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Information Systems and Industrial Resource Planning Nova Suparmanto; Heri Nurdiyanto; Anisa Kharismawati
Journal of Research in Social Science and Humanities Vol 5, No 2 (2025)
Publisher : Utan Kayu Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47679/jrssh.v5i1.275

Abstract

Information Systems (IS) and Industrial Resource Planning (IRP) play a critical role in the optimization and management of industrial operations in today's fast-paced and technology-driven environment. Information Systems encompass a range of technologies and practices designed to collect, store, process, and distribute information within an organization, facilitating decision-making and process automation. Industrial Resource Planning, on the other hand, is an integrated approach to managing the resources required in manufacturing processes, including raw materials, human resources, machinery, and financial assets. The primary objective of IRP is to ensure that industrial operations are carried out efficiently and cost-effectively, with minimal waste and maximum productivity. The integration of IS and IRP allows organizations to better track resources, improve production schedules, and enhance supply chain management. By utilizing data analytics and advanced information technologies, companies can predict demand fluctuations, optimize inventory management, and reduce downtime. Furthermore, IS and IRP systems often incorporate real-time monitoring tools, enabling managers to make informed decisions based on up-to-date information about the production process. This leads to improved overall performance and the ability to respond to challenges quickly and effectively. An essential component of IRP is its ability to forecast and plan for future resource needs based on historical data and industry trends. By leveraging IS tools, such as enterprise resource planning (ERP) software, manufacturers can automate their resource allocation and streamline production processes. The alignment of IS and IRP contributes to achieving sustainability goals, as resource utilization can be carefully monitored and adjusted to minimize environmental impact. As industries continue to evolve with the advent of Industry 4.0 technologies, the role of IS and IRP systems will only grow in significance, shaping the future of manufacturing and industrial operations. The paper highlights the importance of these systems in modern industrial settings and explores their impact on operational efficiency, resource management, and strategic decision-making..
A Deep Learning Approach to Fake News Detection in Images with Text Overlay Heri Nurdiyanto; Nova Suparmanto; Beniati Lestyarini; Novan Edo Pratama
International Journal of Artificial Intelligence Research Vol 10, No 1 (2026): June
Publisher : Universitas Dharma Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29099/ijair.v10i1.1701

Abstract

This study addresses the growing challenge of fake news distributed through images containing overlaid text, a format that is widely used on social media because it is visually persuasive, easy to share, and often difficult to verify. Unlike conventional text-only misinformation, this type of content combines visual and textual cues that can strengthen misleading narratives and increase public trust in false information. To respond to this problem, the study proposes a deep learning approach for detecting fake news in text-overlay images by integrating image-based and text-based feature extraction within a unified classification framework. The proposed method begins with image preprocessing and text extraction from embedded captions, followed by feature learning using deep neural architectures to capture both semantic and visual patterns associated with deceptive content. The model is trained and evaluated on a labeled dataset of images containing news-like textual overlays, with performance assessed using accuracy, precision, recall, F1-score, and confusion matrix analysis. The results indicate that the proposed approach is able to identify fake news content effectively and achieves promising classification performance compared with baseline machine learning methods. These findings indicate that combining visual representation and embedded textual information can significantly improve detection capability in multimodal misinformation settings. This study aids in the advancement of more flexible fake news detection systems, especially in digital contexts where altered or deceptive image-based content disseminates swiftly. The proposed framework is expected to support future research and practical implementation in automated content verification, social media monitoring, and digital information integrity management