{"id":"W4213149274","doi":"10.31219/osf.io/2gwrb","title":"IMPLEMENTASI METODE K-MEANS CLUSTERING DALAM PENGELOMPOKAN PENYEBARAN COVID-19 DI SURABAYA","year":2022,"lang":"en","type":"preprint","venue":"","topic":"Data Mining and Machine Learning Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Innovation Cluster (Canada); WiLAN (Canada)","funders":"","keywords":"Silhouette; Cluster analysis; Coronavirus disease 2019 (COVID-19); Cluster (spacecraft); Corona (planetary geology); Geography; Index (typography); Computer science; Data mining; Cartography; Artificial intelligence; Physics; Medicine; World Wide Web","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005787447,0.0009318903,0.0008293461,0.0006403868,0.0009114671,0.001346701,0.0007947819,0.0007006584,0.00700049],"category_scores_gemma":[0.001338722,0.0003862911,0.0006225731,0.0007896794,0.0002760086,0.001096749,0.0006477705,0.0007559143,0.002731428],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007201706,"about_ca_system_score_gemma":0.001272781,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01781832,"about_ca_topic_score_gemma":0.01510374,"domain_scores_codex":[0.9995772,0.00007300303,0.00003009764,0.0001304707,0.0001324181,0.00005671818],"domain_scores_gemma":[0.9996023,0.00009704704,0.00001923478,0.00002653486,0.000235155,0.00001993017],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0007061687,0.0002223133,0.004912839,0.0005768503,0.0002347998,0.00024003,0.0007012562,0.09497751,0.02802375,0.005187008,0.009900173,0.8543173],"study_design_scores_gemma":[0.00008704296,0.0001723673,0.0049552,0.00007505219,0.00009907626,0.0003200378,0.0005959266,0.9361748,0.0307492,0.004902076,0.02176219,0.0001071019],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1021826,0.002066782,0.87476,0.0005991721,0.000443922,0.0002559822,0.0005341426,0.006129417,0.01302808],"genre_scores_gemma":[0.353834,0.001132801,0.6299632,0.000127042,0.00006440675,0.0003257499,0.001056815,0.0004445708,0.01305142],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01781832,"threshold_uncertainty_score":0.03542924,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05365274174165455,"score_gpt":0.360491461063218,"score_spread":0.3068387193215635,"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."}}