{"id":"W2990789842","doi":"10.30865/komik.v3i1.1628","title":"SISTEM PENDUKUNG KEPUTUSAN DALAM MENENTUKAN HAKIM TERBAIK PADA PENGADILAN AGAMA KELAS 1A MEDAN MENERAPKAN METODE ANALYTICAL HIERARCY PROCESS (AHP) DAN PROMETHEE II","year":2019,"lang":"en","type":"article","venue":"KOMIK (Konferensi Nasional Teknologi Informasi dan Komputer)","topic":"Legal Studies and Policies","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"WiLAN (Canada)","funders":"","keywords":"Analytic hierarchy process; Economic Justice; Law; Law enforcement; Ranking (information retrieval); Operations research; Business; Sociology; Computer science; Political science; Mathematics; Artificial intelligence","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"],"consensus_categories":[],"category_scores_codex":[0.001608003,0.00096998,0.001401695,0.0004875963,0.002566509,0.000725926,0.001840445,0.0006764711,0.0002105891],"category_scores_gemma":[0.000351476,0.0007812667,0.0005808377,0.001089351,0.0009801092,0.001670585,0.001259857,0.001162495,0.0001213423],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006376353,"about_ca_system_score_gemma":0.001034423,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005551004,"about_ca_topic_score_gemma":0.00142401,"domain_scores_codex":[0.9928929,0.0005426561,0.001386844,0.001113522,0.001966421,0.002097676],"domain_scores_gemma":[0.9964555,0.0005653145,0.0006500688,0.000897968,0.0006398358,0.0007913155],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0005691121,0.001207779,0.04345454,0.0004600494,0.00149371,0.0001750166,0.07998922,0.0004338693,0.001296558,0.8027646,0.01219307,0.05596247],"study_design_scores_gemma":[0.002581443,0.001575012,0.02368235,0.0002706612,0.0001887029,0.00008575696,0.01293288,0.003189125,0.00150842,0.001321286,0.9508362,0.001828169],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8835651,0.0003059142,0.00005487761,0.007601192,0.001649712,0.00149517,0.00003298719,0.0006333234,0.1046617],"genre_scores_gemma":[0.9856083,0.0003741575,0.001250599,0.001176263,0.001049218,0.0001006671,0.0001980885,0.00007870199,0.010164],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9386431,"threshold_uncertainty_score":0.9994639,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02819281284558881,"score_gpt":0.3027264692443437,"score_spread":0.2745336563987548,"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."}}