@phdthesis{eprintuntirta51674,
           title = {ANALISIS KLASIFIKASI KUALITAS KABEL TEGANGAN MENENGAH ISOLASI XLPE DENGAN ALGORITMA K - NEAREST NEIGHBOR},
          school = {Fakultas Teknik Universitas Sultan Ageng Tirtayasa},
          author = {SAID RAHMAN AZIZI},
            year = {2025},
           month = {June},
        abstract = {Over time, the quality of the cable decreases. This study conducted a cable assessment test and the results were classified using the K-Nearest Neighbor algorithm to make it easier to determine quality. The cable conditions were divided into 5 classes and the evaluation was carried out by testing the model performance using accuracy, precision, recall, and F1-score. The test results show that increasing the amount of training data can improve classification performance. With the addition of 100 training data, the K-NN algorithm with K=2 achieved the highest accuracy of 92.86\%, precision 0.96, recall 0.9333, and F1-score 0.9378. These results indicate that K-NN can be used to classify underground cables, if supported by sufficient data and the selection of the right K parameters.},
             url = {https://eprints.untirta.ac.id/51674/}
}