KLASIFIKASI MULTI-LABEL MENGGUNAKAN METODE MULTI-LABEL K-NEAREST NEIGHBOR (ML-KNN) PADA PENYAKIT KANKER SERVIKS
Based on GLOBOCAN 2020 statistical data, cervical cancer is the 8th most common cancer in women worldwide. Multi-Label K-Nearest Neighbor (ML-KNN) is one of the adaptive algorithms used to solve multi-label classification cases. The dataset used in this study was obtained from the UCI Machine Learni...
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2022-07-12.
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| 245 | 0 | 0 | |a KLASIFIKASI MULTI-LABEL MENGGUNAKAN METODE MULTI-LABEL K-NEAREST NEIGHBOR (ML-KNN) PADA PENYAKIT KANKER SERVIKS |
| 260 | |c 2022-07-12. | ||
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| 520 | |a Based on GLOBOCAN 2020 statistical data, cervical cancer is the 8th most common cancer in women worldwide. Multi-Label K-Nearest Neighbor (ML-KNN) is one of the adaptive algorithms used to solve multi-label classification cases. The dataset used in this study was obtained from the UCI Machine Learning website. The dataset will be preprocessed by eliminating missing values, checking for duplicate data, checking data types, and resampling data by oversampling the Biopsy label due to unbalanced data. After that the data is divided into training data and test data with a ratio of 80:20. The training data is searched for its proximity to the predetermined k value, namely K=1, K=3, K=5, K=7, and K=9. The evaluation results obtained the best performance for the ML-KNN classification, namely when the value of K = 5 which obtained a hamming loss value of 3.59%, accuracy of 93%, precision weighted of 93%, recall weighted of 96%, and f1-score weighted of 94%. | ||
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