In December 2019, there was a pandemic caused by a new type of coronavirus, namely SARS-CoV-2 (Severe Acute Respiratory Syndrome Corona Virus 2) spread almost throughout the world. The World Health Organization (WHO) named it COVID-19 (Coronavirus Disease). To minimize the spread of the COVID-19, the Indonesian government announced a policy for the social distancing of 1-2 meters and wearing a medical mask. In this study, a mask detection system was built using the Haar Cascade Classifier method by detecting the facial areas such as the nose and lips. The study aims to distinguish between using masks and on the contrary. It is expected that the mask detection system can be implemented to provide direct warnings to people who do not wear masks in public areas. The results using the Haar Cascade Classifier method show that the system designed is able to detect faces, noses, and lips at a light intensity of 80-140 lux. The face is detected at a distance of 30-120cm, while the nose is at a distance of 30-60cm, while the lips are at a distance of 30-70cm. The system designed can perform the detection process at a speed of 5 fps. The overall test results obtained a success rate of 88,89%.

Internet of things (IoT) merupakan topik yang banyak dikembangkan pada dekade terakhir. Pada saat ini, banyak pengembang teknologi membuat perangkat-perangkat pintar yang dapat mempermudah pekerjaan manusia. Sistem rumah pintar adalah salah satunya. Pada sistem rumah pintar, perangkatperangkat fisik dapat melakukan komunikasi melalui jaringan internet atau jaringan near cable lainnya untuk bertukar informasi atau melakukan perintah dari penghuni rumah. Agar bisa bertukar informasi maka perangkat fisik tersebut di integrasikan dengan sensor dan aktuator. Salah satu implementasi dari rumah pintar yaitu pengontrolan lampu yang dapat diaktifkan atau dinonaktifkan menggunakan perintah suara atau menggunakan gawai pengguna. Tujuan dari penelitian ini yaitu agar pengguna dapat mengontrol lampu rumah dengan menggunakan perintah suara dengan bantuan google assistant untuk mengenali kalimat yang di ucapkan oleh penghuni rumah. Metode yang digunakan dalam penelitian ini yaitu IoT. Metode komunikasi berbasis IoT memungkinkan terjadinya pertukaran data antar device. Hasil dari penelitian ini yaitu dapat dibangun sistem kontrol lampu menggunakan Blynk-Google assistant. Pada sistem tersebut telah di tambahkan fitur untuk memantau konsumsi daya listrik pengguna. Dari hasil pengujian yang dilakukan maka didapatkan hasil bahwa presentase keberhasilan dari sistem tersebut yaitu 96,667%. Keberhasilan dari sistem tersebut dipengaruhi oleh kekuatan sinyal internet dan ketepatan dalam pengucapan kata yang telah terprogram.

<p><em><span>Temperature is an object of research that is often studied. Research on temperature is within the scope of control and monitoring. The process of controlling and monitoring temperature is influenced by the selection of the right temperature sensor. The temperature sensors that are often used are the LM35 sensor and the DHT11 sensor. The LM35 sensor has advantages in terms of a simple design and easy to implement, while the DHT11 sensor has the advantage because in one sensor package there are two functions, namely to measure air temperature and humidity. In this study, temperature measurement accuracy was carried out to facilitate researchers in determining the right temperature sensor. The data monitoring method uses the internet of things (IoT). The results of the research show that the DHT11 temperature sensor is more accurate and more stable than the LM35 temperature sensor. The results of the sensor test at room temperature, the DHT11 sensor has an accuracy rate of 97.21% while the LM35 sensor has an accuracy rate of 96.86%. While the results of the sensor test in the server room, the DHT11 sensor has an accuracy rate of 95.26%, while the LM35 sensor has an accuracy rate of 90.32%.</span></em></p>

Water pipe leakage causes financial loss for the user such as PAM or PDAM. YF-S201B flowmeter sensor is a water flow sensor made of plastic with a rotor and hall effect sensor inside. The rotor will spin when the water flow through the sensor. The speed of rotor spin is proportional to the water flow. The hall effect based sensor can be used to detect water flow up to 30 liter/minute (1.800L/hour) thus, the sensor can be used to control the water flow in distribution system and water debit monitoring. The principle of prototype is the placement of sensor before the leakage and after leakage position to obtain different value of water debit. The system will send a short message automatically to the phone if water leakage is detected in the pipeline.

Today, many shooting venue that held shooting training used conventional methods to accumulate shooting scores. These conventional methods for shooting training will need more time and resources to do such a task. These paper describes an automatic shooting scoring system based on image processing for live shooting session. A camera is mounted in front of shooting target frame to capture every single shoot image. We use several image processing algorithms such as target ring detection, perspective transform, image subtraction, as well as morphological image processing. Contour detection method is used to perform a perspective transform, obtaining circle diameter and center circle position by using bounding box function by extracting detected contour and bullet hole position. Our experimental results, show that the accuracy of our method is 91%, based on the experiment by using a tiny circle sticker with a diameter of 7.62mm to simulate as a bullet hole image. We use 10 target sheets which there are 10 bullet hole images using circle sticker in each captured target sheet image.