Analisis Bibliometrik Tren Penelitian Deep Learning untuk Perilaku Konsumen
DOI:
https://doi.org/10.32664/elang.v4i1Kata Kunci:
Analisis Bibliometrik, Deep Learning, Perilaku Konsumen, Pembelian Impulsif, VosViewerAbstrak
Perkembangan teknologi deep learning yang pesat telah mendorong transformasi besar dalam analisis perilaku konsumen, namun arah perkembangan literatur pada bidang ini belum terpetakan secara komprehensif. Penelitian ini bertujuan untuk menganalisis tren perkembangan riset terkait pemanfaatan deep learning dalam klasifikasi perilaku konsumen menggunakan pendekatan bibliometrik. Data diambil dari basis data OpenAlex hingga Juni 2026, menghasilkan 411 dokumen relevan yang kemudian dianalisis menggunakan perangkat lunak VOSviewer dengan teknik co-occurrence. Temuan penelitian menunjukkan adanya lonjakan publikasi yang signifikan sejak tahun 2020 hingga tahun berjalan 2026, dengan volume dokumen terindeks tertinggi berada pada tahun 2025 (data bersifat parsial dan masih dalam proses agregasi). Analisis klaster menunjukkan dominasi bidang computer science (326 kemunculan) dan artificial intelligence (238 kemunculan). Hasil pemetaan kata kunci mengidentifikasi keterkaitan kuat antara teknologi kecerdasan buatan dengan strategi bisnis modern, namun menunjukkan adanya kesenjangan pada kajian psikologis yang lebih spesifik seperti perilaku pembelian impulsif (impulsive buying). Secara praktis, penelitian ini memberikan landasan bagi pengembang strategi bisnis untuk mengintegrasikan model komputasi yang lebih akurat dengan teori perilaku nyata. Implikasi dari penelitian ini diharapkan dapat mendorong pengembangan riset interdisipliner yang lebih seimbang antara kekuatan teknis algoritma dan pemahaman psikologis konsumen di masa depan.
Referensi
Albawi, S., Mohammed, T. A., & Al-Zawi, S. (2017). Understanding of a Convolutional Neural Network. 2017 International Conference on Engineering and Technology (ICET), 1–6. https://doi.org/10.1109/ICEngTechnol.2017.8308186
Alzahrani, M. E., Aldhyani, T. H. H., Alsubari, S. N., Althobaiti, M. M., & Fahad, A. (2022). Developing an Intelligent System with Deep Learning Algorithms for Sentiment Analysis of E-Commerce Product Reviews. Computational Intelligence and Neuroscience, 2022, 3840071. https://doi.org/10.1155/2022/3840071
Aria, M., & Cuccurullo, C. (2017). bibliometrix: An R-tool for Comprehensive Science Mapping Analysis. Journal of Informetrics, 11(4), 959–975. https://doi.org/10.1016/j.joi.2017.08.007
Balakrishnan, V., Khan, S., & Arabnia, H. (2022). Deep Learning Approaches for Consumer Sentiment Analysis in Social Media. Expert Systems with Applications, 198, 116785. https://doi.org/10.1016/j.eswa.2022.116785
Bhuvaneshwari, P., Rao, A. N., Robinson, Y. H., & Thippeswamy, M. N. (2022). Sentiment Analysis for User Reviews Using Bi-LSTM Self-Attention Based CNN Model. Multimedia Tools and Applications, 81, 12405–12419. https://doi.org/10.1007/s11042-022-12410-4
Cobos, C. (2011). A Hybrid Software Tool for the Bibliometric Analysis of Scientific Domains. 4th International Conference on Soft Computing, 345–352.
Ellegaard, O., & Wallin, J. A. (2015). The Bibliometric Analysis of Scholarly Production: How to Build a Reference Library in a Specific Topic. Scientometrics, 105(3), 1809–1831. https://doi.org/10.1007/s11192-015-1645-z
Geethanjali, N., & Valarmathi, M. (2024). Hybrid Deep Learning Model for Multilingual Social Media Sentiment Analysis. Journal of Big Data, 11(1), 55. https://doi.org/10.1186/s40537-024-00812-5
Jain, P. K., Saravanan, V., & Pamula, R. (2021). A Hybrid CNN-LSTM: A Deep Learning Approach for Consumer Sentiment Analysis Using Qualitative User-Generated Contents. ACM Transactions on Asian and Low-Resource Language Information Processing, 20(5), 1–15. https://doi.org/10.1145/3457206
Kim, Y. (2014). Convolutional Neural Networks for Sentence Classification. Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), 1746–1751. https://doi.org/10.3115/v1/D14-1181
Koehn, D., Lessmann, S., & Schaal, M. (2020). Predicting Online Shopping Behaviour from Clickstream Data Using Deep Learning. Expert Systems with Applications, 150, 113342. https://doi.org/10.1016/j.eswa.2020.113342
Liu, Y., Lu, J., Yang, J., & Mao, F. (2020). Sentiment Analysis for E-Commerce Product Reviews by Deep Learning Model of Bert-BiGRU-Softmax. Mathematical Biosciences and Engineering, 17(6), 7819–7837. https://doi.org/10.3934/mbe.2020398
Moher, D., Liberati, A., Tetzlaff, J., & Altman, D. G. (2009). Preferred Reporting Items for Systematic Reviews and Meta-Analyses: The PRISMA Statement. PLoS Medicine, 6(7), e1000097. https://doi.org/10.1371/journal.pmed.1000097
Mongeon, P., & Paul-Hus, A. (2016). The Journal Coverage of Web of Science and Scopus: A Comparative Analysis. Scientometrics, 106(1), 213–228. https://doi.org/10.1007/s11192-015-1765-5
Moral-Muñoz, J. A., Herrera-Viedma, E., Santisteban-Espejo, A., & Cobo, M. J. (2020). Software Tools for Conducting Bibliometric Analysis in Science: An Up-to-Date Review. El Profesional de La Información, 29(1). https://doi.org/10.3145/epi.2020.ene.03
Small, H. (1973). Co-citation in the Scientific Literature: A New Measure of the Relationship Between Two Documents. Journal of the American Society for Information Science, 24(4), 265–269. https://doi.org/10.1002/asi.4630240406
Subramanyam, K. (1983). Bibliometric Studies of Scientific Literature. Journal of Information Science, 6(1), 33–37. https://doi.org/10.1177/016555158300600105
Tan, Q. L., Hashim, S., & Abdullah, N. L. (2025). Mapping the Knowledge-Consumer Behavior Nexus: A Bibliometric Analysis and Systematic Review. Humanities and Social Sciences Communications, 12, 1243. https://doi.org/10.1057/s41599-025-05613-1
van Eck, N. J., & Waltman, L. (2010). Software Survey: VOSviewer, a Computer Program for Bibliometric Mapping. Scientometrics, 84(2), 523–538. https://doi.org/10.1007/s11192-009-0146-3
Unduhan
Diterbitkan
Terbitan
Bagian
Lisensi
Hak Cipta (c) 2026 ELANG: Journal of Interdisciplinary Research
Artikel ini berlisensiCreative Commons Attribution-ShareAlike 4.0 International License.
