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All Journal Jurnal Pendidikan Vokasi International Journal of Artificial Intelligence Research Journal of Research in Social Science and Humanities
Kindiasari, Aktansi
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Religion Arts Humanities Computer Science & IT Economics, Econometrics & Finance Education Electrical & Electronics Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Social Sciences Other

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Fashion as an Expression of Cultural Identity in the Digital Age Wardaya, Awwali Ibnu; Bestari, Afif Ghurub; Sulistiyanto, Sulistiyanto; Kindiasari, Aktansi
Journal of Research in Social Science and Humanities Vol 4, No 1 (2024)
Publisher : Utan Kayu Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47679/jrssh.v4i1.118

Abstract

Fashion has long been a powerful medium for expressing cultural identity, reflecting various communities' values, traditions, and social dynamics. This expression has evolved dramatically in the digital age, shaped by the pervasive influence of technology and social media. This study explores how fashion serves as a conduit for cultural identity in the contemporary digital landscape. It examines the interplay between traditional fashion practices and modern digital platforms. It highlights how individuals and communities use fashion to navigate and assert their cultural identities in an increasingly globalised world. The research investigates the role of digital platforms, such as Instagram, TikTok, and fashion blogs, in disseminating and transforming cultural fashion trends. These platforms provide a space for cultural exchange and the democratisation of fashion, allowing underrepresented voices to showcase their heritage and creativity. Additionally, the study delves into the impact of virtual fashion shows, digital influencers, and online retail on cultural identity expression. It considers how these elements contribute to preserving and evolving traditional fashion practices. Furthermore, the research addresses the complexities of cultural appropriation in the digital age, where fashion trends can be rapidly adopted and commercialised, often detached from their cultural significance. By analysing case studies and engaging with fashion designers, influencers, and consumers, the study offers insights into the ethical considerations and responsibilities involved in cultural fashion representation. The findings suggest that while digital platforms can facilitate celebrating and sharing cultural identities, they pose challenges related to authenticity, appropriation, and commercialisation. This duality underscores the need for a nuanced understanding of fashion as a cultural expression in the digital era. The study concludes with recommendations for fostering a more inclusive and respectful digital fashion ecosystem that honours and preserves cultural identities while embracing innovation and diversity. Through this exploration, the research aims to contribute to the broader discourse on cultural identity, fashion, and digital media, offering valuable perspectives for academics, industry professionals, and cultural advocates.
Development of Human Resource Management Information System to Improve Employee Performance Efficiency and Effectiveness Oktaviannur, Moh.; Kindiasari, Aktansi
International Journal of Artificial Intelligence Research Vol 8, No 1.1 (2024)
Publisher : Universitas Dharma Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29099/ijair.v8i1.1.1198

Abstract

The development of a Human Resource Management Information System (HRMIS) aims to enhance the efficiency and effectiveness of employee performance. This study focuses on designing and implementing an integrated HRMIS that automates various HR functions such as recruitment, attendance tracking, performance evaluation, and employee development. By leveraging advanced information technologies, the HRMIS provides real-time data and analytics, facilitating informed decision-making and strategic planning for HR managers. The system's user-friendly interface ensures ease of use and accessibility, promoting higher adoption rates among employees and management. Empirical data collected from case studies and surveys conducted within organizations demonstrate significant improvements in operational efficiency, reduced administrative burden, and enhanced employee satisfaction and productivity. The findings suggest that a well-implemented HRMIS can be a critical tool in optimizing human resource processes, aligning HR strategies with organizational goals, and ultimately driving overall business performance. Future research should explore the integration of emerging technologies such as artificial intelligence and machine learning to further enhance the capabilities of HRMIS.
Application of Computer Vision and Pattern Recognition in Automated Quality Inspection of Industrial Products Nurdiyanto, Heri; Kindiasari, Aktansi; Sulistiyanto, Sulistiyanto
International Journal of Artificial Intelligence Research Vol 9, No 2 (2025): December
Publisher : Universitas Dharma Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29099/ijair.v9i2.1507

Abstract

Quality inspection is a critical process in industrial production to ensure that products meet predefined standards and specifications. Traditionally, quality inspection has relied heavily on manual visual checks, which are time-consuming, subjective, and prone to human error. This study explores the application of computer vision and pattern recognition techniques to develop an automated quality inspection system for industrial products. The proposed system employs high-resolution cameras and image processing algorithms to capture and analyze visual features of products in real-time on the production line. Key techniques utilized include feature extraction, edge detection, and texture analysis to identify defects such as scratches, dents, and dimensional inaccuracies. Pattern recognition algorithms, such as support vector machines (SVM) and convolutional neural networks (CNN), are trained on large datasets of product images to classify items as acceptable or defective with high accuracy. The system was tested on a dataset collected from a manufacturing facility producing metal components. Experimental results demonstrate that the automated system achieved an inspection accuracy of 98%, significantly outperforming manual inspection methods in terms of speed and consistency. Furthermore, the integration of this system into the production line reduced inspection time by approximately 70% and minimized production downtime. This research highlights the potential of intelligent informatics, particularly computer vision and pattern recognition, in enhancing the efficiency, reliability, and scalability of industrial quality control processes. The findings suggest that such automated systems can contribute significantly to the advancement of Industry 4.0 by enabling smart manufacturing practices and reducing dependence on manual labor. Future work will focus on extending the system to handle more complex products and dynamic production environments
An AI-driven framework for learning analytics and operational optimization in technology and vocational education: Bridging industrial engineering and informatics Nurdiyanto, Heri; Hernandes , Leonel; Hammad, Jehad A.H; Kindiasari, Aktansi
Jurnal Pendidikan Vokasi Vol. 15 No. 3 (2025): November
Publisher : ADGVI & Graduate School of Universitas Negeri Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21831/jpv.v15i3.95617

Abstract

This study aims to present an artificial intelligence-based framework that combines learning analytics with operational optimization, which can address the ever-present problems concerning technology and vocational education. In the case of vocational institutions, it has been noticed that while learning environments are increasingly embracing the incorporation of digital technologies, the connection between the use of data for educational outcomes and operational decision-making remains disconnected. In many instances, learning-related data is analyzed separately from production-oriented activities, which include scheduling, resource allocation, and process efficiency, despite the fact that these activities are part of the learning process in the factory and learning environments. This study aims to address the disconnect between the use of learning-related data and production-oriented activities through the incorporation of perspectives from industrial engineering and informatics, which are integrated into a single framework that is oriented towards artificial intelligence. Machine learning is utilized for the representation of learning processes, while optimization techniques are used for decision-making regarding task allocation, scheduling, and resource allocation. Instead of being restricted to a particular application domain, the framework is developed with the idea of adaptability so that it can be used across different contexts of vocational education. An empirical study was conducted within a particular context of a technology-oriented vocational education domain to assess the viability of the proposed framework. It was found that the integration of learning analytics with operational optimization provides a more consistent outcome compared to the individual analysis of these factors. It was also found that the proposed AI-based framework provides a better outcome for the assessment of competency as well as the prediction of performance, which leads to the efficiency of managing a production-oriented learning process. Such findings indicate the ability of the application of AI to support the field of vocational education more comprehensively. This study contributes to the field of research by proposing an interdisciplinary framework that goes beyond the idea of individual technological tools to offer a more comprehensive perspective on the adoption of AI within the context of vocational education.
Co-Authors Bestari, Afif Ghurub Hammad, Jehad A.H Hernandes , Leonel Moh. Oktaviannur, Moh. Nurdiyanto, Heri Sulistiyanto Sulistiyanto, Sulistiyanto Wardaya, Awwali Ibnu