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All Journal Inspiration: Jurnal Teknologi Informasi dan Komunikasi Journal of Embedded Systems, Security and Intelligent Systems
Yabes Dwi Nugroho
Institut Teknologi dan Bisnis Kalla
Computer Science & IT

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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.
A Comparative Study of Decision Tree and Gradient Boosting Tree Algorithms for Predicting College Enrollment Decisions of High School Students Rezty Amalia Aras; Utami Kusuma Dewi; Yabes Dwi Nugroho
Journal of Embedded Systems, Security and Intelligent Systems Vol 7 No 1 (2026): March 2026
Publisher : Program Studi Teknik Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/jessi.v7i1.10611

Abstract

The declining interest of high school students in pursuing higher education has become a major concern in Indonesia's education sector. This study aims to develop a data-driven predictive model to assist schools in identifying students’ decisions regarding further education. The study compares two popular classification algorithms, Decision Tree and Gradient Boosted Tree, using a dataset of 300 high school students comprising 10 attributes such as school accreditation, parental income, interest level, and residential status. The research method involves data preprocessing, model training, and performance evaluation using a confusion matrix to measure accuracy, precision, and recall. The results show that the Decision Tree algorithm achieved an accuracy of 76.67%, with a precision of 78.57% and a recall of 73.33% for the "college" class. Meanwhile, the Gradient Boosted Tree produced an accuracy of 73.33%, with a strength in recall for the "not attending college" class at 80%, but was less optimal in detecting students who pursued higher education. It can be concluded that the Decision Tree outperforms in terms of accuracy and interpretability, making it more suitable for use in school environments as a decision-support tool for early intervention, scholarship programs, and career counseling.
Co-Authors Akbar Hendra Aras, Rezty Amalia ARIZAL Furqan Zakiyabarsi Muhammad Muhaimin Nur Muhammad Ulil Amri Utami Kusuma Dewi