Analisis Pola Transaksi dan Perilaku Pembelian Pelanggan E-Commerce Berdasarkan Karakteristik Demografis dan Waktu Transaksi

Authors

  • Yeni Roha Mahariani Universitas Bhinneka PGRI
  • Pangki Suseno Universitas Bhinneka PGRI
  • Dwi Junianto Universitas Bhinneka PGRI
  • Nindya N. A. Brillianio Universitas Bhinneka PGRI

DOI:

https://doi.org/10.61132/jumabedi.v3i1.1279

Keywords:

Boxplot, Demographic Characteristics, E-Commerce, Exploratory Data Analysis, Transaction Patterns

Abstract

The rapid growth of e-commerce has intensified the need to understand transaction patterns and customer purchasing behavior as a foundation for strategic decision-making. This study aims to analyze e-commerce transaction patterns and customer purchasing behavior based on demographic characteristics and transaction timing. By utilizing e-commerce transaction data, this research seeks to provide a more comprehensive understanding of customers’ purchasing tendencies and the factors influencing their behavior. This study employs Exploratory Data Analysis (EDA) as the primary method to descriptively explore data characteristics through various statistical visualizations, including histograms, bar charts, line graphs, and boxplots. The analysis conducted to identify transaction trends, the distribution of purchase values, and behavioral differences across demographic groups and specific time periods. The results indicate that e-commerce transaction patterns tend to increase during certain periods, particularly in the latter part of the observation timeframe, suggesting the influence of seasonal factors and promotional strategies. The distribution of transaction values is asymmetric, with most transactions occurring in the low to medium value range, while high-value transactions are conducted by a relatively small proportion of customers. Furthermore, variations in purchasing behavior are observed across demographic groups in terms of transaction frequency and value, despite relatively balanced transaction volumes. The findings confirm that e-commerce customer purchasing behavior is influenced by a combination of temporal factors and demographic characteristics. These results are expected to serve as a basis for e-commerce practitioners in developing more targeted marketing strategies and as a reference for future research in the field of e-commerce data analytics.

Downloads

Download data is not yet available.

References

Agung Yulianto, A., & Elsandra, Y. (2024). Pola pembelian konsumen dengan metode market basket analysis pada perishable product di toko roti Ikobana Bakery. Jurnal Nasional Teknologi Dan Sistem Informasi, 10(1), 82-91. https://doi.org/10.25077/TEKNOSI.v10i1.2024.82-91

Amalia Maresti, F., Mustika Anugraheni, G., Hargiyanto, R. A., & Mustaqim, K. (2024). Penerapan exploratory data analysis (EDA) dan analisis recency, frequency, and monetary (RFM) untuk segmentasi pelanggan e-commerce. Competitive, 19(1), 14-25. https://doi.org/10.36618/competitive.v19i1.4059

Angelina, S., & Magdalena, N. (2025). Analisis perilaku pembelian pelanggan berdasarkan pemasaran media sosial dan keterlibatan aktif pelanggan generasi Z dan milenial. JIIP - Jurnal Ilmiah Ilmu Pendidikan, 8(3), 2595-2600. https://doi.org/10.54371/jiip.v8i3.7151

Asyifah, A., Syafi'i, A., Hanipah, H., & Ispiyani, S. (2023). Pengembangan aplikasi e-commerce untuk peningkatan penjualan online. Action Research Literate, 7(10), 70-75. https://doi.org/10.46799/arl.v7i10.188

Awalina, E. F. L., & Rahayu, W. I. (2023). Optimalisasi strategi pemasaran dengan segmentasi pelanggan menggunakan penerapan K-Means clustering pada transaksi online retail. Jurnal Teknologi Dan Informasi, 13(2), 122-137. https://doi.org/10.34010/jati.v13i2.10090

Beckett, C., Eriksson, L., Johansson, E., & Wikström, C. (2018). Multivariate data analysis (MVDA). In W. S. Schlindwein & M. Gibson (Eds.), Pharmaceutical Quality by Design (1st ed., pp. 201-225). Wiley. https://doi.org/10.1002/9781118895238.ch8

Dinni, S. R., Wibawa, B. M., & Apriyansyah, B. R. (2021). Eksplorasi karakteristik segmentasi demografis dan perilaku berbelanja ibu rumah tangga melalui e-commerce di Indonesia. Jurnal Sains Dan Seni ITS, 9(2), D262-D268. https://doi.org/10.12962/j23373520.v9i2.55007

Fadillah, R., Qadriah, L., & Rizal, M. (2023). Market basket analysis data mining untuk mengetahui pola penjualan pada Cerry Mart Beureunueun menggunakan algoritma Apriori. Jurnal Real Riset, 5(1), 234-239. https://doi.org/10.47647/jrr.v5i1.1152

Hossain, S., Hena, H., & Sampa, P. (2024). Decoding consumer habits: Analyzing retail patterns across demographics. Startupreneur Business Digital (SABDA Journal), 3(2), 148-159. https://doi.org/10.33050/sabda.v3i2.638

Mubarok, D., Adjani, K., Ridho Hutama, B. D., Muhamad Malik Mutoffar, & Rina Indrayani. (2025). Big data analytics dan machine learning untuk memprediksi perilaku konsumen di e-commerce. Jurnal Informatika Dan Rekayasa Elektronik, 8(1), 159-167. https://doi.org/10.36595/jire.v8i1.1561

Prasetyo, S. S., Mustafid, M., & Hakim, A. R. (2020). Penerapan fuzzy c-means kluster untuk segmentasi pelanggan e-commerce dengan metode recency frequency monetary (RFM). Jurnal Gaussian, 9(4), 421-433. https://doi.org/10.14710/j.gauss.v9i4.29445

Riwayat, A. A. P., Susilawati, A. D., & Naqiah, Z. (2024). Purchasing patterns analysis in e-commerce: A big data-driven approach and methodological. International Journal Software Engineering and Computer Science (IJSECS), 4(1), 148-164. https://doi.org/10.35870/ijsecs.v4i1.2384

Salsabila, S., & Novita Dewi, I. (2025). Integrasi algoritma FP-Growth dan K-Means untuk analisis keranjang belanja dan segmentasi pelanggan pada data transaksi ritel. Jurnal Nasional Teknologi Dan Sistem Informasi, 11(2), 128-135. https://doi.org/10.25077/TEKNOSI.v11i2.2025.128-135

Sari, R. (2021). Pengaruh penggunaan paylater terhadap perilaku impulse buying pengguna e-commerce di Indonesia. Jurnal Riset Bisnis Dan Investasi, 7(1), 44-57. https://doi.org/10.35313/jrbi.v7i1.2058

Siagian, R., Pahala Sirait, P. S., & Halima, A. (2021). E-commerce customer segmentation using K-Means algorithm and length, recency, frequency, monetary model. JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING, 5(1), 21-30. https://doi.org/10.31289/jite.v5i1.5182

Siagian, R., Sirait, P., & Halim, A. (2022). The implementation of K-Means dan K-Medoids algorithm for customer segmentation on e-commerce data transactions. SISTEMASI, 11(2), 260. https://doi.org/10.32520/stmsi.v11i2.1337

Sumita Wardani, Saidan Sany Lubis, & Rico Wijaya Dewantoro. (2025). Analisis big data untuk prediksi permintaan produk dalam e-commerce. Jurnal Penelitian Teknologi Informasi Dan Sains, 3(1), 74-81. https://doi.org/10.54066/jptis.v3i1.3066

Sutisna, N. & Sutrisna. (2023). Implementasikan sistem informasi dalam mendukung perilaku pembelian terhadap keputusan pembelian e-commerce. Jurnal MENTARI: Manajemen, Pendidikan Dan Teknologi Informasi, 2(1), 20-30. https://doi.org/10.33050/mentari.v2i1.343

Sylvia, M. L., & Terhaar, M. F. (2023). Clinical analytics and data management for the DNP (3rd ed., pp. 978-0-8261-6324-0). Springer Publishing Company. https://doi.org/10.1891/9780826163240

Weyant, E. (2022). Research design: Qualitative, quantitative, and mixed methods approaches, 5th edition: by John W. Creswell and J. David Creswell, Los Angeles, CA: SAGE, 2018, $38.34, 304pp., ISBN: 978-1506386706. Journal of Electronic Resources in Medical Libraries, 19(1-2), 54-55. https://doi.org/10.1080/15424065.2022.2046231

Downloads

Published

2026-02-10