Article Per Year (5 Year)
p-Index From 2021 - 2026
1.066
P-Index
This Author published in this journals
All Journal ADI Bisnis Digital Interdisiplin (ABDI Jurnal) Jurnal MENTARI: Manajemen, Pendidikan dan Teknologi Informasi International Transactions on Education Technology (ITEE) Journal of Computer Science and Technology Application JOT AI, Innovation, and Resilience for the Environment Journal
Omar Arif Al-Kamari
Pandawan Incorporation
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Languange, Linguistic, Communication & Media Library & Information Science Social Sciences Other

Published : 6 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 6 Documents
Search

 1 
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.
The Impact of Gamified Mobile Applications on Happiness Levels Based on User Data Dwi Andayani; Antonius Ary Setyawan; Aulia Rahma Dina; Lista Meria; Omar Arif Al-Kamari
Journal of Orange Technology Vol. 2 No. 1 (2025): October
Publisher : Sinar Mentari Sundara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.68012/jot.v2i1.53

Abstract

The rapid growth of gamified mobile applications has transformed how users engage with digital technologies, particularly in promoting positive emotional experiences. This study investigates the impact of gamified mobile applications on happiness levels based on user data. A quantitative approach was employed using survey data collected from active users of gamified mobile applications across diverse demographic backgrounds. Happiness levels were measured using a validated well-being scale, while gamification features such as rewards, challenges, feedback, and social interaction were assessed as key predictors. Data were analyzed using structural equation modeling to examine the relationships between gamification elements and users’ happiness. The results indicate that gamified mobile applications have a significant positive effect on users’ happiness levels, with reward mechanisms and social interaction emerging as the strongest contributors. The findings suggest that thoughtfully designed gamification can enhance emotional well-being by fostering enjoyment, motivation, and sustained engagement. This study contributes to the field of affective and positive computing by providing empirical evidence of the humanistic impact of gamified digital technologies. Practically, the results offer insights for developers and designers to create user-centered applications that prioritize emotional well-being alongside functional performance. Overall, the study highlights the potential of gamified mobile applications as effective digital interventions for enhancing users’ happiness and advancing human-centered innovation within positive computing environments.
Integrating AI for Dynamic Personalization in Gamichain Systems: Enhancing Engagement, Ownership, and Value in Digital Business Anny Ratnawati; Tony Irawan; Syahrul Muarif Wahid; Omar Arif Al-Kamari
AI, Innovation, and Resilience for the Environment (AIR) Vol. 1 No. 1 (2026): AI, Innovation, and Resilience for the Environment (AIR) Journal
Publisher : Sinar Mentari Sundara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.68012/air.v1i1.123

Abstract

Gamichain, a hybrid model that integrates gamification and blockchain technology, has emerged as a foundational driver of innovation within modern digital business ecosystems by enabling transparent reward mechanisms, decentralized asset ownership, and long-term incentive structures that promote sustained user participation. Despite these advantages, many existing Gamichain implementations still face a critical limitation: the absence of dynamic and adaptive personalization, resulting in static user experiences that fail to align with evolving preferences, behavioral patterns, and motivational profiles. Artificial intelligence (AI) offers a powerful solution to this gap through data-driven personalization, predictive engagement analytics, dynamic tokenomics optimization, and adaptive gamification design capable of adjusting content and reward mechanisms in real time. By utilizing AI techniques such as machine learning, natural language processing, and recommendation systems, Gamichain ecosystems can deliver more relevant, anticipatory, and responsive interactions that significantly enhance user engagement, ownership, and perceived value. This study investigates the integration of AI-enabled personalization within Gamichain architectures, focusing on identifying suitable AI techniques and strategies that optimize user experience across decentralized gamified environments. The primary contribution of this research is the development of a conceptual framework for AI-powered dynamic personalization in Gamichain systems, highlighting its potential to strengthen user loyalty, foster digital community formation, and support sustainable business growth. This framework provides a theoretical foundation for future empirical studies and practical implementations aimed at advancing next-generation Gamichain applications capable of delivering richer, more adaptive, and value-driven digital experiences.
AI-Driven Educational Data Analytics and Intelligent Tutoring in Learning Factory Environments Po Abas Sunarya; Richard Andre Sunarjo; Maulana Abbas; Omar Arif Al-Kamari; Sabda Maulana
International Transactions on Education Technology (ITEE) Vol. 4 No. 1 (2025): International Transactions on Education Technology (ITEE)
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/itee.v4i1.950

Abstract

The rapid growth of artificial intelligence in higher education creates new op- portunities to make learning factory environments more adaptive, data-informed, and aligned with industrial practice. This study examines how the integration of educational data analytics and intelligent tutoring systems supports smarter learning factory models that connect theoretical instruction with hands-on indus- trial training. Using a quantitative research design, data were collected from 180 higher education students participating in AI-supported learning factory sessions. Log data on learning interactions, performance metrics, and system- generated feedback were analyzed using statistical modeling to test the effects of AI-driven interventions on learning outcomes. The results show that ed- ucational data analytics significantly increases the adaptability of instructional content, enabling the intelligent tutoring system to personalize learning paths in real time based on individual performance profiles. Students who engaged with AI-based tutoring reported higher learning engagement and achieved better problem-solving scores and stronger retention of practical concepts than those in conventional learning factory settings. These findings indicate that combining educational data analytics with intelligent tutoring systems improves both the efficiency and effectiveness of learning factory models by enabling continuous feedback loops, dynamic adjustment of learning tasks, and learner-centered in- struction. The study concludes that AI-driven, data-informed learning factories can play a strategic role in preparing students with industry-relevant compe- tences and offers practical implications for educational technologists and insti- tutions designing next-generation education technology solutions.
Co-Authors Agus Sugiyato Anny Ratnawati Antonius Ary Setyawan Aulia Rahma Dina Bangun, Cicilia Sriliasta Dwi Andayani Fikri Fauzi HANDOKO, HENDRI Maulana Abbas Meria, Lista Muhtarom Mulyati Mulyati Nur Azizah Po Abas Sunarya Richard Andre Sunarjo Sabda Maulana Syahrul Muarif Wahid Tony Irawan Yulina Ismiyanti