegah Perilaku Cyberbullying. https://doi.org/10.52188/Junu.V1i2.1101

Downloads

Published

2025-12-02