Article Per Year (5 Year)
p-Index From 2021 - 2026
0.23
P-Index
This Author published in this journals
All Journal Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer)
Rifki Munawar, Muhammad
Unknown Affiliation
Computer Science & IT Decision Sciences, Operations Research & Management

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

 1 
Rancang Bangun Smart Squeeze Cage Berbasis Internet of Things untuk Monitoring Bobot dan Rekomendasi Pakan Ternak Rania, Ghina; Rifki Munawar, Muhammad; Bonardo Marpaung, Ilham; Tiftazani, Hafiz; Nasir, Muhammad
JURNAL FASILKOM Vol. 16 No. 2 (2026): Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer)
Publisher : Unversitas Muhammadiyah Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/jf.v16i2.11980

Abstract

The development of precision livestock farming requires robust and automated data collection tools to minimize animal stress and improve farm efficiency. Traditional livestock weighing methods often lack immediate data access and do not support dynamic resource planning. This study designs and implements a Smart Squeeze Cage based on the Internet of Things (IoT) integrated with a Random Forest Regressor algorithm for real-time livestock weight monitoring and feed optimization. The system integrates four load cell sensors connected in parallel, an HX711 amplifier, and an ESP32 microcontroller embedded within a customized Squeeze Cage structure. Weight data is transmitted via wireless protocol to a centralized cloud database using Supabase and PostgreSQL. Based on historical data, the Random Forest model automatically predicts livestock weight trends and calculates daily feed requirements to provide intelligent recommendations. Testing results indicate high sensor precision with an accuracy of 97% (error tolerance of 0,5 kg), data transmission latency of 1.2 seconds, and a successful data delivery rate of 99.1%. This system offers a seamless, non-invasive solution for data-driven livestock management in modern farming environments.
Co-Authors Bonardo Marpaung, Ilham Muhammad Nasir Rania, Ghina Tiftazani, Hafiz