Indonesia adalah negara agraris, tetapi disayang saat ini semakin sedikit generasi muda yang mau terjun di bidang pertanian. Berdasarkan data dari Kementerian Pertanian petani muda di Indonesia yang berusia 20-39 tahun hanya sekitar 2,7 juta orang yang artinya hanya sekitar 8 persen dari total 33,4 juta orang petani. Untuk mewujudkan cita-cita Indonesia Lumbung Pangan Dunia tahun 2045 sangat pentingnya mengajak generasi muda atau kaum milenial untuk terjun dan menekuni usaha pertanian. Hal tersebut secara mikro juga tampak di dalam minat para pemuda untuk terjun ke bidang pertanian di Dusun Ngentak, Bulakrejo, Kabupaten Sukoharjo. Para pemuda perlu didekati dengan fakta bahwa pertanian tidak identik dengan terbelakang, tetapi dapat pula menarik dan modern dengan mengimplementasikan teknologi kekinian. Sehingga di dalam program pengabdian ini akan dilakukan implementasi dan sosialisasi smart farming hidroponik berbasis Internet of Thing (IoT) di. Perlu dilakukan sosialisasi dan edukasi teknologi pertanian cerdas atau smart farming agar bisa peningkatan efisiensi produksi, kualitas dan kontinuitas produk-produk pertanian terutama hortikuktura. Selain itu penerapan smart farming juga akan menarik generasi milenial untuk tidak malu menggeluti bidang pertanian. Berbagai macam sistem hidroponik akan dikenalkan baik itu sistem sumbu (WICK) deep flow technique (DFT), Nutrient Film Technique (NFT), sistem rakit apung (Floating Hydroponics System FHS), dan lainnya. Masyarakat juga perlu diedukasi untuk mengetahui parameter-parameter penting yang mempengaruhi pertumbuhan tanaman hidroponik seperti nilai pH, nilai EC (electrical conductivity), suhu nutrisi, dan kadar oksigen. Pengabdian ini diharapkan dapat meningkatkan antusias masyarakat terutama generasi milenial untuk menerapkan smart farming hidroponik.

This project focuses on the issue of improving the quality of plants, especially lettuce. Lettuce grows best in a temperature range of 25°C to 28°C and a humidity range of 65% to 78%. In order to grow high quality lettuce, it is necessary to adjust the temperature and humidity. But when you don't have time to pay attention to these details for other important work, that becomes a problem. To solve this problem, we use the Internet of Things to monitor assets anytime, anywhere. We made it possible. Also, this monitoring system saves a lot of time. I use an instant messenger called Telegram. Telegram Messenger is an open source, free, cross-platform application. Use API Bot in Telegram to create intelligent bots that react to messages about surveillance. The DHT11 Temperature and Humidity Sensor is used to provide sensor temperature and humidity readings. The Arduino Integrated Development Environment (IDE) software is used to create the source code, and the ESP 12e acts as the brains of the technology we develop and can connect to the internet. The results of this temperature and humidity monitoring can be accessed by typing commands into the Telegram bot. Statistics and other visual data can also be monitored from websites developed by us.

The paper presents a method for adaptive cruise control based motor acceleration control. On the long-distance driving, using vehicles that have a risk for accidents. One of these accidents is collisions between vehicles in front of them which can cause multiple collisions. With the help of the ACC feature, it can reduce the occurrence of these accidents which are caused by the driver's fatigue and weather conditions on long-distance trips. By using the Fuzzy adaptive cruise control system, it is successful in adjusting the acceleration set on the fuzzy system, and with the help of the GUI it can make it easier for the operator to set the appropriate acceleration.

This paper presents an algorithm for an autonomous quadcopter to perform autonomous precision landing. This research focuses on designing the quadcopter so that it can land precisely on the landing pad using image processing algorithms. First, the captured image will be converted to grayscale, then the thresholding method is carried out and followed by a morphological process to eliminate noise and produce a clear image. The detected image will be displayed in a frame that will calculate the distance to the middle point. It will be used as Pulse Width Modulation (PWM) input to adjust the direction of motion of the quadcopter. so that it can land autonomously. The algorithm was tested in several color pads which are located in the grass, sand and cluttered ground. Testing is carried out to test the accuracy and precision of the designed algorithm. The results of the experiment show an accuracy rate of 94.76% and a precision level of 96.59% with an average landing time of 19 seconds and an average detection time is 8.55 milliseconds.

Computer vision-based fire detection systems overcome this limitation in that they do not identify flammability on a product-by-product basis. In this study, fire detection was carried out using the YCbCr, RGB, and HSV map approach. The offered system uses color segmentation as a component of fire detection analysis. These three colors space segments will then be extracted to determine the presence of fire in the image used. A rule which consists of five rules based on color space condition had been constructed for classification of a pixel classified as fire. If a pixel satisfies these five rules, the pixels belong to fire class.This paper consists of 6 steps, including image acquisition, image pre-processing, image segmentation, feature extraction, image classification, and GUI creation. GUI provides a visual interface that is intuitive and easy for the user to understand the proposed system. By using button and another visual elements, users can interact with the system efficiently. Based on the tests carried out, the proposed system can detect images of fire in dark and light conditions. Performance testing is done by collecting a set of fire images on the internet. Performance is judged based on how many errors are generated when detecting fire. Performance is categorized into five types, including very good, good, fair, poor, and very poor.