ANALISIS DAN PEMODELAN PURCHASE INTENTION KONSUMEN MENGGUNAKAN ALGORITMA ARTIFICIAL INTELLIGENCE PADA E-COMMERCE MARKETPLACE

Intan  Oktaviani; Triyono Triyono; Triana Triana

doi:10.32500/jebe.v7i2.10976

Authors

Intan Oktaviani Universitas Duta Bangsa Surakarta
Triyono Triyono Universitas Duta Bangsa Surakarta
Triana Triana Universitas Duta Bangsa Surakarta

DOI:

https://doi.org/10.32500/jebe.v7i2.10976

Keywords:

Artificial Intelligence, Machine Learning, Purchase Intention, E-Commerce, Marketplace

Abstract

Dengan pertumbuhan pesat e-commerce, banyak data perilaku pelanggan telah dikumpulkan, tetapi masih belum optimal untuk memprediksi niat pembelian pelanggan. Dengan menggunakan algoritma kecerdasan buatan (AI) pada platform pasar, penelitian ini bertujuan untuk mengembangkan model prediksi minat beli konsumen. Model ini dibangun dengan memanfaatkan algoritma pengajaran mesin seperti Random Forest, Support Vector Machine (SVM), dan regresi logistik. Algoritma ini menganalisis data perilaku konsumen, yang mencakup jumlah halaman produk yang dilihat, durasi kunjungan, dan riwayat interaksi dengan produk. Penelitian dimulai dengan pengumpulan data tentang perilaku konsumen. Kemudian, data dibersihkan dan dinormalisasi melalui preprocessing, dan setelah itu, ketiga algoritma tersebut digunakan untuk memodelkan data. Untuk menilai model, metrik akurasi, ketepatan, recall, dan skor F1 digunakan. Hasil penelitian menunjukkan bahwa algoritma Random Forest adalah yang terbaik dengan akurasi 91,27%, lebih tinggi dari SVM (88,45%) dan Logistic Regression (85,72%). Model ini tidak hanya berhasil memprediksi minat beli konsumen, tetapi juga memberikan pengetahuan yang dapat digunakan untuk meningkatkan strategi pemasaran dan konversi penjualan di pasar. Mereka menekankan betapa pentingnya durasi interaksi dan nilai halaman produk bagi konsumen dalam membuat keputusan pembelian. Penelitian ini menawarkan manfaat praktis bagi pengelola e-commerce dalam mengembangkan strategi pemasaran berbasis data yang meningkatkan kepuasan pelanggan dan efektivitas promosi.

References

-, R. S., -, S. S., & -, S. F. (2023). E-Commerce and Digital Transformation: Trends, Challenges, and Implications. International Journal For Multidisciplinary Research, 5(5), 1–9. https://doi.org/10.36948/ijfmr.2023.v05i05.7128

A.A. Nimbalkar, & A.T., B. (2021). The Increasing Importance of AI Applications in E-Commerce. Vidyabharati International Interdisciplinary Research Journal, 13(1), 388–391.

Aftab, M. O., & Ahmad, U. (2021). Sentiment Analysis of Customer for Ecommerce by Applying AI. Icic.

Ahmed, H., Youssef, H., Tamer, A., & Hossam, A. (2023). Privacy Issues in AI and Cloud Computing in E-commerce Setting: A Review. International Journal of Responsible Artificial Intelligence, 13(7), 37–46.

Aliyev, A. G., Shahverdiyeva, R. O., & Hagverdiyeva, U. H. (2024). Modernization of E-Commerce and Logistics Platforms of Enterprises Based on Artificial Intelligence Technology. Advances in Transdisciplinary Engineering, 48, 170–181. https://doi.org/10.3233/ATDE231327

Anderson, J., & Johnson, D. (2024). EasyChair Preprint The Role of Artificial Intelligence in Enhancing E-Commerce Customer Experience The Role of Artificial Intelligence in Enhancing E-commerce Customer Experience.

Ayyapparajan, R. A., & Sabeena, S. (2022). Impact of Artificial Intelligence in E-Commerce. 24(8), 315–321.

Botana, I. L., & Eiras-franco, C. (2020). Regression Tree Based Explanation for Anomaly Detection Algorithm †. 5–8. https://doi.org/10.3390/proceedings2020054007

Chandramana, S. (2023). AI in Retail Industry: Reshaping Shopping Experience and Business Profitability. Ushus Journal of Business Management, 21(4), 41–50. https://doi.org/10.12725/ujbm/61.3

Chung, M., Chignell, M., Wang, L., Jovicic, A., & Raman, A. (2020). Interactive Machine Learning for Data Exfiltration Detection : Active Learning with Human Expertise.

Cosenz, F., & Bivona, E. (2020). Fostering growth patterns of SMEs through business model innovation . A tailored dynamic business modelling approach. Journal of Business Research, March, 1–12. https://doi.org/10.1016/j.jbusres.2020.03.003

Enache, M.-C. (2019). Machine Learning in E-commerce. Annals of Dunarea de Jos University of Galati. Fascicle I. Economics and Applied Informatics, 25(1), 169–173. https://doi.org/10.35219/eai1584040920

Febriana Sulistya Pratiwi. (2022). No Titleהכי קשה לראות את מה שבאמת לנגד העינים. הארץ, 8.5.2017, 2003–2005. https://dataindonesia.id/sektor-riil/detail/angka-konsumsi-ikan-ri-naik-jadi-5648-kgkapita-pada-2022

Fetaji, M., Sciences, C., Macedonia, N., Fetaji, B., Macedonia, N., Snopce, H., Sciences, C., & Macedonia, N. (2020). Devising a SME Model for developing Mobile m- Commerce for Small and Medium-Sized Enterprises. 1–5.

Flavián, C., Guinalíu, M., & Gurrea, R. (2006). The role played by perceived usability, satisfaction and consumer trust on website loyalty. Information and Management, 43(1), 1–14. https://doi.org/10.1016/j.im.2005.01.002

Huo, Z. (2021). Sales Prediction based on Machine Learning. 410–415. https://doi.org/10.1109/ECIT52743.2021.00093

Jain, A., & Karthikeyan, V. (2020). Demand Forecasting for E-Commerce Platforms. 1–4.

Jing, H., Wei, W., Zhou, C., & He, X. (2021). An Artificial Intelligence Security Framework. https://doi.org/10.1088/1742-6596/1948/1/012004

Khan, M. M. (2020). Development of An e-commerce Sales Chatbot. 173–176.

Khrais, L. T. (2020). Role of Artificial Intelligence in Shaping Consumer Demand in E-Commerce.

Kim, H., Lee, Y., Lee, E., & Lee, T. (2021). Cost-Effective Valuable Data Detection Based on the Reliability of Artificial Intelligence. IEEE Access, 9, 108959–108974. https://doi.org/10.1109/ACCESS.2021.3101257

Klaus, P., Lynne, J., & Klaus, P. (2021). Journal of Retailing and Consumer Services The convenience of shopping via voice AI : Introducing AIDM. Journal of Retailing and Consumer Services, xxxx, 102490. https://doi.org/10.1016/j.jretconser.2021.102490

Kulshrestha, S. (2020). Study for the Prediction of E-Commerce Business Market Growth Using Machine Learning Algorithm. 2020.

Kumar, A., & Kalse, A. (2021). Materials Today : Proceedings Usage and adoption of artificial intelligence in SMEs. Materials Today: Proceedings, xxxx. https://doi.org/10.1016/j.matpr.2021.01.595

Kumar, B. . P., & Sree, D. K. S. (2021). Virtualization-Based Digitization of a Retail Store: An Enhanced Implementation of Digital Transformation. International Journal of Innovative Technology and Exploring Engineering, 10(11), 133–136. https://doi.org/10.35940/ijitee.k9488.09101121

Kuppa, A. (2020). Black Box Attacks on Explainable Artificial Intelligence ( XAI ) methods in Cyber Security. 1–8.

Lstm, A. L. S. M. (2020). Prediction of online consumers ’ buying behavior based on LSTM-RF model.

Luo, Y., Xu, S., & Xie, C. (2022). E-commerce Big Data Classification and Mining Algorithm based on Artificial Intelligence. 1153–1155.

Micol, L., Eugênio, D., Righi, R., Antunes, R., André, C., Luis, J., Barbosa, V., Scorsatto, R., & Arcot, T. (2021). Machine learning through the lens of e-commerce initiatives : An up-to-date systematic literature review. Computer Science Review, 41, 100414. https://doi.org/10.1016/j.cosrev.2021.100414

Mouratidis, H. (2010). International Journal of Computer Science and Security. International Journal of Computer Science and Security, 4(1), 1–3.

MYKYTENKO, N., & RZAIEVA, S. (2024). Application of artificial intelligence in retail. International Scientific-Practical Journal Commodities and Markets, 50(2), 4–20. https://doi.org/10.31617/2.2024(50)01

Nakata, D., & Smith, J. (2024). Transforming E-commerce: How AI Revolutionizes Customer Experience.

Nazim Sha, S., & Rajeswari, M. (2019). Creating a brand value and consumer satisfaction in E-commerce business using artificial intelligence with the help of vosag technology. International Journal of Innovative Technology and Exploring Engineering, 8(8), 1510–1515.

Pawłowski, M., & Pawłowski, M. (2021). Machine Learning Based Product Classification for eCommerce Machine Learning Based Product Classification for eCommerce. Journal of Computer Information Systems, 00(00), 1–10. https://doi.org/10.1080/08874417.2021.1910880

Qu, S., Shi, H., Zhao, H., Yu, L., & Yu, Y. (2021). Research on enterprise business model and technology innovation based on artificial intelligence. EURASIP Journal on Wireless Communications and Networking. https://doi.org/10.1186/s13638-021-02025-y

Rahman, S. S., & Dekkati, S. (2022). Revolutionizing Commerce: The Dynamics and Future of E-Commerce Web Applications. Asian Journal of Applied Science and Engineering, 11(1), 65–73. https://doi.org/10.18034/ajase.v11i1.58

Rosa, G. H. De, & Papa, J. P. (2021). A survey on text generation using generative adversarial networks. Pattern Recognition, 119, 108098. https://doi.org/10.1016/j.patcog.2021.108098

Ruan, S., & Zhao, T. (2024). JungleGPT: Designing and Optimizing Compound AI Systems for E-Commerce. https://huggingface.co/croissantllm/

S., D., & R., S. (2022). Digital transformation in Retail Industry. Cardiometry, 24, 859–866. https://doi.org/10.18137/cardiometry.2022.24.859866

Saeedi, S., & Carr, S. (2021). Consumer Artificial Intelligence Mishaps and Mitigation Strategies. 2248(1), 1–12. https://doi.org/10.1109/MCE.2021.3075329

Sejr, J. H., Zimek, A., & Schneider-kamp, P. (2020). Explainable Detection of Zero Day Web Attacks. 71–78. https://doi.org/10.1109/ICDIS50059.2020.00016

Series, C. (2020). E-Commerce System for Sale Prediction Using Machine Learning Technique E-COMMERCE SYSTEM FOR SALE PREDICTION. https://doi.org/10.1088/1742-6596/1712/1/012042

Sharma, S. (2023). Implementing Automation and AI in Small Businesses. AI for Small Business Leveraging Automation to Stay Ahead, 28–56. https://doi.org/10.46679/978819573223403

Shrirame, V. (2020). Consumer Behavior Analytics using Machine Learning Algorithms. Cd, 5–10.

Srivastava, A. (2021). THE APPLICATION & IMPACT OF ARTIFICIAL INTELLIGENCE ( AI ) ON E-COMMERCE Mr . Ambar Srivastava Assistant Professor Eternal University , Baru Sahib , Himachal Pradesh 1 Introduction Artificial Intelligence ( AI ) is an area of computer science designed to . November.

Tianyi, H. (n.d.). Integration Mechanism of Artificial Intelligence Technology Innovation and Commercialization. 1467–1475.

Vilone, G., & Longo, L. (2021). Classification of Explainable Artificial Intelligence Methods through Their Output Formats. 615–661.

Wang, J. (2021). Research on the Construction of Integrated Management Information System for E-commerce Enterprises Based on Cloud Computing. 1039–1042.

Yildiz, Z. O. (2020). The Emerging AI Policy for e-commerce Industry. 66–70.

Yildiz, Z. O., & Beloff, N. (2020). The Emerging AI Policy for e-commerce Industry. ACM International Conference Proceeding Series, 66–70. https://doi.org/10.1145/3385209.3385210

Yin, J., & Qiu, X. (2021). AI Technology and Online Purchase Intention : Structural Equation Model Based on Perceived Value Artificial Intelligence Winning Consumers : Enabling Human Intelligence ”. According to.

Yoon, N., & Lee, H. (2021). AI Recommendation Service Acceptance : Assessing the Effects of Perceived Empathy and Need for Cognition. 1912–1928.

Zabranskyi, M. (2024). Challenges and Prospects of Implementing Ai in Strategic Management. 74–75. https://doi.org/10.36074/logos-24.05.2024.013

Zhang, D., Pee, L. G., & Cui, L. (2021). Artificial intelligence in E-commerce fulfillment: A case study of resource orchestration at Alibaba’s Smart Warehouse. International Journal of Information Management, 57(December 2020), 102304. https://doi.org/10.1016/j.ijinfomgt.2020.102304

Zhang, Y., Liu, Z., & Sang, C. (2021). Unifying paragraph embeddings and neural collaborative filtering for. Applied Soft Computing Journal, 106, 107345. https://doi.org/10.1016/j.asoc.2021.107345