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Strategic Optimization of Digital Business Models through SEO Implementation Apriani, Desy; Faturahman, Adam; Ningrum, Fanani Islamia; Lachlan, Nicholas; Rahardja, Untung
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/dhcye328

Abstract

The development of technology and digital media has intensified competition among brands in establishing their presence in the digital space. This condition requires effective strategies to enhance brand awareness as a fundamental basis for building trust and brand recognition. Search Engine Optimization (SEO) is one of the strategic approaches that plays an important role in increasing brand visibility on search engines. This study aims to conceptually review the role of SEO in improving digital brand awareness. The study employs a literature review approach and conceptual analysis of the concepts, principles, and common practices of SEO in digital content management. The discussion focuses on the relationship between keyword optimization, content structure and quality, digital design, and information consistency with the level of brand exposure and recognition in the digital environment. The results of the conceptual review indicate that the implementation of SEO integrated with content strategy and digital design can enhance visibility, credibility, and audience recall toward a brand. Therefore, SEO functions not only as a search engine optimization technique but also as a strategic instrument for building and strengthening digital brand awareness sustainably in an increasingly competitive 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.