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All Journal Jurnal EECCIS Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Edu Elektrika Journal
Rizky Ajie Aprilianto
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Computer Science & IT Electrical & Electronics Engineering Engineering

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Journal : Jurnal EECCIS

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Systematic Literature Review: Optimizing Broiler Chicken Cage Temperature and Humidity Fidel Lusiana Putri; Deva Kurnia Setiawan; Arsmanda Adi Nugraha; Feddy Setio Pribadi; Rizky Ajie Aprilianto
Jurnal EECCIS (Electrics, Electronics, Communications, Controls, Informatics, Systems) Vol. 18 No. 3 (2024)
Publisher : Faculty of Engineering, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21776/jeeccis.v18i3.1694

Abstract

Broiler production is highly dependent on environmental conditions, especially temperature and humidity in the cage. This research is a Systematic Literature Review that uses the PRISMA method to optimize the temperature and humidity of broiler cages. A total of 202 journals were found through Google Scholar and other repositories, with an additional 16 journals manually. After filtering, 38 journals were selected based on publication year 2019-2024. From the review of the selected literature, five research questions were derived that led to new understanding and solutions to the problems in the research topic. The results show that temperature and humidity significantly impact broiler performance, including health, welfare, and productivity. An IoT-based monitoring and control system is proven to assist with real-time monitoring and control of temperature and humidity, improving farm efficiency and effectiveness. Supporting factors for implementing this system include a friendly user interface, mobile applications, and real-time notifications. The application of IoT technology in broiler farming has the potential to provide significant benefits to farmers and the livestock industry as a whole.
A Systematic Literature Review on Machine Learning Techniques for Enhancing Embedded Hardware Reliability Desy Natalia; Cahya Renita Pulse; Rizal Ramadhan; Rama Fahrizal Kusuma; Rizky Ajie Aprilianto; Feddy Setio Pribadi
Jurnal EECCIS (Electrics, Electronics, Communications, Controls, Informatics, Systems) Vol. 19 No. 3 (2025)
Publisher : Faculty of Engineering, Universitas Brawijaya

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

Abstract

Embedded systems (ES) have played a vital role in industrial automation and critical infrastructure, but their reliability has often been compromised by hardware faults, leading to downtime and safety concerns. Traditional threshold-based fault detection methods have frequently failed to adapt to dynamic environments and have struggled to identify early-stage failures. This study reviewed the effectiveness of artificial intelligence (AI), specifically machine learning (ML) models, for fault detection in ES. A systematic review methodology was employed to analyze the diagnostic performance of several deep learning (DL) architectures, including hybrid convolutional neural network-long short-term memory (CNN-LSTM) models, when implemented on resource-constrained edge devices. The results showed that optimized AI models achieved higher diagnostic accuracy and earlier fault identification compared to conventional approaches. Furthermore, these models enabled real-time, energy-efficient operation on platforms such as Raspberry Pi and ESP32 microcontrollers. It was concluded that AI-driven solutions significantly enhanced predictive maintenance and operational reliability in embedded system applications, demonstrating their transformative potential for future industrial systems.
Co-Authors Alam, Mohammad Mahruf Annuruddin Tsanasti Yassar Apriansyah Wibowo Arsmanda Adi Nugraha Arvina Rizqi Nurul’aini Cahya Renita Pulse Desy Natalia Deva Kurnia Setiawan Farah Syarifah, Dian Feddy Setio Pribadi Feddy Setio Pribadi Fidel Lusiana Putri Ghesya Arnellia Brillian Muhammad Irfan Ardiansyah Rama Fahrizal Kusuma Rizal Ramadhan Waskito, Deswal