SANJAYA, SOEN ANITA (2025) A COMPARATIVE ANALYSIS OF GAUSSIAN NAIVE BAYES, MULTINOMIAL NAIVE BAYES, AND K-NEAREST NEIGHBOR PERFORMANCE TO ANALYZE RAIN PREDICTION IN THE SEMARANG CITY AREA. S1 thesis, UNIVERSITAS KATOLIK SOEGIJAPRANATA.
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Abstract
Semarang, known for its heavy rainfall, faces a significant impact on the daily lives of its residents. Given the erratic rainfall patterns and frequent changes, accurate predictions have become crucial for disaster response and preparedness. While various algorithmic methods have been used, implementing Gaussian Naive Bayes, Multinomial Naive Bayes, and K-Nearest Neighbor (KNN) remains less than optimal, often being perceived as having lower accuracy than other algorithms. However, several studies indicate that Naive Bayes methods are widely employed and have even achieved up to 84.26% accuracy. This research aims to explore and compare the performance of these three algorithms in predicting rainfall in Semarang. By utilizing historical weather data from 2013 to 2024 obtained from dataonline.bmkg.go.id, this study evaluates the performance of each algorithm. Through data processing and analysis with Gaussian Naive Bayes, Multinomial Naive Bayes, and KNN, the expected outcome is to identify which algorithm provides the most precise and reliable rainfall predictions based on precision, accuracy, recall, and F1 score metrics.
| Item Type: | Thesis (S1) |
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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: | ms. Wiwien Vieragustin |
| Date Deposited: | 10 Jul 2025 08:00 |
| Last Modified: | 10 Jul 2025 08:00 |
| URI: | http://repository.unika.ac.id/id/eprint/37216 |
| Keywords: | UNSPECIFIED |
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