Eye detection technology is used to recognize and analyze unique features of a person's eyes as a way to identify or authenticate their identity. This technology can be used in various applications such as pattern recognition, biometric systems, surveillance systems, and others. Most applications require precision in eye detection, so a fast and reliable eye detection method is needed. In this research, an eye detection method is proposed using the Python OpenCV and MediaPipe libraries, which offer better accuracy compared to existing solutions. Both libraries are implemented in the Python programming language, which is popular among software developers for its ability in object-oriented programming, easy data manipulation and processing, and availability of libraries and modules in various fields such as artificial intelligence. The system was tested using videos captured using a smartphone. Although the videos were captured under suboptimal conditions, such as imperfect lighting, testing was conducted on 56 videos that had relatively good quality and lasted about 5-10 seconds. The results obtained showed an accuracy rate of 100%. Additionally, the system can distinguish between open and closed eye conditions, which will facilitate further research in detecting eye blinks. In conclusion, the model created can detect eyes with a very high accuracy rate.
To know the results of the face mask detection system, one must be near a computer. This problem makes it difficult to reprimand and provide face masks to violators. One of the ways to prevent the spread of the virus is to wear a mask. This study focuses on making a face mask detection system connected to a cellular device. This study aims to make obtaining information more effortless, and monitoring officers can find out from a smartphone. As a medium of communication, we use the telegram application. Smartphone users widely use this application compared to existing messaging media applications. This study uses the YoloV4 algorithm to detect face mask and JSON to send information to the telegram application. The test consists of two stages, the first stage is to determine the accuracy of the face mask detection system and the second stage is to determine the average time required until the information is sent. The two tests performed obtained 97.57% and 0.255 seconds, respectively. The test results show that the system created can solve the existing problems. The researcher can do further research by increasing the number of datasets to increase the accuracy of face mask detection.Keywords: Face Mask Detection, JSON, Telegram Application, YoloV4 Algorithm.
Perkembangan teknologi digital telah memberikan dampak besar pada berbagai sektor, termasuk pertanian. Salah satu teknologi yang semakin pesat perkembangannya adalah Internet of Things (IoT). Penelitian ini membahas tentang implementasi sistem perawatan otomatis tanamana indoor berbasis IoT, yang bertujuan untuk meningkatkan efisiensi dalam pengendalian dan pemantauan tanaman indoor. Sistem ini memanfaatkan beberapa sensor untuk mendeteksi suhu, kelembapan, dan cahaya, yang secara otomatis akan mengirimkan data ke server melalui internet. Penggunaan metode blackbox testing dan pengujian perangkat keras serta adanya beberapa alat seperti ESP8266, Sensor DS18B20, Soil moisture, Sensor BH1750, dan Fan, mini pump DC, dan LED Grow membantu memastikan kinerja sistem. Data sensor yang diperoleh akan diproses oleh mikrokontroler dan dikirimkan ke database server, yang kemudian dapat ditampilkan pada aplikasi mobile. Dalam pengujian, sistem ini berhasil menunjukkan akurasi dalam mengukur suhu, kelembapan, dan intensitas cahaya dengan rata-rata error masing-masing 1,5%, 3,2%, dan 2,54%. Sistem ini dapat diaplikasikan pada berbagai jenis tanaman indoor dan diharapkan dapat meningkatkan efisiensi dalam perawatan tanamana indoor secara otomatis. Dalam konteks pertanian, sistem ini memiliki potensi untuk meningkatkan produktivitas dan efisiensi secara signifikan. Dengan memanfaatkan teknologi IoT, para petani dapat memantau dan mengontrol lingkungan tumbuh tanaman mereka dengan lebih akurat dan efektif. Hal ini dapat membantu mereka mengambil keputusan yang lebih baik terkait pemeliharaan tanaman dan meningkatkan hasil panen. Dengan demikian, penggunaan teknologi IoT dalam pertanian dapat membantu meningkatkan kesejahteraan petani dan meningkatkan ketersediaan pangan bagi masyarakat.
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