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All Journal Inspiration: Jurnal Teknologi Informasi dan Komunikasi Journal of Embedded Systems, Security and Intelligent Systems
Muhammad Muhaimin Nur
Institut Teknologi dan Bisnis Kalla
Computer Science & IT

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DESIGN AND CONSTRUCTION OF WEBSITE CARBON CREDIT RECORDING APPLICATION Muhammad Muhaimin Nur; Andi Hutami Endang
Journal of Embedded Systems, Security and Intelligent Systems Vol 5, No 3 (2024): November 2024
Publisher : Program Studi Teknik Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/jessi.v5i3.4289

Abstract

Global climate change is caused by the increasing emissions of greenhouse gases, especially CO2, which have significant impacts on human-induced climate change. In addressing this issue, Indonesia is committed to reducing greenhouse gas emissions by 29% by 2030. The development of a carbon accounting system using the SDLC method in this research is an essential step towards achieving this target. This system helps companies to effectively reduce and report carbon emissions, supports transparency, and facilitates interaction among various stakeholders. Through research methods such as interviews, surveys, and the SDLC Waterfall method, the carbon accounting system can be implemented as an effective tool in managing carbon data, reducing emissions, and improving environmental quality.
Design of Employee Absence System Using Web-Based Realtime Geo-Location Library Nur Azizah Yusuf; Muhammad Muhaimin Nur; Andi Hutami Endang
Journal of Embedded Systems, Security and Intelligent Systems Vol 6, No 2 (2025): June 2025
Publisher : Program Studi Teknik Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/jessi.v6i2.6018

Abstract

Employee performance is one of the most critical aspects of a company or organization. This is because the company's goals or targets can only be achieved if employees can demonstrate good performance in achieving them. One way to ensure this is by maintaining employee discipline. This study proposes a web-based employee attendance system using the Geo-Location Realtime library to monitor employee discipline at PT. Portal Indonesia Perkasa, which previously relied solely on a fingerprint-based attendance system. The method used is the waterfall method. Which employs a systematic and sequential approach. The system design includes flowcharts, use case diagrams, data flow diagrams, and logical legal structures. The main features include real-time location tracking and automatic attendance recording. Implementation results show the system's effectiveness in facilitating accurate attendance monitoring and reporting.
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

Show Abstract | Download Original | Original Source | Check in Google Scholar

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.
Co-Authors Akbar Hendra Andi Hutami Endang, Andi Hutami ARIZAL Furqan Zakiyabarsi Muhammad Ulil Amri Nur Azizah Yusuf Yabes Dwi Nugroho