{"id":"W4404619263","doi":"10.62383/polygon.v2i5.238","title":"Pengelompokan Data Kriminal untuk Menentukan Pola Rawan Tindak Kriminal Menggunakan Algoritma K-Means","year":2024,"lang":"en","type":"article","venue":"Polygon Jurnal Ilmu Komputer dan Ilmu Pengetahuan Alam","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":"Cluster analysis; Criminology; Socialization; Persecution; Computer security; Psychology; Business; Computer science; Political science; Social psychology; Law; Artificial intelligence; Politics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":{"n_in":0,"stratum":"aff_core","weight":5595.2375,"opus":{"tier":"OUT","genre":"empirical","about_ca":false,"confidence":"high","reason":"K-means clustering of crime data to identify high-risk areas; applied data mining."},"gpt":{"tier":"OUT","genre":"empirical","about_ca":false,"confidence":"high","reason":"This applies clustering to criminal data and does not study research itself."},"grok":{"tier":"OUT","genre":"empirical","about_ca":false,"confidence":"high","reason":"Applied K-means clustering of local crime data for police operations."}},"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009608287,0.001352681,0.00110471,0.001238029,0.0008038565,0.002651324,0.001098444,0.0007319263,0.009392165],"category_scores_gemma":[0.003274136,0.0005434849,0.0008681506,0.001760225,0.0007077357,0.002519899,0.0009737799,0.001410353,0.003783212],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005731887,"about_ca_system_score_gemma":0.001407361,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006153949,"about_ca_topic_score_gemma":0.005439139,"domain_scores_codex":[0.9992397,0.0001292981,0.00009918917,0.0002588745,0.0002092896,0.00006366774],"domain_scores_gemma":[0.9990293,0.0003977507,0.00004952552,0.0001015811,0.0003971111,0.00002484762],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0005112311,0.0001355051,0.001509792,0.001216418,0.0001574067,0.0002701538,0.0007336488,0.04988422,0.01827161,0.00822173,0.01056308,0.9085252],"study_design_scores_gemma":[0.0001352664,0.000509784,0.008039728,0.0005996412,0.0003092294,0.001288114,0.001956819,0.7635335,0.05195955,0.03646821,0.1349291,0.0002709757],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03286855,0.003614635,0.9477804,0.001042725,0.0006400797,0.0003256567,0.001212531,0.003680018,0.008835396],"genre_scores_gemma":[0.1615011,0.003457176,0.8169416,0.000238342,0.0001512058,0.0005823848,0.002561472,0.000863725,0.01370295],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009392165,"threshold_uncertainty_score":0,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03417709483029344,"score_gpt":0.3041832793749095,"score_spread":0.270006184544616,"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."}}