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All Journal Electronic Integrated Computer Algorithm Journal Jurnal Pengabdian Bakti Akademisi SITEKNIK: Sistem Informasi, Teknik dan Teknologi Terapan
Thamrin, Daffa Shidqi
Unknown Affiliation
Humanities Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Social Sciences

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Business Model for Effectiveness of Human-AI Collaboration Patterns in Digital Fiction Storytelling: A Systematic Literature Review Thamrin, Daffa Shidqi
SITEKNIK: Sistem Informasi, Teknik dan Teknologi Terapan Vol. 2 No. 3 (2025): July
Publisher : RAM PUBLISHER

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5281/zenodo.15788175

Abstract

The unavoidable pace of Artificial Intelligence (AI) is changing its role from a simple tool to a potential thought partner in various creative fields, including digital storytelling. This research discusses the effectiveness of human-AI collaboration patterns specifically in the aspect of fictional storytelling, for which creativity, narrative structure, and imagination are crucial. By a Systematic Literature Review (SLR), this research synthesizes results from published article research from 2020 to 2025, with the focus on how AI supports and co-creates with human authors throughout the storytelling process. This research also discovered and identified several benefits, which include increased creativity, idea generation, and efficiency, and also discussed challenges such as authority for storytelling, author originality, and ethical considerations in the ownership of a story. This research also discusses implications for future development and proposes a collaboration model that involves two types of human actors, which are writers and editors, and then collaborating with an AI system by a human-centered business design approach. Those results offer a framework business model for implementing practical storytelling systems by results of literature review and suggest that fictional storytelling may be an ideal and potential field for learning about AI's creative potential. The contribution of this research is the aspect of human-centered AI and provides guidelines for designing collaborative storytelling platforms that include both human and AI roles
Workshop on the Double-Edged Sword of Artificial Intelligence: A Study on the Effectiveness of Outreach for Mitigating the Risk of AI Crime Lubis, Muharman; Thamrin, Daffa Shidqi
Jurnal Pengabdian Bakti Akademisi Vol 2, No 4 (2025): Jurnal Pengabdian Bakti Akademisi
Publisher : Universitas Syiah Kuala

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24815/jpba.v2i4.49867

Abstract

Artificial Intelligence (AI) is a double-edged sword that offers innovation as well as advanced new cyber threats, known as AI Crime. The purpose of this activity is to measure and evaluate the effectiveness of outreach in improving cyber literacy among webinar participants regarding the classification, risks, and mitigation strategies of AI Crime. The outreach was conducted on Sunday, October 5, 2025, via Zoom and was attended by 92 participants. Quantitative data was collected through pre-tests and post-tests. The community service focused on presenting AI as a double-edged sword in technological advancement, which can lead to violations divided into three types of AI Crime threats (with, on, and by AI). The average pre-test score was 94.78%. This score increased to 95.22% on the post-test, resulting in an effectiveness improvement of 0.46%. This high initial score indicates a ceiling effect. This small but positive improvement confirms the success of socialization in strengthening public knowledge. Qualitative findings also show that participants need guidance in detecting deepfakes and data privacy protection strategies. Regarding deepfake detection itself, several studies also describe ways to deal with it, which are by using AI technology and other technologies such as blockchain. In this era of AI advancement, data privacy protection strategies require appropriate regulations, which require cooperation between stakeholders regarding the duality of AI use. Not only that, but we as end-users of technology must also participate in realizing justice in the use of technology by increasing digital literacy regarding AI advances, one of which is through this community service webinar. This research emphasizes the important role of public education as a crucial risk mitigation measure for AI use in this era of smart technology.
Implementing Collaborative Filtering for E-Commerce Product Personalization Using a Rapid Application Development Approach Giawa, Mullah Cadre; Fansyah, Egi Al; Alsyah, Dwi fahira; Nusantara, Abdul Hakim Satria; Thamrin, Daffa Shidqi; Fauzan, Ratandi Ahmad
Electronic Integrated Computer Algorithm Journal Vol. 3 No. 2 (2026): VOLUME 3, NO 2: APRIL 2026
Publisher : Yayasan Asmin Intelektual Berkah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62123/enigma.v3i2.145

Abstract

The rapid expansion of e-commerce has increased the difficulty of guiding users toward relevant products, particularly as catalogs grow and user preferences become more diverse. This paper presents an end-to-end implementation of a personalized product recommendation feature using a memory-based collaborative filtering approach integrated into an e-commerce platform. Development followed a Rapid Application Development (RAD) workflow, enabling iterative prototyping, integration, and testing of the recommendation module within the operational system. Recommendations were generated using a K-Nearest Neighbors method with cosine-based similarity to identify related items from user interaction histories and to produce Top-N product suggestions in the storefront interface. Model evaluation employed a transactional dataset commonly used for recommender experiments, which was refined from 541,909 records (8 attributes) to 406,829 interaction-focused records (CustomerID, Description, Quantity). Performance was assessed using MAE, RMSE, and F1-score, yielding values of 0.6, 0.8, and 0.6, respectively. The results indicate that collaborative filtering can provide moderately accurate and relevant recommendations when interaction history is available, while also exposing practical limitations for users with limited transactions, reflecting a cold-start constraint. These findings suggest that RAD-supported integration of collaborative filtering is feasible for e-commerce personalization and provides a baseline for further enhancement.
Co-Authors Alsyah, Dwi fahira Fansyah, Egi Al Fauzan, Ratandi Ahmad Giawa, Mullah Cadre Muharman Lubis Nusantara, Abdul Hakim Satria