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Raden Sutiadi
Lembaga Ilmu Pengetahuan Indonesia
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Perancangan Purwarupa Pendeteksian Masker Menggunakan Mobilenetv2 dan Sensor Suhu GY-906 MLX-90614 Berbasis OpenCV Lukman Medriavin Silalahi; David Martin Antoyo; Setiyo Budiyanto; Imelda Uli Vistalina Simanjuntak; Gunawan Osman; Agus Dendi Rochendi; Raden Sutiadi
PETIR Vol 15 No 1 (2022): PETIR (Jurnal Pengkajian Dan Penerapan Teknik Informatika)
Publisher : Sekolah Tinggi Teknik - PLN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33322/petir.v15i1.1346

Abstract

The background of this research problem is the handling of the Covid-19 virus by using masks and regular body temperature measurements for each user entering/exiting a building. So this research aims to monitor people in carrying out the principle of using a mask and detecting a person's temperature with a limit not exceeding the normal temperature of 37.5 selsius. Outside of this research is the design of a face mask detection system and can measure a person's body temperature using Python programming that contains OpenCV, MobileNetV2 and gy-906 MLX 90614 non contact temperature sensors. the results of this research can be concluded that face mask detection can be done in the range of 50cm to 1.5m while face detection can be detected up to 3m when conducting real time testing and has a success rate of detecting face masks 86.6% to 93.3% from 15 times the experiments that have been conducted.
Alat Bantu Training Elektronika Berbasis Internet Of Things dengan Logika Fuzzy Menggunakan NODEMCU Lukman Medriavin Silalahi; Agus Dendi Rochendi; Irfan Kampono; Muhamad Husni; Raden Sutiadi
KILAT Vol 10 No 2 (2021): KILAT
Publisher : Sekolah Tinggi Teknik - PLN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33322/kilat.v10i2.1348

Abstract

Electronic training activities are still carried out conventionally, namely through a face-to-face system. The trainer has to check the trainees' measurement scores manually, this takes a long time. In addition, during the Covid-19 pandemic, human interaction is limited. Therefore it is necessary to make a tool that can help the training process. The proposed IoT-based electronic training tool uses nodeMCU, with a MySQL database system using IoT (Internet of Things) technology. Data communication media uses Wifi (IEEE 802.11n) with nodeMCU 8266 and INA219 sensors. The data is directly displayed through the internet browser media(PHP). From the results of our research, this tool can monitor the implementation of training for a distance of 8 meters and the sensor response time is less than 1 second. The sensor sensitivity level reaches 99.01% with a measurement accuracy of 0.01. With this tool, checking measurement results can be done quickly and can be done from anywhere.
Pemantau Gas Metana, Suhu, dan Kelembaban sebagai Penyebab Efek Rumah Kaca Dipadang Lamun Berbasis Internet Of Things Lukman Medriavin Silalahi; Irfan Kampono; Agus Dendi Rochendi; Muhamad Husni; Raden Sutiadi; Daniel Putra Pardamean Mbarep
KILAT Vol 10 No 2 (2021): KILAT
Publisher : Sekolah Tinggi Teknik - PLN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33322/kilat.v10i2.1349

Abstract

Seagrass is a plant that covers coastal areas/shallow seas that can produce methane gas (CH4) during the decomposition process. The occurrence of decay caused by microbes in seagrass plants that have died in the process produces methane gas (CH4) as the cause of the greenhouse effect. Methane gas monitoring system (CH4) is proposed using MQ-4 sensor, temperature and humidity sensor (DHT11) using NodeMCU ESP8266 module, SD Card module as backup data storage and processed with local database and through mysql database the data will be displayed on the website page for information. . From the tests carried out, the response time for DHT11 is 5.6 seconds and MQ-4 is 1.5 seconds. It has a reading sensitivity rate of 99.92% for DHT11, 99.997% for MQ-4. The accuracy rate for DHT11 is a multiple of 1. For the MQ-4 sensor it has an accuracy level of 2 digits behind the comma. The tool has a data transfer rate of up to that which appears on the front-end 0.2736. With this tool, checking measurement results can be done quickly and can be done from anywhere.
Co-Authors Agus Dendi Rochendi Daniel Putra Pardamean Mbarep David Martin Antoyo Gunawan Osman Imelda Uli Vistalina Simanjuntak Irfan Kampono Lukman Medriavin Silalahi Muhamad Husni Setiyo Budiyanto