Article Per Year (5 Year)
p-Index From 2021 - 2026
0.408
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This Author published in this journals
All Journal Journal of Multidisciplinary Sustainability Asean Islamic Studies in the World
Siti Mariam
Universiti Teknologi Brunei
Religion Computer Science & IT Education Languange, Linguistic, Communication & Media Library & Information Science Other

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The Sociology of the “Hijrah” Movement Among Indonesian Urban Professionals and Celebrities: A Quest for Pious Modernity Wijaya Wijaya; Siti Mariam; Rina Haji Omar
Islamic Studies in the World Vol. 2 No. 6 (2025)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/isw.v2i6.2583

Abstract

Background. The “Hijrah” movement among Indonesian urban professionals and celebrities represents a contemporary form of Islamic revivalism shaped by the intersections of faith, modernity, and social identity. This study investigates how participants in this movement construct and perform piety within the context of urban consumer culture and digital visibility. Purpose. The research aims to explore the sociological dynamics underlying their motivations, networks, and self-representations as expressions of a quest for “pious modernity. Method. Using a qualitative ethnographic approach, the study combines in-depth interviews, participant observation, and digital ethnography across Jakarta, Bandung, and Surabaya from 2023 to 2024. Results. Findings reveal that the hijrah phenomenon is not merely a religious transformation but also a form of social repositioning and identity negotiation in response to moral uncertainty in modern life. Hijrah communities utilize social media, fashion, and entrepreneurial ventures to embody Islamic ethics while remaining embedded in urban capitalist systems. Conclusion. The study concludes that the hijrah movement exemplifies a hybrid religiosity merging spiritual authenticity with middle-class aspirations thereby illustrating the ongoing negotiation between Islam and modernity in Indonesia’s post-reform urban culture.
AI-Assisted Early Detection of Crop Disease Using Hyperspectral Imaging and Deep Learning in Smallholder Farms Ardi Azhar Nampira; Siti Mariam
Journal of Multidisciplinary Sustainability Asean Vol. 2 No. 3 (2025)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/ijmsa.v2i3.2305

Abstract

Background. Crop disease is a major threat to smallholder farmers who lack access to timely diagnostic tools. Traditional detection methods rely on visual inspection and often occur too late to prevent significant yield losses. Early detection using hyperspectral imaging and artificial intelligence presents a transformative solution for precision agriculture in resource-limited settings. Purpose. This study aims to develop and evaluate an AI-assisted early detection system for crop diseases using hyperspectral imaging and deep learning, tailored for application in smallholder farms. Method. A convolutional neural network (CNN) model was trained on hyperspectral data collected from five farm sites, with ground-truth annotations by agricultural experts. The model’s performance was evaluated using standard classification metrics, including accuracy, precision, recall, and F1-score. A case study was also conducted to assess real-world applicability. Results. The model achieved an average detection accuracy of 94.2% across all locations, with F1-score reaching 0.92 when using hyperspectral features. Confusion matrix analysis indicated high true positive and true negative rates, confirming reliability. In a field case, early diagnosis enabled targeted intervention and improved yield by 22% compared to prior seasons. Conclusion. The integration of hyperspectral imaging and deep learning offers a practical and scalable solution for early disease detection in smallholder farms. The system demonstrates high accuracy, adaptability, and operational feasibility in real-world conditions. Future work should focus on expanding crop and disease types, user interface development, and integration with mobile and IoT-based platforms.
Co-Authors Ardi Azhar Nampira Rina Haji Omar Wijaya Wijaya