Universitas Pembangunan Nasional Veteran Jakarta (UPN Veteran Jakarta) is one of the public universities which views student associations to play an important role in student self-development. Student’s self-development can be realized if students participate in every activity. but a lot of problems that occur because the process related to student association is still done manually without using an information system, where students have to come to campus to take care of all the needs to hold an activity. So that we need a system that aims to improve services to student associations as well as facilitate the management of existing student associations data and can increase the credibility of UPN Veteran Jakarta itself. It is called SIWA. It is expected to minimize errors that occur and manage business processes that exist in each student association. So that the benefits generated later, it is hoped that information on Real Estimate of Cost, submission of activity proposals, accountability reports and annual reports can be managed properly. Besides that, it can also support a paperless culture in the college environment. This information system will be built based on a web-based system and its development will use the waterfall method.
Air pollution has received much attention in recent years, especially in the most densely populated areas. Sources of air pollution include factory emissions, vehicle emissions, building sites, wildfires, wood-burning devices, and coal power plants. Common and dangerous air pollutants include nitrogen dioxide (NO2), ozone (O3), carbon dioxide (CO2), particulate matter 10 (PM 10) and particulate matter 2.5 (PM 2.5). This study focused on PM 2.5 because it has an aerodynamic diameter less than or equal to 2.5 μm. The small size of this pollutant makes it easily inhaled by humans and may end up deep in the lungs or even the bloodstream. Such pollutants can trigger health problems such as asthma, respiratory inflammation, reduced lung function and lung cancer. The purpose of this work was to forecast the next hour of PM 2.5 based on air pollution concentrations and meteorological conditions. The approach also uses station location data to cluster the area and to determine the neighboring areas of each station. Forecasting is based on the Long Short-Term Memory (LSTM). The result shows that the proposed approach can effectively forecast the next hour of PM 2.5 pollution.
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