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Urban plant care is increasingly important for sustainable living, but many users face inconsistent watering, insufficient care knowledge, unsuitable plant selection and delayed disease recognition. This study presents Easy Grow Plants, an integrated web and Internet of Things (IoT) ecosystem that connects plant care guidance, soil-moisture monitoring, automated watering, plant recommendation, image-based plant health assistance, marketplace functions, community interaction and plant exchange. The prototype was implemented using a React frontend, Django REST backend, SQLite database and an Arduino UNO R4 WiFi smart pot with a soil moisture sensor, relay module and DC water pump. Functional, interface, API, IoT connectivity, sensor calibration, watering control and LAN deployment tests were conducted. The results show that the core modules operated together as an integrated academic prototype. The system demonstrates a practical foundation for smart urban gardening, although cloud deployment, multi-device testing and stronger AI validation remain future improvements

This study discusses the design and simulation of an automatic lighting system based on a photodiode sensor using an Arduino Uno microcontroller as the main controller. The system is designed to detect changes in ambient light intensity and automatically control the lamp without human intervention. The photodiode sensor is used as a light detector that converts light energy into an analog electrical signal, which is then processed by the Arduino to determine bright or dark conditions. The output from the Arduino subsequently controls a relay module that functions as an electronic switch to connect or disconnect the current to the light bulb. The research method employed is an experimental approach, which includes system planning, hardware design, software programming, simulation using Proteus, and system performance testing. The simulation results indicate that the system operates as expected, where the lamp automatically turns on when the light intensity decreases and turns off when the ambient brightness increases. This system is expected to improve electrical energy efficiency and provide ease of operation for lighting control, particularly in public facilitiesand workplace environments.

This research aims to design and develop a simple Dino-themed game based on a microcontroller, with the display using an I2C LCD. The game is inspired by the offline Dino game on Google Chrome, adapted to run on an Arduino Uno microcontroller. The research method used is Research and Development (R&D), consisting of stages such as needs analysis, system design, simulation using Wokwi, hardware assembly, programming, and testing. The system uses push buttons as input and a 16x2 I2C LCD as the output display. The testing results show that the system can respond quickly to user input and display character movement and score updates dynamically on the LCD screen. Although the graphical capability of the LCD is limited, the game runs well and successfully demonstrates the basic concepts of microcontroller programming as well as interactive input-output processing. Further development is recommended to enhance the graphical interface and add features such as sound effects and progressive difficulty levels.

Indramayu mango is a seasonal fruit that is highly favored due to its delicious taste and high nutritional content. However, high mango production is often not supported by adequate post-harvest facilities, particularly in terms of fruit ripeness classification. Currently, mango ripeness classification is still performed manually, which tends to be subjective and inconsistent. To address this issue, this study proposes a ripeness detection system for Indramayu mangoes by integrating the TGS2602 gas sensor and the YOLOv11 algorithm based on image processing. The TGS2602 sensor is used to detect ethylene gas emitted by ripe mangoes, while YOLOv11 is employed for visual image analysis of the fruit. This study aims to evaluate the system’s performance in classifying ripe and unripe mangoes, as well as analyze the integration between the gas sensor and the object detection model. The test results show that the TGS2602 sensor can detect increased ethylene gas concentration in ripe mangoes, while YOLOv11 demonstrates high accuracy in detecting mangoes based on visual images, with precision and recall close to 1.0. The system was also tested under various lighting conditions, including dark environments, and still performed well, although with a slight decrease in accuracy under low-light conditions.

Training on a Bluetooth-based lighting control system with Arduino Uno for students of SMA Xaverius 3 Palembang is one of the community service activities carried out by lecturers from the Palcomtech Institute of Technology and Business and lecturers from Lembah Dempo University. The purpose of this activity is to increase students' knowledge and abilities to utilize Arduino Uno for lighting control. The activity was carried out on Tuesday, November 11, 2025 in Room 3.2 of the Palcomtech Institute of Technology and Business, Palembang. The training was attended by 36 students. The implementation method was carried out by presentation and practice. The training activities were divided into three parts, namely the first presentation about Arduino Uno, the second practice of using Arduino Uno for control, the third exercise by students as an evaluation material to determine the level of understanding of participants. The results of this activity are to increase students' knowledge about Arduino Uno and its various benefits, one of which is for lighting control. This implementation went very well where the training participants actively participated in the activities from the beginning to the end and were able to carry out the exercise on using Arduino Uno for lighting control well and on time, which means that participants could understand the material presented well.

This study aims to design, implement, and test a prototype that automates three functions, namely watering, fertilizing, and pest control based on Arduino Uno with the ability to directly monitor soil moisture and pH. This system is equipped with four main types of sensors. Soil condition monitoring involves an FC-28 soil moisture sensor and a soil pH sensor, water level measurement involves an HC-SR04 ultrasonic sensor, and pest detection in the plant area involves a RIP sensor. All data obtained from these sensors is then processed by the Arduino Uno microcontroller to automatically activate actuators such as water pumps, liquid fertilizer pumps, buzzers, and DC motors according to soil conditions and plant needs. Prototype testing was conducted on simulated land with various scenarios of moisture, soil pH, and pest activity. The test results revealed that the system was proven to be able to significantly optimize water and fertilizer utilization, as well as reduce pest disturbances that could potentially damage plants.  In addition, this system also displays the operational status directly through an LCD screen, making it easy for users to monitor. The advantage of this system is its multi-function integration in a single device that is cost-effective and easy to operate. In the future, the functionality of this system can be improved through integration with Internet of Things (IoT) technology, enabling remote monitoring and control with greater efficiency. More broadly, this study is expected to support increased production and sustainable agricultural practices in Indonesia.