Utilization of Natural Language Processing for AIDA Formula-Based Copywriting Text Generation

Penulis

  • Adi Ahmad STMIK Indonesia Banda Aceh

DOI:

https://doi.org/10.55123/jomlai.v5i2.9280

Kata Kunci:

Natural Language Processing, Digital Copywriting, AIDA Formula, Artificial Intelligence, Text Generation

Abstrak

The need for rapid and persuasive digital promotional content has increased, yet manual copywriting text generation often consumed time and exhibited inconsistent quality. This study proposed a solution by utilizing Natural Language Processing (NLP) technology to automatically generate copywriting texts. The utilized approach focused on applying the AIDA (Attention, Interest, Desire, Action) formula to ensure the generated texts remained structured and possessed high marketing appeal. An artificial intelligence-based model was tested to produce text outputs based on specific parameters and keywords. The testing results indicated that the model successfully generated promotional scripts that accurately followed the AIDA structure, possessed natural grammar, and were highly relevant to the offered product context. The conclusion of this study affirmed that the utilization of natural language processing in digital content creation proved effective in improving production time efficiency while maintaining the persuasive quality of the texts. This technology provided a significant contribution to accelerating the digital marketing script production workflow.

Referensi

[1] K. Marzuki, A. Setyanto, and A. Nasiri, "Audit Tata Kelola Teknologi Informasi Menggunakan COBIT 4.1 Domain Monitoring Evaluasi pada Perguruan Tinggi Swasta," BITe: Journal Bumigora Information Technology, vol. 3, no. 3, pp. 1-10, Nov. 2021.

[2] R. K. Sharma and M. Patel, "The Role of Persuasive Copywriting in Modern E-Commerce Marketing," Journal of Digital Marketing Strategy, vol. 5, no. 2, pp. 45-58, Aug. 2023.

[3] A. B. Pratama dan S. Rahayu, "Analisis Efisiensi Waktu dan Biaya Pembuatan Konten Promosi Digital pada UMKM," Jurnal Manajemen dan Pemasaran Digital, vol. 2, no. 1, hal. 12-22, Jan. 2024.

[4] Y. LeCun, Y. Bengio, and G. Hinton, "Deep Learning," Nature, vol. 521, no. 7553, pp. 436-444, May 2015.

[5] T. Brown et al., "Language Models are Few-Shot Learners," in Advances in Neural Information Processing Systems (NeurIPS), vol. 33, pp. 1877-1901, Dec. 2020.

[6] A. Vaswani et al., "Attention is All You Need," in Proc. 31st Int. Conf. Neural Information Processing Systems (NIPS), Dec. 2017, pp. 6000-6010.

[7] M. H. Lee, "Evaluation of Automated Text Generation Tools for Commercial Content Creation," IEEE Transactions on Computational Social Systems, vol. 10, no. 4, pp. 1120-1131, Aug. 2023.

[8] S. Gupta and R. Verma, "Integrating Consumer Behavior Psychological Models into AI Text Generators," International Journal of Human-Computer Interaction, vol. 39, no. 8, pp. 1540-1552, Apr. 2023.

[9] E. K. Strong, The Psychology of Selling and Advertising. New York: McGraw-Hill, 1925.

[10] D. A. Aaker, Strategic Market Management, 11th ed. Hoboken, NJ: John Wiley & Sons, 2017.

[11] H. Wijaya dan S. Kurniawan, "Penerapan Formula AIDA dalam Optimasi Konten Iklan Digital," Jurnal Komunikasi Pemasaran, vol. 8, no. 2, hal. 89-98, Jul. 2022.

[12] C. D. Manning, P. Raghavan, and H. Schütze, An Introduction to Information Retrieval. Cambridge: Cambridge University Press, 2008.

[13] R. Hidayat, M. F. Arifin, dan T. H. Putra, "Metodologi Penelitian Sistem Informasi Berbasis Kecerdasan Artifisial," Jurnal Teknologi Informasi dan Ilmu Komputer, vol. 11, no. 1, hal. 55-64, Feb. 2024.

[14] F. Zhao, L. Meng, and X. Chen, "Dataset Creation and Benchmark for Automated Copywriting in E-Commerce," IEEE Access, vol. 11, pp. 34520-34531, Apr. 2023.

[15] A. K. Joshi, "Natural Language Processing," Science, vol. 253, no. 5025, pp. 1242-1249, Sep. 1991.

[16] L. Zheng, H. Wang, and Y. Zhang, "Prompt Engineering Techniques for Controlled Text Generation in Marketing," ACM Transactions on Intelligent Systems and Technology, vol. 15, no. 3, pp. 1-22, Jun. 2024.

[17] K. Papineni, S. Roukos, T. Ward, and W. J. Zhu, "BLEU: A Method for Automatic Evaluation of Machine Translation," in Proc. 40th Annual Meeting of the Association for Computational Linguistics (ACL), Jul. 2002, pp. 311-318.

[18] C. Y. Lin, "ROUGE: A Package for Automatic Evaluation of Summaries," in Text Summarization Branches Out, Barcelona, Spain, Jul. 2004, pp. 74-81.

Unduhan

Diterbitkan

2026-06-15

Cara Mengutip

Adi Ahmad. (2026). Utilization of Natural Language Processing for AIDA Formula-Based Copywriting Text Generation. JOMLAI: Journal of Machine Learning and Artificial Intelligence, 5(2), 89–96. https://doi.org/10.55123/jomlai.v5i2.9280

Terbitan

Vol 5 No 2 (2026): Juni 2026

Bagian

Articles

Lisensi

Hak Cipta (c) 2026 Adi Ahmad

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