{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001295009,0.00105782,0.001173474,0.0006704355,0.0009711824,0.00166659,0.001086666,0.001083795,0.005011064],"category_scores_gemma":[0.003315226,0.0004457112,0.001141104,0.0008294568,0.0004443348,0.00127607,0.0006343322,0.001461351,0.001673984],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007451479,"about_ca_system_score_gemma":0.002097792,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0172325,"about_ca_topic_score_gemma":0.01681664,"domain_scores_codex":[0.9992762,0.0002142323,0.00006712892,0.0002106169,0.0001488534,0.00008288944],"domain_scores_gemma":[0.9991422,0.0004151014,0.00005907319,0.00006788313,0.0002886083,0.00002715155],"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":[0.0004026541,0.0002585997,0.00432648,0.0004402617,0.0002988691,0.000138184,0.0006507311,0.2133791,0.00739961,0.004699147,0.006127856,0.7618786],"study_design_scores_gemma":[0.0000498984,0.0001216044,0.002392436,0.00007523297,0.00007561038,0.00008847389,0.0003975118,0.9792357,0.005961456,0.004255017,0.007299692,0.00004734822],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07801752,0.002359998,0.9059686,0.001058264,0.000489485,0.0002990082,0.000261397,0.002804767,0.008741015],"genre_scores_gemma":[0.3607022,0.001641654,0.6238838,0.0003863262,0.0001350491,0.0006071189,0.0007306155,0.0003544848,0.01155866],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0172325,"threshold_uncertainty_score":0.03426439,"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."}}