## 1 Introduction  
Newborn babies are referred to as neonates, and babies born before reaching 37 weeks of gestation are considered premature. Premature babies face higher health risks than those born at full term, requiring intensive care. Intensive care for these infants necessitates using a medical device known as a neonatal incubator. The incubator maintains a stable environmental temperature and humidity using an internal heater, assisted by a fan for air circulation. Operating the incubator requires significant knowledge, and medical personnel must be able to monitor the conditions inside the incubator at all times. Additionally, efficient energy management within the incubator is crucial, as excessive energy usage could lead to the heater inside increasing the temperature conditions, potentially negatively impacting infant care and raising energy consumption. Therefore, research was conducted to develop an efficient energy-management system and enable real-time monitoring and control of the incubator's conditions to enhance neonatal care. In this study, a combination of LSTM-CNN methods and the Internet of Things (IoT) concept was utilized to create an energy prediction model and a real-time operational system for monitoring and control. The Internet of Things (IoT) is a technological concept that enables hardware or objects, regardless of location, to communicate over the Internet without human intervention [1]. The IoT has been widely adopted in various aspects of life to enhance daily living, even within the industrial sector [2]. The IoT comprises hardware, networking, and software components [3, 4]. In this research, IoT technology was applied in healthcare, specifically in neonatal incubators. Multiple sensors, actuators, and network modules are installed in the incubator. All hardware sensors and actuator modules are connected to a microcontroller, which processes the data and sends them to an IoT broker using the message queuing telemetry transport (MQTT) communication protocol [5]. Several previous studies have explored using the Internet of Things (IoT) in baby incubators, incorporating various sensors. Furthermore, previous research has employed a hybrid fuzzy and PID approach in incubator control systems. Researchers have also researched incubator monitoring and apnea detection systems for premature babies using IoT and wearable devices [6; 7; 8; 9]. However, no research has effectively combined artificial intelligence (AI) with IoT within the context of baby incubators. Our study addresses this gap by developing a system capable of understanding energy consumption, monitoring temperature conditions through a web application, and comprehending temperature distribution within the incubator. Generally, healthcare professionals use neonatal incubators to maintain temperature and humidity conditions. Therefore, this research uses DHT11 sensors to monitor temperature and humidity and ACS712 devices to measure energy consumption [10; 11]. Additionally, this research conducted experiments to understand the temperature distribution inside the incubator. Multiple DHT11 sensors were installed at various corners within the incubator to collect temperature data from each point in more detail and visualize. Through visualization, temperature distribution patterns were observed, which can provide further insights into improving temperature management efficiency and energy usage. This, in turn, contributes to the optimization of neonatal care. The main hardware components utilized included a microcontroller, a single-board computer, a 1200-watt AC heater, and a 12 V DC motor with a propeller for the fan. The microcontroller was the ATMega256, integrated into the Arduino Mega 2560 board [12]. Arduino Mega 2560 has digital input/output (I/O) and analog I/O to connect with the DHT11 and ACS721 sensors. It also features three serial ports (Tx/Rx), with one of these serial ports used in the research to connect the ESP8266 WiFi module [13; 14].