{"id":"W4384573886","doi":"10.59697/jsik.v6i2.169","title":"PENERAPAN DATA MINING PENGELOMPOKAN DATA PASIEN BERDASRKAN JENIS PENYAKIT MENGGUNAKAN METODE CLUSTERING (STUDI KASUS KLINIK MITRA ND)","year":2022,"lang":"id","type":"article","venue":"Jurnal Sistem Informasi Kaputama (JSIK)","topic":"Data Mining and Machine Learning Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Kootenay Association for Science & Technology","funders":"","keywords":"Humanities; Art","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":[0.00537105,0.001537274,0.001371386,0.003250067,0.001397414,0.005413959,0.001694966,0.00151974,0.02280852],"category_scores_gemma":[0.01332573,0.0009519876,0.001871523,0.004493789,0.001008505,0.004372245,0.002736826,0.002373494,0.01570691],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002315541,"about_ca_system_score_gemma":0.003720155,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008901821,"about_ca_topic_score_gemma":0.01184373,"domain_scores_codex":[0.9954465,0.001131996,0.0004402758,0.001091718,0.001651175,0.0002383235],"domain_scores_gemma":[0.9902817,0.004535699,0.0005658132,0.001241147,0.002918659,0.0004569712],"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":[0.0007278458,0.0003652858,0.01336821,0.00151822,0.0003099833,0.0004246013,0.001703887,0.01844926,0.02099909,0.01546434,0.07318036,0.8534889],"study_design_scores_gemma":[0.0002940833,0.0009106172,0.03791219,0.001334564,0.000553689,0.002309422,0.005125616,0.252174,0.1074951,0.06808481,0.5233469,0.000458956],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0579113,0.005522418,0.8359695,0.01014427,0.001210488,0.001274758,0.009072523,0.01615564,0.06273908],"genre_scores_gemma":[0.1868538,0.005909905,0.7147135,0.001980085,0.0004887832,0.001364303,0.01056871,0.002898903,0.07522209],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02280852,"threshold_uncertainty_score":0.07630211,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08241776119678437,"score_gpt":0.3159980042057383,"score_spread":0.2335802430089539,"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."}}