SUGIARTO, ONG AXEL BELAMY (2019) IMPLEMENTATION OF K-MEANS CLUSTERING FOR WEATHER CLASSIFICATION AND PREDICTION. Other thesis, UNIKA SOEGIJAPRANATA SEMARANG.
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Abstract
The weather classification process requires many components of weather data and large amounts of data. Extremely changes in the weather conditions can cause a lack of accuracy and speed in forecasts during the classification process.. To solve this problem, clustering model research has been carried out using several data mining techniques, namely K-Means. The dataset are weather conditions which can be accessed every 2 days in 2018. The main dataset attributes are temperature, humidity, air pressure, and wind speed. For other attributes adjusted to the presented data. From the results testing, it can be summarized that the data that has been grouped in each cluster has a weather condition results that match with the original data. Weather components that allow significant changes are temperature, dew point, humidity, wind speed, and pressure. Keyword: Data Mining, K-Means, classification, weather
| Item Type: | Thesis (Other) |
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| Subjects: | 000 Computer Science, Information and General Works > 004 Data processing & computer science |
| Divisions: | Faculty of Computer Science > Department of Informatics Engineering |
| Depositing User: | Mr Lucius Oentoeng |
| Date Deposited: | 10 Jul 2019 08:02 |
| Last Modified: | 11 Nov 2020 02:03 |
| URI: | http://repository.unika.ac.id/id/eprint/19649 |
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