Big Data Analysis to Predict Consumer Behaviour Patterns in E-Commerce Using Deep Learning
Keywords:
Big Data, Consumer Behavior Patterns, Deep LearningAbstract
The rapid growth of e-commerce in Indonesia presents new challenges in understanding increasingly complex and dynamic consumer behavior patterns. Despite the availability of millions of data points—transactions, searches, clicks, and reviews—these are not yet optimally utilized to enhance service accuracy and marketing strategies. This study aims to analyze the potential of big data in mapping and predicting consumer behavior patterns, to develop and test deep learning models, and to provide personalized service strategy recommendations for e-commerce platforms. A descriptive qualitative approach was used, with data collected through in-depth interviews, direct observations, and documentation. Informants included data managers, active e-commerce users, and AI development teams from relevant companies. Data were analyzed using the Miles and Huberman model involving reduction, display, and conclusion drawing, validated by source triangulation. The findings reveal that the implementation of deep learning in big data analysis is still limited, with most e-commerce platforms relying on traditional analytical methods. However, integrating big data with deep learning significantly enhances the accuracy of behavior predictions and the relevance of product recommendations. This research highlights that Indonesian e-commerce holds immense potential to improve operational efficiency and user experience through deep learning-based personalization strategies, provided adequate infrastructure and resources are in place
References
Al-ramahi, N., Odeh, M., Khanfar, I., Qoazmar, N., Hamdan, A., Alsabatin, H., & Kanan, M. (2024). The Effects of Innovative Technology on Quality Assurance in Higher Education Institutions in Developing Countries: A Case Study of Jordan. Dalam R. E. Khoury & N. Nasrallah (Ed.), Intelligent Systems, Business, and Innovation Research (hlm. 253–263). Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-36895-0_21
Amanda, D. P., & Absharina, E. D. (2025). IMPLEMENTASI AI-POWERED INTRUSION DETECTION SYSTEMS UNTUK MENDETEKSI ANCAMAN KEAMANAN PADA BIG DATA. Simtek: jurnal sistem informasi dan teknik komputer, 10(1), 29–33. https://doi.org/10.51876/simtek.v10i1.1381
Amory, J. D. S., & Mudo, M. (2025). Transformasi ekonomi digital dan evolusi pola konsumsi: Tinjauan literatur tentang perubahan perilaku belanja di era internet. Jurnal Minfo Polgan, 14(1), 28–37. https://doi.org/10.33395/jmp.v14i1.14608
Breutner, N. (2024). Introduction: Objective of this Research. Dalam Managing Workspace Changes: How Organizations Use Innovative Workspace Concepts to Facilitate Organizational Change (hlm. 1–25). Springer Fachmedien Wiesbaden. https://doi.org/10.1007/978-3-658-46692-3_1
Chaudhary, P. S., Khurana, M. R., & Ayalasomayajula, M. (2024). Real-World Applications of Data Analytics, Big Data, and Machine Learning. Dalam P. Singh, A. R. Mishra, & P. Garg (Ed.), Data Analytics and Machine Learning: Navigating the Big Data Landscape (hlm. 237–263). Springer Nature Singapore. https://doi.org/10.1007/978-981-97-0448-4_12
Darman, R. (2024). Sertipikat Tanah VS Sertifikat Tanah: Analisis Data Penggunaan Istilah Produk Hukum di Media Sosial. Journal Islamic Global Network for Information Technology and Entrepreneurship, 2(3), 62–78. https://doi.org/10.59841/ignite.v2i3.1758
Ibna, A. Z., & Nasution, M. I. P. (2024). Implikasi Penggunaan Basis Data dalam Era Big Data. Journal Sains Student Research, 2(4), 255–265. https://doi.org/10.61722/jssr.v2i4.1995
Karl, D. (2024). Forecasting e-commerce consumer returns: A systematic literature review. Management Review Quarterly. https://doi.org/10.1007/s11301-024-00436-x
Kreutzer, R. T. (2022). Eight Fields of Action for Building Digital Excellence. Dalam Toolbox Digital Business: Leadership, Business Models, Technologies and Change (hlm. 121–497). Springer Fachmedien Wiesbaden. https://doi.org/10.1007/978-3-658-37017-6_3
Kumar, V. (2023). The Might of MAAMA. Dalam The Economic Value of Digital Disruption: A Holistic Assessment for CXOs (hlm. 525–688). Springer Nature Singapore. https://doi.org/10.1007/978-981-19-8148-7_7
Lestari, D. A., & Nasution, M. I. P. (2025). PENGELOLAAN BIG DATA: INOVASI SOLUSI DAN TANTANGAN DALAM ERA INFORMASI MODERN. JOURNAL SAINS STUDENT RESEARCH, 3(3), 502–511. https://doi.org/10.61722/jssr.v3i3.4818
Mageed, I. A., Bhat, A. H., & Edalatpanah, S. A. (2024). Shallow Learning vs. Deep Learning in Finance, Marketing, and e-Commerce. Dalam Ö. F. Ertuğrul, J. M. Guerrero, & M. Yilmaz (Ed.), Shallow Learning vs. Deep Learning: A Practical Guide for Machine Learning Solutions (hlm. 77–91). Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-69499-8_3
Mertaningrum, N. L. P. E., Giantari, I. G. A. K., Ekawati, N. W., & Setiawan, P. Y. (2023). Perilaku belanja impulsif secara online. Jurnal Ilmu Sosial Dan Humaniora, 12(3), 605–616. https://doi.org/10.23887/jish.v12i3.70463
Muchlis, M., Agustia, D., & Narsa, I. M. (2021). Pengaruh Teknologi Big Data Terhadap Nilai Perusahaan Melalui Kinerja Keuangan Perusahaan Di Bursa Efek Indonesia. EKUITAS (Jurnal Ekonomi Dan Keuangan), 5(2), 139–158. https://doi.org/10.24034/j25485024.y2021.v5.i2.4928
Muhamad, A., & Sumiah, A. (2025). ANALISIS IMPLEMENTASI APLIKASI BIG DATA PADA INDUSTRI KESEHATAN, KEUANGAN DAN PENDIDIKAN. Digital Business and Entrepreneurship Journal, 3(1), 24–35. https://doi.org/10.25134/digibe.v3i1.256
Muslim, M., & Permatasari, D. (2024). Strategi dan Peluang Indonesia dalam Kerja Sama BRICS untuk Memperkuat Keamanan Ekonomi Nasional. Jurnal Keamanan Nasional, 10(2), 205–234. https://doi.org/10.31599/s7aaj612
Nicoletti, B. (2021). Platforms for Banking 5.0. Dalam Banking 5.0: How Fintech Will Change Traditional Banks in the “New Normal” Post Pandemic (hlm. 231–301). Springer International Publishing. https://doi.org/10.1007/978-3-030-75871-4_8
Raup, A., Ridwan, W., Khoeriyah, Y., Supiana, S., & Zaqiah, Q. Y. (2022). Deep Learning dan Penerapannya dalam Pembelajaran. JIIP-Jurnal Ilmiah Ilmu Pendidikan, 5(9), 3258–3267. https://doi.org/10.54371/jiip.v5i9.805
Shaban, A. (2024). New Digital Economic Geography. Dalam A. Shaban (Ed.), Digital Geographies—Urbanisation, Economy, and Modelling: A Machine-Generated Literature Review (hlm. 599–833). Springer Nature Singapore. https://doi.org/10.1007/978-981-97-9278-8_6
Sopang, F. I. (2021). Analisis Faktor-Faktor Yang Mempengaruhi Konsumen Dalam Pengambilan Keputusan Pembelian Produk Mie Instan (Studi Pada Mahasiswa Fakultas Ekonomi Universitas Dharmawangsa). Journal Economy and Currency Study (JECS), 3(2), 24–36. https://doi.org/10.51178/jecs.v3i2.291
Vitolla, F., Raimo, N., Marrone, A., & Rubino, M. (2025). Barriers and Benefits of Implementing Circular Economy Models: A Case Study Analysis. Dalam N. Castellano, F. De Luca, G. D’Onza, M. Maffei, & A. Melis (Ed.), Environmental, Social, Governance (ESG): Risk, Performance, Monitoring (hlm. 739–758). Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-76618-3_35
Wahida, N., Parakkasi, I., & Sudirman, S. (2024). Perilaku Konsumen dalam Ekonomi Islam. ADILLA: Jurnal Ilmiah Ekonomi Syari’ah, 7(2), 151–169. https://doi.org/10.52166/adilla.v7i2.6556
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