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All Journal Technomedia Journal Aptisi Transactions on Technopreneurship (ATT) ADI Bisnis Digital Interdisiplin (ABDI Jurnal) International Transactions on Education Technology (ITEE) International Transactions on Artificial Intelligence (ITALIC) Journal of Computer Science and Technology Application JOT AI, Innovation, and Resilience for the Environment Journal
Carlos Perez
Ilearning Incorporation
Humanities 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

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Deep Learning Enabled Security Monitoring for Intrusion Detection in Smart Campus Networks Ruli Supriati; Nuke Puji Lestari Santoso; Steven Harazaki Lase; Carlos Perez
CORISINTA Vol 3 No 2 (2026): August
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/a88eeq63

Abstract

The increasing complexity of smart campus networks has heightened the need for advanced cybersecurity measures to protect sensitive data and ensure seamless operations. Traditional Intrusion Detection Systems (IDS) often struggle to cope with the dynamic and heterogeneous nature of network traffic in smart campus environments, necessitating the development of more effective solutions. This study aims to propose a deep learning-based intrusion detection system for smart campus networks, utilizing a Hybrid CNN-LSTM model to enhance security monitoring. The proposed methodology integrates Convolutional Neural Networks (CNN) for feature extraction and Long Short-Term Memory (LSTM) networks for capturing temporal dependencies in network traffic. The model was trained and evaluated on a combination of publicly available datasets and simulated smart campus data, measuring performance through key metrics such as accuracy, precision, recall, and F1-score. Results show that the Hybrid CNN-LSTM model outperforms traditional machine learning models, achieving an accuracy of 97.3% and a ROC-AUC of 0.99, demonstrating superior detection of both known and unknown intrusions. The findings suggest that deep learning models, especially when tailored to smart campus contexts, offer significant advantages in real-time threat detection and adaptive learning. This research contributes to the growing body of knowledge on AI-driven network security and provides practical insights for improving cybersecurity infrastructures in higher education institutions.
Adaptive Fuzzy Hybrid AI for Urban Energy Traffic Decision Support Qurotul Aini; Andriyansah Andriyansah; Mekani Vestari; Po Abas Sunarya; Carlos Perez
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.v4i2.1049

Abstract

Urban energy and traffic systems are two highly interdependent components of smart city infrastructures, both of which operate under significant uncertainty caused by fluctuating demand, human mobility patterns, weather variability, and policy constraints. While Artificial Intelligence (AI) techniques particularly machine learning and deep learning have demonstrated strong predictive capabilities in these domains, their black box nature limits interpretability, trust, and adoption in real world urban governance. Methods: This study proposes an adaptive fuzzy hybrid artificial intelligence framework that integrates fuzzy inference systems with ensemble machine learning models to support uncertainty aware and explainable decision making in urban energy and traffic management. The proposed framework is validated using real world secondary data obtained from open government and smart city data portals, including urban energy demand, traffic flow, and environmental indicators. The primary objective of this research is to develop a robust and interpretable decision-support model capable of dynamically adapting to uncertain urban conditions while maintaining high predictive performance. Experimental evaluations demonstrate that the proposed fuzzy hybrid AI framework consistently outperforms standalone machine learning approaches in terms of decision stability, robustness under uncertainty, and interpretability across multiple urban scenarios. Conclusion: The findings indicate that adaptive fuzzy hybrid AI offers a practical, scalable, and policy aligned solution for urban energy traffic decision support, contributing to sustainable smart city governance and supporting evidence-based decision making in line with global sustainability agendas.
Digitalization of Financial Literacy and Social Media as a Sustainable Economic Strategy in Food & Beverages: Digitalisasi Literasi Keuangan dan Media Sosial sebagai Strategi Ekonomi Berkelanjutan pada Food & Beverages Hamdan Hamdan; Efa Ayu Nabila; Reyhan Algiffary Gunawan; Ajeng Putri Valentie; Carlos Perez
ADI Bisnis Digital Interdisiplin Jurnal Vol 6 No 1 (2025): ADI Bisnis Digital Interdisiplin (ABDI Jurnal)
Publisher : ADI Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34306/abdi.v6i1.1229

Abstract

The digitalization of financial literacy is a crucial foundation for strengthening the resilience and economic sustainability of the Micro, Small, and Medium Enterprises (MSMEs) sector, particularly in the dynamic and competitive food and beverage industry. This study aims to analyze the role of creative communication strategies, brand personality, and digital marketing engagement in encouraging the optimization of interactive social media and the utilization of user-generated content as instruments for improving MSME financial literacy. Using quantitative methods and a Structural Equation Modeling (SEM) approach based on SmartPLS software, this study involved 150 MSMEs in the food and beverage sector across Indonesia. The results show that creative communication strategies and brand personality have a significant influence on digital marketing engagement, which in turn encourages social media use and consumer participation in producing educational content. These findings confirm that the digitalization of financial literacy through digital platforms not only improves financial understanding but also strengthens MSME businesses position in a sustainable digital economy ecosystem. This study provides strategic implications for MSMEs, policymakers, and other stakeholders in developing social media-based financial literacy models relevant to the characteristics of the food and beverage sector.
Integrating Digital Marketing into Learning Enhances Strategic Student Competencies for Sustainable Industry Readiness Raden Roro Ayu Metarini; Shofiyul Millah; Ramzi Zainum Ikhsan; Sherli Triandari; Carlos Perez
Technomedia Journal Vol 11 No 2 (2026): October
Publisher : Pandawan Incorporation, Alphabet Incubator Universitas Raharja

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/tmj.v11i2.2681

Abstract

The advancement of digital technology has accelerated the transformation of higher education in preparing students to meet dynamic industrial demands. However, digital technology utilization in learning remains largely focused on content delivery rather than strategic competency development. This study aims to analyze the integration of digital technology in learning and evaluate its contribution to developing industry based strategic competencies among students. A qualitative descriptive approach was employed involving students, lecturers, and industry partners. Data were collected through observations, interviews, questionnaires, and documentation, then analyzed using descriptive and thematic analysis. The findings reveal that the implementation of Learning Management Systems (LMS), Artificial Intelligence (AI), digital collaboration platforms, video conferencing, and digital simulations supports the development of critical thinking, creativity, digital communication, collaboration, problem solving skills, and student readiness for industrial environments. The combination of technology integration and project-based learning creates adaptive learning experiences aligned with professional requirements. This study concludes that digital technology serves not only as a learning medium but also as a strategic approach for strengthening student competencies and supporting higher education transformation toward SDG 4 (Quality Education). This research provides an analytical perspective by connecting technology implementation, learning practices, and industry expectations to promote sustainable human resource development.
Enhancing Academic Wellbeing through Empathic Digital Attendance Systems using UTAUT Approach Oscar Jayanagara; Ika Yuni Purnama; Meri Mayang Sari; Sheila Aulia Anjani; Carlos Perez
Journal of Orange Technology Vol. 1 No. 2 (2025): April
Publisher : Sinar Mentari Sundara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.68012/jot.v1i2.15

Abstract

The increasing integration of information technology in higher education has transformed various academic processes, including attendance monitoring. Online attendance systems are gaining traction as institutions seek greater accuracy, transparency, and operational efficiency in managing student participation. This study aims to analyze how the use of an online attendance system influences student attendance performance within the framework of the Unified Theory of Acceptance and Use of Technology (UTAUT). It explores key determinants that shape user acceptance and behavioral responses toward the system. A quantitative case study was conducted at a higher education institution, involving students and academic staff who actively used the system. Data were collected through structured questionnaires and analyzed using statistical techniques to examine the relationships among system usage, perceived usefulness, ease of use, and attendance performance. The findings reveal that the online attendance system significantly improves student attendance performance by enhancing consistency, reducing manual errors, and encouraging timely participation. Perceived usefulness and ease of use were found to mediate the relationship between system usage and attendance outcomes, demonstrating the system’s practical and motivational impact. This study underscores the vital role of digital solutions in strengthening attendance management and promoting user compliance. The results offer valuable guidance for institutions seeking to optimize online attendance systems. Future research should involve broader samples and comparative institutional settings to generate more comprehensive insights.
Digital Green Strategies Driving Sustainable Competitive Advantage in Agripreneurial SMEs Andriyansah Andriyansah; Fatia Fatimah; Venty Fitriany Nurunisa; Ninda Lutfiani; Carlos Perez
Aptisi Transactions On Technopreneurship (ATT) Vol 8 No 3 (2026): November
Publisher : Pandawan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34306/att.v8i3.858

Abstract

Achieving Sustainable Competitive Advantage (SCA) for green agricultural products in demanding international markets, particularly Europe, requires strategic integration beyond conventional marketing approaches. Although green branding and digital marketing have been widely recognized, the synergistic mechanism through which digital strategies, green value propositions, and market penetration collectively strengthen SCA remains underexplored. This study aims to develop and validate an integrated model that explains how Digital Traffic Management, Green Product Value, and Market Penetration contribute to SCA while supporting sustainability-oriented business growth. This study employs Structural Equation Modeling (SEM) because it provides stronger analytical robustness than conventional regression through latent variable estimation, construct validation, and comprehensive goodness-of-fit evaluation. The \textbf{findings} show that Market Penetration is the strongest predictor of SCA (β = 0.44), indicating that market expansion through digital channels significantly enhances long-term competitiveness. The results also reveal a positive reinforcement cycle between Green Product Value and SCA, demonstrating a virtuous loop of continuous improvement, digital adaptability, and green competitiveness. Furthermore, Market Penetration and Digital Traffic Management support SDG 8, SDG 9, and SDG 17 by encouraging sustainable economic growth, technology-based innovation, collaborative business ecosystems, and stronger digital partnerships for SMEs. This study provides a robust empirical foundation and strategic blueprint for SMEs, policymakers, and digital marketers to enter and grow in international markets while strengthening sustainable competitiveness and contributing meaningfully to a greener future.
A Framework for Data-Informed Content Creation Elda Diah Safitri; Fitra Putri Oganda; Carlos Perez
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.v1i1.128

Abstract

The rapid growth of digital platforms has transformed content creation into a data-rich activity where audience interactions generate valuable insights. De- spite the availability of large volumes of data, many content creators still rely heavily on intuition and use analytics only for superficial performance monitor- ing. This study addresses the gap between data availability and its practical inte- gration into the content creation process by proposing a structured framework for data-informed content creation. The research reviews existing literature on dig- ital marketing, content strategy, and data analytics to identify key stages where data can effectively guide creative decision-making. The proposed framework integrates audience analysis, data-driven content planning, content production, and performance evaluation into a continuous cycle of improvement. By com- bining analytical insights with creative processes, the framework helps organi- zations develop more relevant, engaging, and strategically aligned content. The findings highlight that a data-informed approach not only improves engagement and efficiency but also supports better collaboration between creative and analyt- ical teams. Ultimately, this study contributes both theoretically and practically by providing a systematic model that enables marketers and content creators to leverage data more effectively throughout the entire content lifecycle.
Enhancing Adaptive Learning Environments in Learning Factories through Artificial Intelligence Ersa Aura Natasya; Nuke Puji Lestari Santoso; Lukita Pasha; Chua Toh Hua; Carlos Perez
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.v4i1.957

Abstract

The rapid advancement of Artificial Intelligence (AI) has significantly trans- formed educational paradigms, particularly in adaptive learning environments where real-time personalization and intelligent feedback are essential. This study aims to explore how AI-driven mechanisms can enhance adaptive learning within learning factory environments by utilizing data analytics to personalize learning processes and optimize instructional delivery. Employing a quantita- tive research design, the data collection process involved distributing question- naires to 200 university students enrolled in AI-supported learning factory pro- grams. From this distribution, 120 valid responses were successfully obtained and analyzed, consisting of 80 students and 40 instructors across three universi- ties, representing the final usable dataset for this study. Statistical analysis was performed using regression and correlation models to assess the impact of AI- based adaptivity on learning performance, engagement, and cognitive retention. The findings reveal that AI integration within learning factories leads to sig- nificant improvements in learner adaptability, interaction efficiency, and overall academic achievement. The adaptive AI models dynamically adjusted learning content based on individual performance metrics, resulting in higher engage- ment rates and enhanced skill mastery compared to traditional non-AI-based environments. The outcomes confirm that AI can function as a critical enabler of responsive and data-driven education by bridging theoretical and practical as- pects of industrial learning. This research underscores the transformative poten- tial of Artificial Intelligence in reshaping adaptive learning environments within learning factories, emphasizing the need for further development of AI systems that prioritize personalization, continuous assessment, and the seamless integra- tion of human and machine intelligence
Co-Authors Ajeng Putri Valentie Andriyansah . Chua Toh Hua Efa Ayu Nabila Elda Diah Safitri Ersa Aura Natasya Fatia Fatimah Fitra Putri Oganda Hamdan, Hamdan Ika Yuni Purnama Lukita Pasha Mekani Vestari Meri Mayang Sari Ninda Lutfiani Nuke Puji Lestari Santoso Oscar Jayanagara Po Abas Sunarya Qurotul Aini Raden Roro Ayu Metarini Ramzi Zainum Ikhsan Reyhan Algiffary Gunawan Ruli Supriati, Ruli Sheila Aulia Anjani Sherli Triandari Shofiyul millah Steven Harazaki Lase Venty Fitriany Nurunisa