Alvionita, Vinni (2024) Analisa Dan Prediksi Ketidakpastian Data Lingkungan Di Provinsi Riau Menggunakan Sensor Cuaca. Other thesis, Universitas Islam Riau.
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Abstract
This study focuses on the analysis and prediction of environmental data uncertainty in Pekanbaru using weather sensors. The main objectives are to provide a better understanding of the uncertainty in environmental data produced by weather sensors and to perform analysis using Jupyter Notebook to visualize weather data and present useful information. The methodology adopted is the k-Nearest Neighbors (KNN) algorithm, chosen for its capability in classification, regression, and anomaly detection tasks without requiring an explicit model. KNN operates by identifying the 'k' nearest training samples based on a specific distance metric and making predictions based on the majority class of its eighbors or the average output for regression cases. The results indicate that weather predictions offer a comprehensive and detailed overview of atmospheric conditions in Pekanbaru, benefiting the general public and sectors such as agriculture, transportation, and security. Model evaluation shows low Root Mean Squared Error (RMSE) for temperature and wind speed predictions, at 1.77 and 1.24 respectively, while the RMSE for humidity prediction is 5.62, indicating good performance in predicting temperature and wind speed and acceptable performance for humidity prediction. By using the KNN method for weather prediction analysis, this study provides a clear understanding of anticipated weather trends and serves as a crucial tool for developing adaptation and mitigation strategies in response to expected weather conditions.
| Item Type: | Thesis (Other) |
|---|---|
| Contributors: | Contribution Contributors NIDN/NIDK Sponsor Evizal, Evizal 1029027601 |
| Uncontrolled Keywords: | Environmental Data, Weather Sensors, k-Nearest Neighbors (KNN), Data Uncertainty, Weather Prediction, Jupyter Notebook |
| Subjects: | T Technology > T Technology (General) |
| Divisions: | > Teknik Informatika |
| Depositing User: | Yolla Afrina Afrina |
| Date Deposited: | 25 Sep 2025 01:15 |
| Last Modified: | 25 Sep 2025 01:15 |
| URI: | https://repository.uir.ac.id/id/eprint/30433 |
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