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All Journal ADI Pengabdian kepada Masyarakat Jurnal (ADIMAS Jurnal) BEAM
Ninda Lutfiani
Satya Wacana Christian University, Indonesia
Humanities Social Sciences

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Optimizing the Potential of 3D Printing for Industry from Prototype to Production Mohamad Agus Setiawan; Ageng Setiani Rafika; Ramzi Zainum Ikhsan; Ninda Lutfiani; Richard Evans
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.v6i1.1193

Abstract

The adoption of 3D printing technology has increased across various industrial sectors due to its ability to accelerate product development, reduce production costs, and support flexible manufacturing. However, implementation at the community and small–medium industry level remains limited due to gaps in technical skills, digital design knowledge, and structured technology transfer. This community service program aims to enhance the practical capacity of industrial partners in utilizing 3D printing, particularly in transitioning from prototyping to small-scale production. The program used the Participatory Action Research (PAR) approach through needs assessment, technology transfer training, hands-on practice, mentoring, and evaluation. Participants were guided in 3D modeling, printer setup, material selection, and print optimization. The program improved participants technical skills and confidence in producing functional prototypes according to industrial needs. Production time was reduced, design modification became more efficient, and opportunities for product innovation increased, supporting independent and adaptive manufacturing. This program successfully strengthened community-based industrial capabilities in adopting digital manufacturing, contributing to SDG 8 (Decent Work and Economic Growth) and SDG 9 (Industry, Innovation, and Infrastructure). Future activities are recommended to expand collaborative product development and sustain innovation networks between academia and industry.
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

Show Abstract | Download Original | Original Source | Check in Google Scholar

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 Ageng Setiani Rafika Kristina Vaher Mohamad Agus Setiawan Ramzi Zainum Ikhsan Richard Evans Steven Harazaki Lase Umi Rusilowati