{"id":"W4401636466","doi":"10.51544/jurnalmi.v5i1.1197","title":"PENERAPAN DATA MINING KORELASI UMUR, PANGKAT DAN PENDIDIKAN TERHADAP JABATAN PADA POLRES BINJAI MENGGUNAKAN METODE ALGORITMA APRIORI","year":2020,"lang":"id","type":"article","venue":"JURNAL MAHAJANA INFORMASI","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":"Computer science; Mathematics","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":[0.00642311,0.00249321,0.002023618,0.004802559,0.001516589,0.00799621,0.002575198,0.001951778,0.006491814],"category_scores_gemma":[0.01580394,0.0009927597,0.00290105,0.005881925,0.0009515601,0.007363797,0.002942936,0.002875498,0.0059944],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00161358,"about_ca_system_score_gemma":0.003208584,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005204213,"about_ca_topic_score_gemma":0.005631675,"domain_scores_codex":[0.9940709,0.001354853,0.0008246073,0.001454141,0.00197349,0.0003218646],"domain_scores_gemma":[0.9896549,0.005245891,0.000850131,0.001244245,0.002733944,0.0002708177],"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":[0.0008490061,0.0005715812,0.03268701,0.003285595,0.0008393126,0.001095865,0.002019977,0.03062113,0.0208228,0.02537447,0.0199438,0.8618895],"study_design_scores_gemma":[0.0001954484,0.001326249,0.03200055,0.002178259,0.001213755,0.003986917,0.007607288,0.4843111,0.08460422,0.125754,0.2564082,0.0004139268],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08395122,0.01239361,0.8528891,0.006889364,0.0007145055,0.001037483,0.008102247,0.006450708,0.02757181],"genre_scores_gemma":[0.3592697,0.009164184,0.5912533,0.001319865,0.0005301135,0.000972273,0.01073007,0.0006747914,0.02608564],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00799621,"threshold_uncertainty_score":0.0339691,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05993675724750001,"score_gpt":0.2953231961000846,"score_spread":0.2353864388525846,"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."}}