# A real-time data association of internet of things based for expert weather station system
> Indrabayu

URL kanonis: https://discover.unhas.ac.id/publications/pub_scopus_85129050920
Jurnal / Konferensi: Iaes International Journal of Artificial Intelligence
Tahun terbit: 2022
DOI: https://doi.org/10.11591/ijai.v11.i2.pp432-439
ISSN: 20894872
Kuartil SJR: Q2
Citations: 5

## Authors
- Indrabayu

## Abstract
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.

## Keywords
- 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

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Sumber: Discover Unhas — RIMS Universitas Hasanuddin.
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