Pengembangan sistem pendukung keputusan berbasis AI untuk evaluasi ide konten digital pra-produksi
DOI:
https://doi.org/10.24246/itexplore.v5i1.2026.pp109-118Keywords:
Kecerdasan Buatan, Sistem Pendukung Keputusan, Ide Konten Digital, Media Sosial, Design Science ResearchAbstract
The rapid growth of short-form social media platforms has increased the complexity of decision-making during the digital content planning stage. Content creators are required to evaluate the feasibility of content ideas and determine platform suitability prior to production, while most existing tools primarily focus on post-publication analytics. This study aims to design an Artificial Intelligence (AI)-enabled Decision Support System (DSS) to evaluate digital content ideas in the pre-production stage. Adopting a Design Science Research approach, the study develops a conceptual design artifact that integrates intrinsic content idea characteristics with cognitive and affective response modeling grounded in the Stimulus–Organism–Response (S-O-R) framework, alongside platform affordance mapping. The proposed artifact operationalizes a reflective evaluation mechanism that generates platform recommendation scores and idea enhancement suggestions without claiming deterministic or predictive performance modeling. Evaluation was conducted qualitatively through practitioner assessment to examine perceived usefulness, clarity of recommendations, and decision support contribution. The findings indicate that the developed artifact provides a structured reflective framework for early-stage content evaluation. Theoretically, this study extends the application of the S-O-R framework by operationalizing it as a design logic for a pre-production DSS artifact. Practically, the proposed system has the potential to support more systematic decision-making prior to content production.
Downloads
References
X. Dong, H. Liu, N. Xi, J. Liao, and Z. Yang, “Short video marketing: What, when and how short-branded videos facilitate consumer engagement,” Internet Research, vol. 34, no. 3, pp. 1104–1128, 2024, doi: 10.1108/INTR-02-2022-0121.
C. Luo, N. A. Mohd Hasan, A. M. Z. Ahmad, and G. Lei, “Influence of short video content on consumers’ purchase intentions on social media platforms with trust as a mediator,” Scientific Reports, vol. 15, 16605, 2025, doi: 10.1038/s41598-025-94994-z.
J. Berger and K. L. Milkman, “What makes online content viral?” Journal of Marketing Research, vol. 49, no. 2, pp. 192–205, 2012.
T. Tafesse and C. Wood, “Followers’ engagement with Instagram influencers,” Journal of Business Research, vol. 143, pp. 123–134, 2022.
H. H. Widagdo, D. Lukito, and T. William, “Keberlanjutan penggunaan email marketing sebagai strategi pemasaran digital untuk UMKM,” Jurnal ARIMBI, vol. 5, no. 1, 2025.
J. W. Treem and P. M. Leonardi, “Social media use in organizations: Exploring affordances of visibility, editability, persistence, and association,” Annals of the International Communication Association, vol. 36, no. 1, pp. 143–189, 2013.
D. Dellermann, P. Ebel, M. Söllner, and J. M. Leimeister, “Hybrid intelligence,” Business & Information Systems Engineering, vol. 61, no. 5, pp. 637–643, 2019.
A. Rai, “Explainable AI: From black box to glass box,” Journal of the Academy of Marketing Science, vol. 48, pp. 137–141, 2020
A. Mehrabian and J. A. Russell, An Approach to Environmental Psychology. Cambridge, MA: MIT Press, 1974.
R. Hu, “The evolution of social media affordances and the changing landscape of digital marketing,” Advances in Economics, Management and Political Sciences, vol. 189, pp. 136–141, 2025.
A. R. Hevner, S. T. March, J. Park, and S. Ram, “Design science in information systems research,” MIS Quarterly, vol. 28, no. 1, pp. 75–105, 2004.
M. Peffers, T. Tuunanen, M. Rothenberger, and S. Chatterjee, “A design science research methodology for information systems research,” Journal of Management Information Systems, vol. 24, no. 3, pp. 45–77, 2007.
H. Zhang, Y. Zhao, and J. Xu, “Applying the S-O-R framework to digital platform engagement,” Electronic Commerce Research, vol. 23, 2023.
S. Gregor and A. R. Hevner, “Positioning and presenting design science research for maximum impact,” MIS Quarterly, vol. 37, no. 2, pp. 337–355, 2013.
B. Liu, Sentiment Analysis and Opinion Mining. Morgan & Claypool, 2020.
L. Liu, H. Li, and Y. Hu, “Emotion and engagement in algorithm-driven content platforms,” Telematics and Informatics, vol. 76, 2023.
Y. R. Shrestha, S. M. Ben-Menahem, and G. von Krogh, “Organizational decision-making structures in the age of artificial intelligence,” California Management Review, vol. 61, no. 4, pp. 66–83, 2019.
W. Sun and Q. Zhang, “Cognitive and affective drivers of social media interaction,” Information & Management, vol. 60, no. 4, 2023.
J. F. Hair, G. T. M. Hult, C. M. Ringle, and M. Sarstedt, A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM), 3rd ed., Sage, 2022.
R. B. Kline, Principles and Practice of Structural Equation Modeling, 4th ed., Guilford Press, 2021.
V. Venkatesh, J. Y. L. Thong, and X. Xu, “Consumer acceptance and use of information technology: Extending the unified theory,” MIS Quarterly, vol. 36, no. 1, pp. 157–178, 2012.
V. Venkatesh, M. G. Morris, G. B. Davis, and F. D. Davis, “User acceptance of information technology: Toward a unified view,” MIS Quarterly, vol. 27, no. 3, pp. 425–478, 2003.
B. Liu, “Sentiment analysis and opinion mining,” Synthesis Lectures on Human Language Technologies, vol. 5, no. 1, pp. 1–167, 2012.
A. Cambria and B. White, “Jumping NLP curves: A review of sentiment analysis methods,” IEEE Computational Intelligence Magazine, vol. 17, no. 1, pp. 36–52, 2022.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2026 Hermawan Honggowidagdo, Thomas William, Henkie Ongowarsito
This work is licensed under a Creative Commons Attribution 4.0 International License.
All articles published in IT-Explore: Jurnal Penerapan Teknologi Informasi dan Komunikasi is licensed under a Creative Commons Attribution 4.0 International License.
