Open AccessA real-time data association of internet of things based for expert weather station system
Author(s) -
Indrabayu Indrabayu,
Intan Sari Areni,
Anugrayani Bustamin,
Rizka Irianty
Publication year - 2022
Publication title -
iaes international journal of artificial intelligence
Language(s) - English
Resource type - Journals
eISSN - 2252-8938
pISSN - 2089-4872
DOI - 10.11591/ijai.v11.i2.pp432-439
Subject(s) - meteorology , environmental science , wind speed , atmosphere (unit) , internet of things , automatic weather station , humidity , association rule learning , air temperature , computer science , association (psychology) , a priori and a posteriori , wind direction , weather station , data mining , world wide web , geography , philosophy , epistemology
The wind carries moisture into an atmosphere and hot or cold air into a climate, affecting weather patterns. Knowing where the wind is coming from gives essential insight into what kind of temperatures are to be expected. However, the wind is affected by spatial and temporal variabilities, thus making it difficult to predict. This study focuses on finding data associations from the weather station installed at Hasanuddin University Campus based on internet of things (IoT) using Raspberry Pi as a gateway that associated all the meteorological data from sensors. The generation of association rules compares the Apriori and FP-growth algorithms to determine relations among itemsets. The results show that high humidity and warm temperature tend to associate with a westerly wind and occur at night. In contrast, conditions with less humid and moderate temperatures tend to have southerly and southeasterly wind.