{"id":"W4386686627","doi":"10.30591/jpit.v8i2.5216","title":"Pemanfaatan Algoritma K-Means untuk Membuktikan Implementasi Undang-Undang Pelanggaran Hukum Korupsi di Pengadilan Negeri Banjarmasin","year":2023,"lang":"en","type":"article","venue":"Jurnal Informatika Jurnal Pengembangan IT","topic":"Data Mining and Machine Learning Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Innovation Cluster (Canada)","funders":"","keywords":"Law enforcement; Language change; Enforcement; Silhouette; State (computer science); Law; Value (mathematics); Research method; Political science; Business; Mathematics; Computer science; Statistics; Algorithm; Artificial intelligence; Business administration","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","sts","scholarly_communication","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.002022956,0.0006926063,0.0006860392,0.0009765255,0.001601136,0.001689727,0.003350102,0.0001984263,0.000248107],"category_scores_gemma":[0.0002007934,0.0006581451,0.0003834489,0.002191582,0.0001715124,0.003323936,0.001211786,0.001521163,0.003606984],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000269653,"about_ca_system_score_gemma":0.0004001352,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002641684,"about_ca_topic_score_gemma":0.0002013242,"domain_scores_codex":[0.9946939,0.0002566245,0.001480165,0.0007349132,0.00131449,0.001519936],"domain_scores_gemma":[0.9961984,0.0003921269,0.0007658615,0.001632932,0.00025374,0.0007569715],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00009988113,0.000403489,0.005316101,0.0004560662,0.0006444384,0.0005408108,0.02666106,0.003332587,0.001342413,0.06821679,0.676924,0.2160623],"study_design_scores_gemma":[0.001690263,0.0004738024,0.02246116,0.0001890493,0.00009915778,0.0009192242,0.003567177,0.08867979,0.0004981834,0.0006532144,0.8795393,0.001229752],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.3302879,0.0008129969,0.3100716,0.06787845,0.007248676,0.004183174,0.001699066,0.01025803,0.2675602],"genre_scores_gemma":[0.9502941,0.0005487353,0.02319861,0.005702295,0.002108756,0.0002923551,0.002794609,0.0002752007,0.01478538],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6200062,"threshold_uncertainty_score":0.9996986,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02523906497801547,"score_gpt":0.298709758372329,"score_spread":0.2734706933943136,"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."}}