{"id":"W3155993640","doi":"","title":"Implementasi SMOTE untuk mengatasi Imbalance Class pada Klasifikasi Car Evolution menggunakan K-NN","year":2021,"lang":"id","type":"article","venue":"","topic":"Data Mining and Machine Learning Applications","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"Kootenay Association for Science & Technology","funders":"","keywords":"Mathematics; Artificial intelligence; Computer science","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001057942,0.0006080431,0.0006020801,0.0001744075,0.001035308,0.0007853738,0.00175751,0.0002381319,0.0005134887],"category_scores_gemma":[0.0002668275,0.0006498252,0.0002451547,0.001550378,0.0001759516,0.0008414607,0.001447381,0.0009651214,0.001501405],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004543663,"about_ca_system_score_gemma":0.001004205,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001758876,"about_ca_topic_score_gemma":0.0008875385,"domain_scores_codex":[0.9944834,0.0006471921,0.000925823,0.001871685,0.0008609911,0.001210941],"domain_scores_gemma":[0.99551,0.000322161,0.0004183143,0.002815713,0.0004539546,0.0004797872],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0000445005,0.001394359,0.02448695,0.0005388556,0.0007995102,0.0002966474,0.004233322,0.0006221084,0.01825167,0.5155877,0.2824416,0.1513027],"study_design_scores_gemma":[0.001719899,0.0003043503,0.0209855,0.0001782618,0.0001969058,0.0002381363,0.00157339,0.05018298,0.006678239,0.001734346,0.914912,0.001295934],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04837226,0.00617403,0.8449901,0.02792944,0.004283086,0.001372071,0.001500487,0.001851368,0.06352714],"genre_scores_gemma":[0.9076305,0.0008013058,0.05328422,0.001992809,0.0005856767,0.0001321858,0.001815834,0.00009076631,0.03366673],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8592582,"threshold_uncertainty_score":0.9995953,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01560721734585094,"score_gpt":0.2792210424184654,"score_spread":0.2636138250726145,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}