Optimasi Klasifikasi Kelayakan Perizinan Frekuensi Radio Menggunakan Varian Naïve Bayes
DOI:
https://doi.org/10.47065/bulletincsr.v6i5.1297Keywords:
Complement Naïve Bayes; Gaussian Naïve Bayes; Naïve Bayes; Radio Station License; Spectrum Licensing ClassificationAbstract
Efficient management of the radio frequency spectrum requires a fast and accurate license evaluation system to avoid signal interference and ensure regulatory compliance. High submission volumes often cause processing bottlenecks in public licensing services. This study aims to optimize the classification of Radio Station License (ISR) applications into Granted and Rejected classes using variants of the Naïve Bayes algorithm. A real-world dataset comprising 38,954 application records with 26 technical, administrative, and geographic features was used. Data preprocessing involved removing post-decision leakage features, median imputation, label encoding, and feature scaling/transformation. Three algorithm variants were evaluated: Standard Gaussian Naïve Bayes, Gaussian Naïve Bayes with PowerTransformer, and Complement Naïve Bayes. Empirical results show that Gaussian Naïve Bayes with PowerTransformer achieved the highest overall accuracy of 78.32% and a high precision of 88.63% (5,112 True Negatives and 990 True Positives). Conversely, Complement Naïve Bayes effectively handled class imbalance by achieving a significantly higher recall of 73.71% and an F1-Score of 64.21%. In conclusion, transforming non-Gaussian technical features improves precision, while Complement Naïve Bayes optimizes candidate identification for approval. These findings demonstrate that optimized Naïve Bayes variants provide an effective and transparent model for automated decision support systems in spectrum licensing.
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