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All Journal Technomedia Journal ADI Journal on Recent Innovation (AJRI) International Transactions on Artificial Intelligence (ITALIC)
Zainarthur, Henry
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Religion Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Education Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science

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Assessing User Satisfaction in Hadirku Through an Extended TAM Framework Jaya, Aswadi; Zainarthur, Henry; Sijabat, Apriani; Dina, Aulia Rahma; Faturahman, Adam
International Transactions on Artificial Intelligence Vol. 4 No. 1 (2025): November
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/italic.v4i1.937

Abstract

The rapid advancement of Information and Communication Technology (ICT) has accelerated the transition from manual, paper based attendance systems toward digital platforms that promote efficiency and environmental sustainability through reduced paper usage. In this context, the Hadirku online attendance platform has been increasingly adopted across educational, organizational, and eventmanagement settings. This study employs the Technology Acceptance Model (TAM), extended with Service Quality, Organizational Support, and Information Security, to examine determinants of User Satisfaction and Continued Usage. A quantitative design was implemented with 200 valid respondents, and SmartPLS was used to assess construct validity and structural relationships. Reliability was strong (Cronbach’s α = 0.77–0.92), and model fit met recommended thresholds (SRMR = 0.057; NFI = 0.91). The study aims to analyze how perceived usefulness, ease of use, service quality, information security, and organizational support influence user engagement with Hadirku. Findings reveal that information security and perceived usefulness significantly predict continued usage intention, while perceived ease of use and organizational support enhance user satisfaction. Users overall reported positive experiences and strong behavioral intention to continue using the platform. This study contributes to digital transformation and Green ICT literature by providing an extended TAM framework that explains sustained engagement with online attendance systems. The results offer practical insights for platform developers and institutions seeking to optimize user trust, system reliability, and sustainable administrative practices.
AI Agent Based Service Innovation to Enhance Efficiency and User Experience Cahyono, Dwi; Atmaja, Hanung Eka; Zainarthur, Henry
Technomedia Journal Vol 10 No 3 (2026): February
Publisher : Pandawan Incorporation, Alphabet Incubator Universitas Raharja

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/tmj.v10i3.2585

Abstract

The innovation of services based on Artificial Intelligence (AI) Agent has become a key strategy in improving operational efficiency, service quality, and user experience across various digital business sectors. AI Agent, utilizing natural language processing, machine learning, and realtime data analysis, can automate service processes that previously required manual interaction, such as customer responses, recommendations, and processing complex information. This study aims to analyze how the application of AI Agent can accelerate service responses, improve information accuracy, and create more personalized interactions for users. The research method used is a literature review from reputable journals, academic books, and industry reports, which are then analyzed descriptively to identify the adoption patterns of AI Agent across various digital platforms such as e-commerce, financial services, education, and creative industries. The results of the literature synthesis show that AI Agent can reduce operational workload by up to 40%, accelerate service response time by up to 60%, and enhance user satisfaction through adaptive interactions tailored to individual preferences and behaviors. Additionally, the implementation of AI Agent also proves to improve service consistency, expand operational scalability, and reduce the risk of human error in service processes. These findings emphasize that the integration of AI Agent not only enhances the efficiency and effective- ness of digital business processes but also plays a key role in creating strategic innovation, strengthening competitiveness, and building a more responsive and valuable service experience for users in the digital era.
Analysis of Inorganic Waste Classification Orange Box Based on TensorFlow Lite using Raspberry Pi 5 Aini, Qurotul; Faturahman, Adam; Agustian, Harry; Aritonang, Frengky Jonathan; Zainarthur, Henry
ADI Journal on Recent Innovation (AJRI) Vol. 7 No. 2 (2026): March
Publisher : ADI Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34306/ajri.v7i2.1428

Abstract

While Smart City initiatives are evolving, waste management infrastructure remains a critical bottleneck, often hindered by high energy dependency and latency issues associated with cloud computing. Traditional automated solutions lack the autonomy required for scalable, outdoor deployment. This research introduces Orange Box a self-sustaining Edge-AI waste classifier designed to bridge the gap between high-performance computing and energy efficiency. The primary goal is to demonstrate that complex Deep Learning tasks can be executed locally on renewable energy without sacrificing classification precision. The system orchestrates a MobileNetV2 architecture on the Raspberry Pi 5, utilizing TensorFlow Lite (TFLite) quantization to drastically reduce computational load. Uniquely, this Green IoT node is fully decoupled from the power grid, driven by a custom power management system utilizing a 100Wp monocrystalline solar panel to sustain both the neural processing unit and robotic actuators. Experimental benchmarks reveal a robust 92% classification accuracy with an inference latency of just 45ms, significantly outperforming previous edge-device generations. Crucially, energy analysis validates operational autonomy for up to 72 hours without sunlight, confirming the system’s reliability for continuous urban deployment. This study demonstrates that the convergence of quantized Edge AI and solar harvesting is not merely theoretical but a deployable standard for the next generation of Smart City infrastructure, directly advancing the Sustainable Development Goals (SDGs) for sustainable urbanization.
Co-Authors Agustian, Harry Apriani Sijabat Aritonang, Frengky Jonathan Aswadi Jaya, Aswadi Dina, Aulia Rahma DWI CAHYONO Faturahman, Adam Hanung Eka Atmaja Qurotul Aini