Lots of damages, losses, and costs have been the major concern, why handling natural disasters of tornados is very important. Several attempts using different approaches have been carried out, but up to now the results are not yet satisfactory. More promising approaches through a kind of artificial intelligent forecaster have been started for a while, but the results are still not satisfactory either. The capability of mHGN as a pattern recognizer has opened up a new possibility of recognizing a pattern of tornado many hours earlier. Therefore, it can be used to forecast a tornado more efficiently. The results taken from a simulated circumstances of a multidimensional pattern recognition have shown, that the 91% of accuracy can be regarded as satisfactory. Though, several modifications related to the data representation within the mHGN architecture need to be implemented. The deployment of mHGN in several risky areas of tornados can then be expected as a tool for reducing those damages, losses, and costs.
Public campus has a mandate to saving of electrical energy. Electrical energy consumption is often wasteful in building. There is tendency wasteful by user. Electronic equipment is often still turn on at idle time. Only a few students want to turn off the equipment and shut down the computer. Saving of electrical energy is not only at idle time but it can be improved into operational hour. It is not depending on idle time or operational hours, but depends on human presence. Implementation of electrical energy saving has to be supported by frugal behavior and equipment technology. In this study, we name system of smart detection and control to electrical energy (Sisdece). This system is consist of hardware and software. Hardware applies passive infrared sensor (PIR) sensor, wireless sensor network (WSN), microcontroller ESP32, access point, relay. Software use C++, hypertext preprocessor (PHP), hypertext markup language (HTML) and android studio. Result of measurement has been done in a month during November 2020. Average of energy saved is 12.51 kWh and total of electrical energy is 105.86 kWh. Comparison of energy saved to electrical energy is 11.81%. This is a significant reduction to electrical bill. The result is expected as benchmark of electrical energy management in Politeknik Negeri Medan (POLMED).
The problem of environmental damage caused by the palm oil industry has become a global issue. This often becomes an obstacle to the development of the palm oil industry in Indonesia. In fact, millions of Indonesians work in the palm oil sector and are a source of non-oil and gas state revenue. For this reason, efforts to support a sustainable palm oil industry need to be carried out. One of them is the implementation of a smart system in the form of a carbon dioxide (CO2) level detection system. It is important to know these parameters because they are affected by the expansion of oil palm plantations. The measurement results can be accessed and displayed in real-time web-based using the SIM7600 communication module. It is hoped that this CO2 detector will be a solution to environmental problems and related parties can determine mitigation actions in the most extreme conditions.
Monitoring of environmental pollution level on campus is very important to do. According to UI Green Metric 2018 data, there are 719 campuses to participate in the developing of green campus and issues of sustainability. One of assessment is implementation of smart system to solve environmental problems in the campus area. To develop an environmental pollution monitoring tool based on IoT needs hardware and software. The hardware consists of sensors namely dust particle of PM-2.5 sensor type GPY21010AU0F, temperature and humidity sensor type SHT11, light intensity sensor type BH1750, CO2 sensor type MG-811, SIM 800, ADC module and microcontroller ESP32. Software is done using PHP and database. Parameters to be measured are density of dust particle, temperature, humidity, light intensity, CO2 level and loudness. Furthermore, information about environmental pollution data can be displayed and accessed in real-time via smartphones and computers. The sensors read the value every in 5 seconds. The results is in average value, density of dust particle is 1.39 µg/m3. Temperature is 36.58 0C. Humidity is 53.80 %RH. Light intensity is 10,613 lux. CO2 level is 15.50 ppm (parts per million). There is upper threshold value for temperature and humidity. Others, it is a normal condition and no pollution because campus is still in lockdown.
Laboratory is the most important instrument in vocational education. That is a place where student working in vocational education institution. Therefore, big attention for safety aspect becomes the priority. Students practiced under the risk. Thus, they needs safety and healthy in working environment, especially in laboratory. The identification of potential hazards and risks must be done in Laboratory. This study have assessed 5 of potential hazards and risks in Politeknik Negeri Medan laboratory by Likert Scale. This object was divided into 2 assessment, namely likelihood of hazards and severity of consequences. The data are collected based on questionnaire results that involving 100 students with random academic level. The result shows that the highest state is chemical hazards, which accounted for 73.2% in likelihood of hazards, meanwhile electrical hazards contributed of 85% in severity of consequences. These condition are classified as “high” state. The specific attention must be given to “high” state. The action plan table giving an information literacy to help us for determining mitigate action.
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