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All Journal ADI Bisnis Digital Interdisiplin (ABDI Jurnal) Jurnal MENTARI: Manajemen, Pendidikan dan Teknologi Informasi Journal of Computer Science and Technology Application
Omar Arif Al-Kamari
Pandawan Incorporation
Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Languange, Linguistic, Communication & Media Library & Information Science Other

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Evaluating the Effectiveness of AI in Developing Digital Marketing Content for Certification Service Firms Agus Sugiyato; Cicilia Sriliasta Bangun; Fikri Fauzi; Mulyati Mulyati; Omar Arif Al-Kamari
ADI Bisnis Digital Interdisiplin Jurnal Vol 6 No 2 (2025): ADI Bisnis Digital Interdisiplin (ABDI Jurnal)
Publisher : ADI Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34306/abdi.v6i2.1305

Abstract

The implementation of Artificial Intelligence (AI) in digital marketing has become a major driver of business efficiency, yet its strategic implications and the role of human involvement still require in-depth study. This qualitative case study research aims to analyze the effectiveness of adopting generative AI (Gemini AI and Claude AI) in the content creation process at PT. Gaivo Solusi Manajemen, focusing on perceived usefulness, the role of human-in-the-loop, and the perspective of competitive advantage. Data were collected through semi-structured interviews with key informants and analyzed using Thematic Analysis. The findings indicate that AI substantially increases process efficiency, particularly in drafting content and SEO Meta packages, which boosts production volume and speed. However, key findings emphasize that AI is merely a supporting tool and necessitates mandatory supervision by expert staff (human-in-the-loop) to ensure information integrity and quality that complies with professional service industry regulations. Strategically, AI is not considered a source of hardly imitable competitive advantage (it is a commodity), but rather an enabler. The true competitive advantage lies in the staff’s ability in prompt engineering, supported by the company’s relevant internal data. This study provides managerial contributions by recommending a focus on investment in human resource skill development rather than solely on the acquisition of AI tools.
Machine Learning Enabled Social Media Competitive Intelligence System Hendri Handoko; Yulina Ismiyanti; Omar Arif Al-Kamari
CORISINTA Vol 3 No 1 (2026): February
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/480er062

Abstract

Social media platforms generate massive volumes of publicly accessible digital data that reflect organizational competitive strategies, yet most existing competitor analyses remain manual, descriptive, and limited to surface-level engagement metrics, resulting in low scalability and weak strategic intelligence. This study proposes a Machine Learning Enabled Social Media Competitive Intelligence System designed to automate competitor strategy extraction through artificial intelligence and big data analytics. The objective is to develop a computational framework capable of identifying strategic content patterns, communication objectives, audience positioning, and paid advertising behaviors using data-driven techniques. Large-scale public data from social media posts, engagement indicators, and advertising transparency libraries are collected and processed through data preprocessing pipelines, including text normalization, tokenization, and feature extraction using TF-IDF and word embedding representations. Supervised machine learning algorithms are implemented to classify content themes, detect strategic clusters, and model competitive positioning patterns, while performance evaluation is conducted using accuracy, precision, recall, and F1-score metrics to ensure robustness and reliability. Experimental findings demonstrate that the proposed system significantly enhances analytical consistency, scalability, and strategic insight generation compared to traditional mixed method approaches. This research contributes to the advancement of AI-driven social media analytics and establishes a computational foundation for scalable big data-based competitive intelligence systems aligned with Artificial Intelligence and Big Data domains.
Blockchain-Based Transformation of Academic Data Management for Enhancing University Governance Nur Azizah; Muhtarom; Omar Arif Al-Kamari
Jurnal MENTARI: Manajemen, Pendidikan dan Teknologi Informasi Vol 4 No 2 (2026): March
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/mentari.v4i2.1021

Abstract

Academic data management at universities often faces challenges related to security, transparency, and efficiency. Conventional systems are still vulnerable to manipulation, data duplication, and slow verification processes. This situation demands technological innovation to strengthen academic data governance comprehensively. This study aims to analyze the application of blockchain technology as a solution to transform academic data management and evaluate its contribution to improving security, transparency, and accountability in university governance. The research approach uses a systematic literature review and a comparative analysis of traditional data management models and blockchain based systems. In addition, empirical validation was conducted through a survey involving 120 respondents consisting of academic stakeholders, including lecturers, administrative staff, and students. Furthermore, a mapping of university governance needs and simulation of relevant blockchain architectures for academic applications were conducted. The results show that blockchain integration can improve academic data integrity through distributed recording and encryption mechanisms. This technology also accelerates the diploma verification process, minimizes the risk of document forgery, and increases the efficiency of data exchange between university units. The application of blockchain in academic data management has the potential to become a strong foundation for more transparent, secure, and efficient university governance. This transformation can drive improvements in academic services and support more accurate decision-making based on reliable data.
Co-Authors Agus Sugiyato Bangun, Cicilia Sriliasta Fikri Fauzi Hendri Handoko Muhtarom Mulyati Mulyati Nur Azizah Yulina Ismiyanti