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All Journal JIEET (Journal of Information Engineering and Educational Technology) Jurnal Riset dan Aplikasi Mahasiswa Informatika (JRAMI)
Adrian Fardan Andi
IPB University
Computer Science & IT Engineering

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Pengembangan Aplikasi Berbasis Kecerdasan Buatan untuk Diagnostik Penyakit dan Optimalisasi Ekosistem Akuarium Zolla Perdana Putra Harahap; Maulana Irfan; Farhan Hakim; Adrian Fardan Andi; Aditya Wicaksono; Gema Parasti Mindara; Inna Novianty; Lathifunnisa Fathonah; Endang Purnama Giri
Jurnal Riset dan Aplikasi Mahasiswa Informatika (JRAMI) Vol. 7 No. 01 (2026): Jurnal Riset dan Aplikasi Mahasiswa Informatika (JRAMI)
Publisher : Program Studi Teknik Informatika, FTIK, Universitas Indraprasta PGRI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30998/jrami.v7i01.235

Abstract

Indonesia boasts a high diversity of ornamental fish species, both freshwater and marine, making it a significant commodity in the global market. However, the high mortality rate of ornamental fish due to a lack of knowledge about disease diagnosis and aquarium ecosystem management remains a major challenge. To address this issue, FishCo was developed as an AI-based application designed to assist ornamental fish owners, particularly beginners, in diagnosing diseases and managing aquarium ecosystems. FishCo offers features such as disease diagnosis using Convolutional Neural Network (CNN) technology, ecosystem setup recommendations tailored to specific fish species, and consultations via FishBot. The application is built as a cross-platform solution, with a backend powered by Laravel and a mobile application developed in Android Studio using the Java programming language. Testing has shown that FishCo can accurately identify diseases, provide appropriate recommendations, and receive positive feedback from users. This application is expected to enhance the success rate of ornamental fish care, reduce mortality rates, and contribute to the preservation of Indonesia’s aquatic biodiversity.
Application of YOLOv11s Algorithm to Automate Egg Counting in Poultry Farms Muh. Hakkam Zakka Robbani; Akhfa Bagas Alfarizi; Adrian Fardan Andi; Gema Parasti Mindara; Endang Purnama Giri
JIEET (Journal of Information Engineering and Educational Technology) Vol. 10 No. 1 (2026)
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/jieet.v10n01.p39-50

Abstract

Chicken eggs are a strategic food commodity and a globally affordable source of animal protein. In the industrial poultry farming sector, accurate daily production data is fundamental for evaluating biological performance, feed efficiency, and supply chain management. However, the conventional manual counting process is highly susceptible to human error caused by physical fatigue and dynamic environmental conditions, resulting in significant data inaccuracies. To address these inefficiencies, this study aims to develop a precise automated egg counting system by implementing the You Only Look Once (YOLO) deep learning algorithm version 11s. This study employed an experimental approach that included data collection, manual annotation, and extensive augmentation techniques to ensure the model's robustness to variations in lighting and spatial orientation. The model was trained using a specific egg dataset and evaluated against standard object detection performance metrics. The experimental results demonstrated superior performance, with the model achieving 97.80% Precision, 98.60% Recall, and 99.10% Mean Average Precision at intersection over union 50%. Furthermore, the F1-Score reached 98.20%, indicating a strong balance between detection accuracy and sensitivity. The system successfully detected and counted eggs in a real-time environment while maintaining stability across various visual scenarios. In conclusion, the proposed architecture proved to be a highly effective solution for automating production recording in poultry farms. These findings offer a practical transition towards digitized precision farming, significantly reducing operational inefficiencies associated with manual monitoring while enabling sound managerial decision-making.
Co-Authors Aditya Wicaksono Akhfa Bagas Alfarizi Endang Purnama Giri Farhan Hakim Fathonah, Lathifunnisa Gema Parasti Mindara Inna Novianty Maulana Irfan Muh. Hakkam Zakka Robbani Zolla Perdana Putra Harahap