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All Journal ADI Pengabdian kepada Masyarakat Jurnal (ADIMAS Jurnal) BEAM
Umi Rusilowati
Pamulang University, Indonesia
Humanities Social Sciences

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Community Empowerment to Address Technology and HR Barriers in Rare Earth Development Felix Sutisna; Ester Ananda Natalia; Dwi Andayani; Umi Rusilowati; Kristina Vaher
ADI Pengabdian Kepada Masyarakat Vol 6 No 1 (2025): ADI Pengabdian Kepada Masyarakat
Publisher : ADI Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34306/adimas.v6i1.1340

Abstract

Indonesia holds significant potential in Rare Earth Elements (REE), which play an essential role in supporting the clean energy transition and strengthening national industrialization. However, the downstream development of REE still faces major obstacles in the aspects of technology, infrastructure, and human resources (HR), resulting in suboptimal national value creation. This study aims to describe community empowerment strategies as an alternative approach to overcoming REE downstreaming challenges in Indonesia, emphasizing community involvement in enhancing technological capacity, developing local infrastructure, and improving the quality of human resources. The research employs a qualitative approach based on literature review, descriptive analysis, and reflections from community service practices. Data were obtained from secondary sources such as academic journals, policy reports, and previous studies. The findings indicate that community empowerment can be achieved through local capacity building, collaboration among academics, industry, and communities, as well as infrastructure development tailored to regional needs. This strategy aligns with the Sustainable Development Goals (SDGs), particularly Goal 8 which focuses on decent work and economic growth, Goal 9 which promotes industry, innovation, and infrastructure, and Goal 11 which emphasizes sustainable cities and communities, highlighting inclusive economic development and community resilience. The downstreaming of REE cannot rely solely on top-down industrial policies. Its success requires active community participation through integrated and continuous empowerment strategies to achieve inclusive and sustainable national development.
Machine Learning and Blockchain Integration for RealTime Sentiment Analysis and Digital Rupiah Ecosystem Ninda Lutfiani; Umi Rusilowati; Steven Harazaki Lase; Kristina Vaher
Bridging of Emerging AI and Media Broadcasting (BEAM) Vol. 1 No. 1 November (2025): Bridging of Emerging AI and Media Broadcasting
Publisher : Sundara Publishing

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Abstract

The rapid growth of digital streaming platforms and global online communities has significantly increased the volume of user generated content, making it difficult for organizations to understand viewer engagement trends in real time. This study develops and evaluates machine learning models for real time sentiment analysis to identify global viewer engagement patterns across large scale digital data streams. Analytical framework is employed by collecting viewer comments and interaction data from multiple online platforms, followed by preprocessing techniques including text normalization, tokenization, and feature extraction. Several machine learning algorithms, including supervised classification models and natural language processing techniques, are trained and evaluated to detect positive, negative, and neutral sentiments in real time. Model performance is assessed using accuracy, precision, recall, and F1 score to determine the most effective approach for large scale sentiment monitoring. The findings demonstrate that optimized machine learning models significantly improve the accuracy and responsiveness of real time sentiment detection, enabling more reliable identification of global viewer engagement trends and behavioral patterns. The integration of automated sentiment analysis also enhances the capability of organizations to process large volumes of streaming textual data efficiently. This research highlights the importance of machine learning driven sentiment analysis systems as strategic tools for understanding global audience engagement, supporting data driven decision making, and improving adaptive content strategies in rapidly evolving digital media environments.
Co-Authors Dwi Andayani Ester Ananda Natalia Felix Sutisna Kristina Vaher Kristina Vaher Ninda Lutfiani Steven Harazaki Lase