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All Journal International Journal of New Media Technology Inspiration: Jurnal Teknologi Informasi dan Komunikasi
Muhammad Ulil Amri
Institut Teknologi dan Bisnis Kalla
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering

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Lightweight Deep Learning for Mobile Crab Larvae Detection in Aquaculture Environments Furqan Zakiyabarsi; Yabes Dwi Nugroho; Muhammad Muhaimin Nur; Muhammad Ulil Amri; Akbar Hendra; Arizal Arizal
Inspiration: Jurnal Teknologi Informasi dan Komunikasi Vol. 15 No. 2 (2025): Inspiration: Jurnal Teknologi Informasi dan Komunikasi
Publisher : Pusat Penelitian dan Pengabdian Pada Masyarakat Sekolah Tinggi Manajemen Informatika dan Komputer AKBA Makassar

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Abstract

Efficient monitoring of crab larvae remains a critical challenge in aquaculture, as early-stage mortality is high due to the lack of practical and scalable detection systems. Although deep learning-based object detection has demonstrated strong performance for small aquatic organisms, many existing approaches are computationally intensive and unsuitable for mobile or resource-constrained hatchery environments. This study investigates the feasibility of lightweight deep learning models for mobile crab larvae detection in aquaculture environments. Using crab larvae at the zoea stage as a case study, lightweight YOLO-based architectures are evaluated to analyze the trade-off between detection accuracy and computational efficiency. The results indicate that extremely lightweight models offer minimal memory requirements and high deployment feasibility, but with limited detection accuracy. In contrast, more advanced lightweight architectures achieve substantially higher accuracy at the cost of increased model size and computational complexity. Rather than focusing solely on algorithmic comparison, this work emphasizes deployment-oriented insights for selecting appropriate lightweight detection models under practical mobile constraints. The findings demonstrate that lightweight deep learning provides a viable foundation for mobile aquaculture applications and establish a baseline for future optimization toward efficient on-device deployment.
Implementation of Ergonomic Criteria for Evaluating the Logistic Mobile App User Interface Design Hany Alexandra; Muhammad Ulil Amri; Fadhilah Eka Putri; Andi Jamiati Paramita; Rena Nainggolan; Fenina Adline Twince Tobing
IJNMT (International Journal of New Media Technology) Vol 12 No 2 (2025): Vol 12 No 2 (2025): IJNMT (International Journal of New Media Technology)
Publisher : Universitas Multimedia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31937/ijnmt.v12i2.4593

Abstract

The rapid growth of digital logistics has increased dependence on mobile delivery applications to manage shipments, package information, and courier operations under strict time constraints. Nowdays, ergonomic and usable user interface design is essential to reduce cognitive load, minimize errors, and maintain operational efficiency. However, empirical usability research focusing specifically on delivery service applications remains limited, particularly studies conducted in realistic usage contexts. This study evaluates the ergonomic quality of a mobile delivery application using online usability testing. A quantitative evaluation framework is applied, employing three established metrics: the System Usability Scale (SUS), task completion time, and error rate. These measures capture perceived usability, interaction efficiency, and error occurrence during representative delivery-related tasks. The findings reveal key usability issues related to navigation structure, information presentation, and interaction flow that negatively affect user performance. The result, study proposes data-driven UI design improvements aimed at enhancing clarity, efficiency, and cognitive ergonomics. The novelty is its integration of online usability testing with ergonomic evaluation in the delivery application domain, an area that remains underrepresented in recent human–computer interaction research. The outcomes provide scalable evaluation benchmarks and practical design guidance to support the development of more efficient, user-friendly, and ergonomically appropriate mobile delivery applications.
Co-Authors Akbar Hendra Andi Jamiati Paramita ARIZAL Fadhilah Eka Putri Fenina Adline Twince Tobing Furqan Zakiyabarsi Hany Alexandra Muhammad Muhaimin Nur Rena Nainggolan Yabes Dwi Nugroho