Article Per Year (5 Year)
p-Index From 2021 - 2026
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This Author published in this journals
All Journal Indonesian Journal of Electrical Engineering and Computer Science International Journal Software Engineering and Computer Science (IJSECS)
Lika Mariya
Sekolah Tinggi Teknologi Nusantara Lampung
Computer Science & IT

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Optimization of Energy Efficiency and Hatchability Rates in IoT-Based Egg Incubators Muhammad Ferdiansyah; Lika Mariya; Siti Kholifah K
International Journal Software Engineering and Computer Science (IJSECS) Vol. 5 No. 1 (2025): APRIL 2025
Publisher : Lembaga Komunitas Informasi Teknologi Aceh (KITA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/ijsecs.v5i1.3633

Abstract

This study aims to develop an Internet of Things (IoT)-based egg incubator integrated with renewable energy to enhance operational efficiency and hatching success rates. The system utilizes an ESP32 microcontroller to regulate temperature and humidity automatically, with a 100 Wp solar panel as the primary energy source. Testing results demonstrate that the IoT-based system maintains optimal temperature and humidity levels more effectively than conventional systems, achieving a 92% hatching success rate, which surpasses the 85% success rate of traditional incubators. Additionally, the integration of solar panels reduces dependency on conventional electricity and lowers operational costs by 30%, making it a more energy-efficient and sustainable solution. These findings highlight the potential of combining IoT automation and renewable energy to improve production efficiency, reduce costs, and support sustainable livestock management. The success of this system paves the way for further advancements in IoT and renewable energy applications in the agricultural sector, with potential scalability for both small-scale farmers and large-scale poultry industries, fostering digital transformation in more efficient and eco-friendly food production.
IoT-enabled digital twin with renewable energy for sustainable mudless eel aquaculture Muhammad Ferdiansyah; Lika Mariya; Taufik Rahman; Sugeng Dwiono
Indonesian Journal of Electrical Engineering and Computer Science Vol 41, No 3: March 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v41.i3.pp912-923

Abstract

This research develops and tests a digital twin (DT)-based smart aquaculture system for mud-free eel farming through the integration of IoT sensing, artificial intelligence (AI)-based prediction, edge computing, and solar energy-based automation. The approach used is experimental systems engineering, which includes system design, hardware and software implementation, virtual replication, and physical-digital two-way synchronization. The system utilizes ESP32-based pH, temperature, dissolved oxygen (DO), ammonia (NH₃), and turbidity sensors, MQTT communication, and Raspberry Pi edge computing. Water quality prediction is performed using long short-term memory (LSTM) and random forest regression. The dataset consists of 30 days of real-time data covering water quality, actuator activity (aerator, pump, feeder), and energy production and consumption by IoT sensors and energy meters. Results show that LSTM excels by R² = 0.94; RMSE = 0.14; MAPE <5% and synchronization latency <1.5 seconds. Solar energy integration reduces energy consumption by 54 67%, whilst automation increases eel survival rate by 78% to 91%. The novelty of this research lies in the first integrated implementation of DT, AIoT, and solar energy-based automation in mud-free eel farming. The proposed framework provides a precise, scalable, and sustainable solution for the development of modern aquaculture.
Co-Authors Muhammad Ferdiansyah Siti Kholifah K Sugeng Dwiono Taufik Rahman