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All Journal Technomedia Journal ADI Bisnis Digital Interdisiplin (ABDI Jurnal) Jurnal MENTARI: Manajemen, Pendidikan dan Teknologi Informasi International Transactions on Artificial Intelligence (ITALIC) Journal of Computer Science and Technology Application
Kamal Arif Al-Farouqi
Eduaward Incorporation
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Languange, Linguistic, Communication & Media Library & Information Science Other

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Video Storytelling as an Effective Strategy for College Brand Awareness Rilla Gantino; Desy Apriani; Fanani Islamia Ningrum; Rifqi Fahrudin; Kamal Arif Al-Farouqi; Bintang Nanda Henry
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.v6i2.1303

Abstract

As digital threats to privacy and data integrity continue to rise, cybersecurity has become a critical issue in the business and financial sectors. However, existing risk mitigation strategies tend to focus primarily on technical aspects, often overlooking the ethical dimensions that are vital for governance and decision-making, especially in the development and implementation of emerging technologies such as Artificial Intelligence (AI). This study aims to explore the role of ethical principles in cybersecurity risk mitigation through a Systematic Literature Review (SLR). A total of eleven relevant articles were analyzed using the SALSA framework (Search, Appraisal, Literature Systems, Analysis). The findings indicate that ethical values such as transparency, accountability, fairness, and compliance with regulations play a significant role in enhancing the effectiveness of cybersecurity strategies. Key challenges identified include the absence of global ethical standards, limited human resources with digital ethics expertise, and ethical dilemmas in the use of AI. This review underscores the necessity of integrating ethical values into organizational policies, technologies, and cultures to establish more sustainable and trustworthy cybersecurity systems.
Efficient Machine Learning Acceleration with Randomized Linear Algebra for Big Data Dwi Cahyono; Apriani Sijabat; Kamal Arif Al-Farouqi
CORISINTA Vol 3 No 1 (2026): February
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/v3i1.163

Abstract

The rapid growth of big data has significantly increased the computational complexity of machine learning models, particularly due to intensive linear algebra operations that limit scalability and efficiency. This study aims to investigate the effectiveness of Randomized Linear Algebra (RLA) as an acceleration strategy for machine learning in large scale data environments. The research adopts an experimental methodology by integrating randomized techniques such as matrix sketching and random projection into standard machine learning pipelines and evaluating their performance against deterministic baseline approaches. Experiments are conducted on large dimensional datasets using multiple machine learning models, with performance assessed in terms of computational time, memory usage, model accuracy, and scalability. The results demonstrate that the proposed RLA based approach substantially reduces computational cost and memory consumption while maintaining comparable predictive accuracy to conventional methods. These findings indicate that randomized techniques provide an effective trade off between efficiency and accuracy, enabling scalable machine learning for big data applications. In conclusion, this study contributes to the advancement of efficient Artificial Intelligence (AI) systems by demonstrating that RLA can serve as a practical and scalable solution for accelerating machine learning computations in big data contexts, aligning with the growing demand for resource efficient and high performance AI infrastructures.
Emerging Educational Technologies and Their Influence on Preschool Learning Outcomes Annisa Ardien; Novena Ulita; Kamal Arif Al-Farouqi; Solahudin
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.v4i2.1047

Abstract

This study examines the influence of emerging educational technologies on preschool learning outcomes amid the increasing integration of digital tools such as educational applications, interactive games, and multimedia based platforms in early childhood education. Using a quantitative approach with a quasi experimental design, the study involved 167 preschool students who participated in technology-enhanced learning activities over a defined instructional period. Data were collected through structured observations, standardized assessments of learning outcomes, and teacher evaluations to comprehensively measure cognitive, social, and emotional development. The findings indicate that the use of emerging educational technologies has a statistically significant positive effect on preschool learning outcomes, particularly in improving student engagement, foundational literacy skills, problem-solving abilities, and cooperative behaviors compared to traditional instructional methods. In addition, teachers reported better classroom interaction, increased instructional efficiency, improved student participation, and more effective learning management when digital tools were implemented appropriately. These results suggest that the integration of technology not only enhances academic related skills but also supports broader developmental aspects of young learners. Therefore, the study highlights the importance of thoughtful and well structured implementation of educational technologies to maximize their benefits in early childhood education settings, ensuring sustainable, inclusive, and developmentally appropriate learning experiences.
SEO Dimensions and AI-Assisted Predictive Scoring for Digital Business Sales Performance in Indonesia Muchtadin Muchtadin; Michael Surya Gunawan; Dwi Safarina; Kamal Arif Al-Farouqi
International Transactions on Artificial Intelligence Vol. 4 No. 2 (2026): May
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/italic.v4i2.1093

Abstract

The intensification of digital commerce competition has elevated Search Engine Optimization (SEO) into a strategically important yet empirically underexplored determinant of sales performance, particularly within Indonesia’s rapidly expanding e-commerce ecosystem, where organic search drives over 64% of commercial platform traffic. This study examines the influence of three core SEO dimensions on page optimization, off-page optimization, and technical SEO on digital business sales performance, measured through website traffic growth, customer conversion rates, and revenue improvement. A quantitative explanatory design was applied using primary data from structured question naires administered to 120 digital business owners and e-commerce managers in Indonesia, cross-validated with secondary Google Analytics records over a twelve-month observation window. Multiple linear regression was conducted after validity, reliability, normality, multicollinearity, and heteroscedasticity tests. An AI-assisted Predictive SEO Performance Scoring (PSPS) model was also developed as a weighted composite function based on standardized regression coefficients to simulate performance trajectories across three SEO deployment scenarios. All three SEO dimensions showed statistically significant positive effects on sales performance (p < 0.05), with on-page SEO recording the strongest coefficient (β = 0.342), followed by technical SEO (β = 0.318) and off-page SEO (β = 0.291). The model explained 68% of performance variance (R2 = 0.68). PSPS scenario analysis showed that comprehensive SEO adoption produced an 83.5% relative performance improvement over minimal deployment. These findings position SEO as a core strategic investment aligned with Indonesia’s Making Indonesia 4.0 agenda and SDGs 8, 9, and 10.
Business Intelligence Implementation to Support Data Driven Strategic Decision Making in Digital Organizations Tessa Handra; Agung Rizky; Mungkap Mangapul Siahaan; Steven Harazaki Lase; Kamal Arif Al-Farouqi
Technomedia Journal Vol 11 No 1 (2026): June
Publisher : Pandawan Incorporation, Alphabet Incubator Universitas Raharja

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/tmj.v11i1.2640

Abstract

Digital transformation has encouraged organizations to generate large volumes of data that must be managed effectively to support strategic decision-making. Business Intelligence has become a solution for integrating, analyzing, and presenting data into valuable information. This study aims to analyze the implementation of Business Intelligence in digital organizations, its role in supporting data-driven strategic decision-making, as well as the benefits and challenges of its implementation. This study used a qualitative method with a literature review approach by examining journals, scientific articles, and other academic publications related to the research topic. The data were analyzed using descriptive qualitative analysis. The results show that the implementation of BI through data warehouses, dashboards, reporting systems, and data analytics can improve operational efficiency, accelerate access to information, and support performance monitoring and business trend analysis more effectively. BI also supports faster and more accurate data-driven decision-making. Business Intelligence plays an important role in supporting strategic data-driven decision-making in digital organizations. However, its implementation still faces challenges related to data quality, information security, limited human resource competencies, and high technology implementation costs.
Co-Authors Agung Rizky Annisa Ardien Apriani Sijabat Bintang Nanda Henry Desy Apriani Dwi Cahyono Dwi Safarina Fanani Islamia Ningrum Michael Surya Gunawan Muchtadin Muchtadin Mungkap Mangapul Siahaan Novena Ulita Rifqi Fahrudin Solahudin Steven Harazaki Lase Tessa Handra