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All Journal ADI Bisnis Digital Interdisiplin (ABDI Jurnal) Jurnal MENTARI: Manajemen, Pendidikan dan Teknologi Informasi Blockchain Frontier Technology (BFRONT) International Transactions on Artificial Intelligence (ITALIC) Journal of Computer Science and Technology Application
Henry Zainarthur
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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Decentralized Data Storage Using IPFS for Sustainable Blockchain Availability Improvement Aswadi Jaya; Muh Fahrurrozi; Susy Alestriani Sibagariang; Vinkan Likita; Henry Zainarthur
Blockchain Frontier Technology Vol. 5 No. 2 (2026): Blockchain Frontier Technology
Publisher : IAIC Bangun Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34306/bfront.v5i2.860

Abstract

The rapid expansion of digital ecosystems has highlighted the limitations of centralized data storage systems, which often struggle with data loss, censorship, and single points of failure. To address these challenges, this study explores the InterPlanetary File System (IPFS) as a decentralized data management solution that enhances security, availability, and sustainability in distributed information environments. Using the IPFS-KI framework, a descriptive qualitative methodology, this research examines the architectural design, operational mechanisms, and real-world implementations of IPFS. Through literature analysis, node simulations, and case based evaluation, the study investigates IPFS performance in maintaining data integrity, fault tolerance, and resilience against network disruptions and censorship. The findings reveal that IPFS provides improved data reliability, transparency, and scalability compared to conventional centralized architectures, although certain limitations remain in terms of node stability and hidden centralization. This study contributes to a broader understanding of how decentralized storage technologies like IPFS can support the development of more secure, equitable, and sustainable digital infrastructures.
Data Driven UI Colour Selection for Enhanced User Engagement Sandy Setiawan; Titih Nursugiharti; Fitra Putri Oganda; Henry Zainarthur
Blockchain Frontier Technology Vol. 6 No. 1 (2026): Blockchain Frontier Technology
Publisher : IAIC Bangun Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34306/b-front.v6i1.1057

Abstract

This study highlights the importance of color selection in user interface (UI) design, as it can influence users’ perceptions, emotional responses, and levels of engagement. Many applications still rely on designers’ intuition without strong empirical evidence. The objective of this research is to develop a data-driven framework for UI color selection that can measurably enhance user engagement and satisfaction. The method involves collecting a large dataset of color schemes from various popular applications, followed by preprocessing steps such as normalization, clustering, and analysis of the relationships between colors and emotional responses. In addition, user interaction metrics and engagement data are analyzed to evaluate the impact of specific color combinations. The results show that certain color palettes consistently generate higher levels of engagement, with specific hues and contrast levels improving users’ attention and effectiveness in completing tasks. The analysis also identifies patterns that can serve as guidelines for designers in selecting colors that balance aesthetic appeal and functional performance. The conclusion of this study is that adopting a data-driven approach to UI color selection leads to a significant improvement in user engagement, while also providing practical guidance for designers and developers to optimize digital interface experiences.
The Impact of Digital Content Marketing Strategies on Perceived Usefulness and VALORANT Game Acceptance Nengah Sukendri; Zulfadli Ardiansyah; Agung Rizky; Asri Asri; Henry Zainarthur
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.v6i2.1335

Abstract

In the digital gaming industry that is becoming increasingly competitive, content marketing strategies play a crucial role in shaping players’ perceptions of a game’s quality and usefulness while influencing their decision to adopt it. As one of Riot games’ most prominent titles, VALORANT faces market dynamics that require digital marketing strategies that are informative, engaging, and sustainable. This study aims to analyze the influence of digital content marketing strategies on perceived usefulness and game acceptance using the Technology Acceptance Model (TAM) framework. The research employs a quantitative approach through an online survey of active VALORANT players, and the data are analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) to examine causal relationships within TAM. The findings reveal that digital content marketing strategies have a significant positive effect on perceived usefulness, while Perceived Ease of Use (PEOU) also enhances Perceived Usefulness (PU) and overall game acceptance. Relevant and consistent content strengthens players’ perceived value, whereas ease of use supports a more comfortable and enjoyable gaming experience. This study highlights that strong digital content marketing strategies combined with a user-friendly game design are key factors in increasing player acceptance and engagement. The findings offer essential insights for developers and marketers in formulating effective content strategies to expand adoption, boost retention, and sustain the game’s community ecosystem.
Empowering Edupreneurship through AI-Based Creative Journalism Education Hayatun Nufus; Subyantoro; Hari Bakti Mardikantoro; Rahayu Pristiwati; Henry Zainarthur
CORISINTA Vol 2 No 2 (2025): August
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/corisinta.v2i2.112

Abstract

The advancement of Artificial Intelligence (AI) has reshaped journalism and education, creating opportunities to strengthen creative writing and entrepreneurial skills. AI-driven journalism platforms, such as automated content generation and real-time feedback, empower students to become both skilled writers and edupreneurs, while developing 21st-century competencies including creativity, critical thinking, collaboration, and digital literacy. This study examines the influence of AI integration on creative writing skills, edupreneurship, and competency development using a quantitative research design. Data were collected through structured questionnaires distributed to university students and analyzed with Partial Least Squares Structural Equation Modeling (PLS-SEM) via SmartPLS 4. The analysis revealed high reliability and convergent validity of the measurement model, with structural results confirming significant positive relationships between AI integration and creative writing skills, and between creative writing skills and 21st-century competencies. Furthermore, creative writing was shown to mediate the connection between AI adoption and both entrepreneurship and competency development, indicating its key role in linking technological tools with educational and entrepreneurial outcomes. The findings underline the transformative role of AI in journalism education, not only in enhancing writing proficiency but also in building entrepreneurial capacity and essential future-ready skills. This \textbf{research provides} practical implications for integrating AI literacy, creative expression, and entrepreneurship into curricula, while also aligning with Sustainable Development Goals (SDGs) such as Quality Education, Economic Growth, Innovation, and Global Partnerships, supporting inclusive and technology-driven learning.
Addressing the Scalability Trilemma for Mass Market Gamichain Applications Ayun Maduwinarti; Muhamad Ikhsan Mustopa; Henry Zainarthur
CORISINTA Vol 3 No 1 (2026): February
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/corisinta.v3n1

Abstract

The convergence of blockchain technology and gamification has led to the emergence of Gamichain ecosystems that are increasingly adopted in digital business environments to enhance user engagement, transparency, and value exchange. Despite this potential, the blockchain scalability trilemma, which involves trade offs between decentralization, security, and scalability, remains a critical barrier to the mass market adoption of Gamichain applications. This study aims to analyze how the scalability trilemma influences the feasibility of large scale Gamichain systems and to identify architectural strategies that enable high frequency transactions and large user participation while preserving system trust. A qualitative conceptual approach supported by comparative case analysis is employed, examining representative implementations such as Axie Infinity, StepN, and Polygon based gaming platforms. The analysis evaluates existing scalability solutions including Layer 2 rollups, sharding mechanisms, and hybrid off chain and on chain architectures across key dimensions of security, decentralization, transaction throughput, and user experience. The results indicate that Layer 2 and hybrid architectures provide the most viable balance for mass market Gamichain deployment, as they significantly improve performance efficiency and transaction cost without substantially undermining decentralization and security guarantees. This study contributes an applied evaluation framework that connects blockchain scalability discourse with practical digital business requirements. In conclusion, scalable Gamichain adoption depends not only on technical scalability solutions but also on strategic architectural alignment with user experience optimization, regulatory readiness, and sustainable digital innovation, thereby supporting broader participation in the global digital economy.
Optimizing Inclusion and Accessibility in Digital Education with Adaptive Learning Riya Widayanti; Henry Zainarthur; Maulana Abbas
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.v4i2.990

Abstract

This study examines the persistent challenges in achieving inclusive and accessible digital education, particularly for learners with diverse abilities, varying levels of technological readiness, and different socio-economic backgrounds, despite rapid advancements in educational technology. It focuses on optimizing inclusion and accessibility through an adaptive digital education approach that responds to individual learner needs and specific learning contexts. A mixed-methods research design was employed, combining a quantitative survey administered to students and educators engaged in digital learning environments with qualitative interviews to 200 respondents capture in-depth experiential insights. Structural Equation Modeling (SEM) was utilized to analyze the relationships among adaptive learning features, accessibility, and inclusive learning outcomes. The findings demonstrate that adaptive digital education systems significantly enhance learning accessibility, learner engagement, and perceived inclusivity. Personalization mechanisms and adaptive content delivery emerged as the strongest contributing factors. Moreover, adaptive technologies were shown to reduce learning barriers for students with varying learning speeds and specific accessibility requirements. Overall, the study confirms that optimizing adaptive digital education is essential for fostering inclusive and accessible learning environments and underscores the need to integrate adaptive system design into educational policies and digital learning strategies to promote equitable educational opportunities across diverse contexts.
Reliable Machine Learning Models for Energy Optimization in Smart Green Cities Ignatius Agus Supriyono; Mochamad Heru Riza Chakim; Henry Zainarthur; Dimas Aditya Prabowo
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.v4i2.1092

Abstract

Rapid urbanization and increasing energy consumption have intensified the need for intelligent approaches that support sustainable and efficient energy management in smart green cities. This study investigates the effectiveness of machine learning models in improving energy demand forecasting and energy optimization through a reliability-oriented evaluation framework. The research utilizes a real-world smart city energy consumption dataset comprising 17,520 hourly observations collected between January 2022 and December 2023. Three machine learning models, namely Random Forest, Support Vector Machine (SVM), and Long Short-Term Memory (LSTM), were developed and evaluated using 30 independent execution runs. Model performance was assessed through Mean Absolute Error (MAE), Root Mean Square Error (RMSE), coefficient of determination (R2), reliability analysis, and interpretability consistency measurements. The results demonstrate that LSTM achieved the best predictive performance with an MAE of 0.31, RMSE of 0.45, and R2 of 0.93, outperforming Random Forest and SVM across all evaluation metrics. Furthermore, LSTM exhibited the highest reliability score of 0.912 and superior explanation stability, indicating robust and consistent performance under repeated executions. The forecasting outputs were integrated into an energy optimization framework, resulting in reductions in peak energy loads and overall electricity consumption. These findings confirm that reliable and explainable machine learning models can support adaptive, data-driven energy management strategies capable of enhancing operational efficiency and sustainability in urban environments. The proposed framework contributes to the development of trustworthy intelligent systems for smart green cities and supports the achievement of sustainable development objectives related to clean energy, sustainable communities, and climate action.
Co-Authors Agung Rizky Asri Asri Aswadi Jaya Ayun Maduwinarti Dimas Aditya Prabowo Fitra Putri Oganda Hari Bakti Mardikantoro Hayatun Nufus Ignatius Agus Supriyono Maulana Abbas Mochamad Heru Riza Chakim Muh Fahrurrozi Muhamad Ikhsan Mustopa Nengah Sukendri Rahayu Pristiwati Riya Widayanti Sandy Setiawan Sibagariang, Susy Alestriani Subyantoro Titih Nursugiharti Vinkan Likita Zulfadli Ardiansyah