{"id":"W4402380046","doi":"10.62951/bridge.v2i4.200","title":"Penerapan Algoritma Apriori Mengetahui Pola Tindakan Kriminal Berdasarkan Wilayah ( Studi Kasus : Polsek Sunggal)","year":2024,"lang":"en","type":"article","venue":"Bridge","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","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":[0.0006037344,0.0005651666,0.0003796101,0.0007765187,0.001088734,0.003006465,0.0004206458,0.0004039894,0.02312275],"category_scores_gemma":[0.0008254966,0.0002694987,0.0003082184,0.00105726,0.0005201299,0.001544141,0.0009789052,0.000810519,0.005282972],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006394802,"about_ca_system_score_gemma":0.001427392,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001751113,"about_ca_topic_score_gemma":0.002584197,"domain_scores_codex":[0.9996821,0.00008067732,0.00003451512,0.00007455884,0.00008564802,0.00004251789],"domain_scores_gemma":[0.9997904,0.0000643993,0.00002663692,0.00001691626,0.00008018843,0.00002158456],"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":[0.0005336931,0.0004524639,0.01182179,0.002043705,0.00008336904,0.001608036,0.00804818,0.001845167,0.01000468,0.04625224,0.02800081,0.8893059],"study_design_scores_gemma":[0.00007672511,0.0005770826,0.03937989,0.001027178,0.0001889165,0.002656989,0.01300653,0.003877423,0.01163647,0.01414308,0.913331,0.00009877607],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.4059282,0.04452547,0.04653024,0.006514606,0.002797617,0.0007912759,0.001691635,0.00110392,0.4901169],"genre_scores_gemma":[0.7094933,0.0301702,0.08025566,0.0004943893,0.0004308996,0.000452174,0.001633366,0.0004074333,0.1766626],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02312275,"threshold_uncertainty_score":0.07735336,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02093933713468355,"score_gpt":0.297167069792211,"score_spread":0.2762277326575274,"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."}}