Particulate Matter Indoor Data Analysis Related to South Jakarta Temperature and Relative Humidity

Clean air for breath is essential for humans to live healthily. Exposure to air pollution may lead to diseases. Air pollution may affect how people behave when they exercise and may even cancel out some or all of the health benefits. Globally, particulate matter (PM), one of the primary air pollutants, is a significant contributor to non-communicable diseases. Humans can inhale suspended dust PM with a diameter of less than 10 $\mu \mathrm{m}$, which can then be placed in the lungs, especially the alveoli. There are types of PMs, and they are PM
1
, PM
2.5
, and PM
10
. This study aimed to find the correlations between PM
10
with temperature and relative humidity RH, especially in South Jakarta, Indonesia. Does each shift in temperature and humidity values have an impact on changes in PM
10
directly? This research contributes to enhancing algorithms for machine learning model development by adding factors that may result in changes in PM
10
values. This machine learning model will later be used during the Occupational Health and Safety Systems and Sports Information Systems development. This study used a quantitative process, primary data collected independently using sensors, and descriptive measurements of the gathered data. It has been found from the result analysis that both temperature and RH increments and decrement have not many relations with the increment of PM
10
, and neither did the decrements. It was shown that value movement for PM
10
and temperature was different for 64.46%, and PM10 with the RH was also not the same for about 63.83%.