{"id":"W4387217162","doi":"10.59697/jik.v5i1.318","title":"IMPLEMENTASI DATA MINING PENGELOMPOKAN JUMLAH DATA PRODUKTIVITAS UBINAN TANAMAN PANGAN BERDASARKAN JENIS UBINAN DENGAN METODE CLUSTERING DIKAB LANGKAT (STUDI KASUS : BADAN PUSAT STATISTIK LANGKAT)","year":2021,"lang":"id","type":"article","venue":"Jurnal Informatika Kaputama (JIK)","topic":"Data Mining and Machine Learning Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Kootenay Association for Science & Technology","funders":"","keywords":"Horticulture; Mathematics; Biology","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","open_science"],"consensus_categories":["metaepi_narrow","open_science"],"category_scores_codex":[0.004011603,0.001393289,0.001531947,0.000624223,0.002461532,0.003897751,0.01051024,0.0003455896,0.0002306525],"category_scores_gemma":[0.001194596,0.001436055,0.0002291781,0.002030577,0.0003105155,0.007268995,0.01902335,0.002126277,0.0007328007],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004795505,"about_ca_system_score_gemma":0.001505808,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002526471,"about_ca_topic_score_gemma":0.007173639,"domain_scores_codex":[0.9893542,0.0008410806,0.002730936,0.002699108,0.002023714,0.002350938],"domain_scores_gemma":[0.9855778,0.0008924181,0.001852914,0.01012479,0.0005429018,0.001009245],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001280291,0.001001105,0.0114141,0.001429061,0.001813285,0.001021039,0.02813367,0.000450989,0.001047686,0.003183224,0.3945771,0.5558007],"study_design_scores_gemma":[0.002959558,0.0005466219,0.01684522,0.0009890093,0.0008596891,0.001755638,0.01194197,0.1946584,0.0006527723,0.00007693544,0.7662389,0.002475291],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.3992845,0.01096335,0.3731979,0.04285747,0.01224651,0.008592723,0.08045879,0.00479554,0.06760328],"genre_scores_gemma":[0.7369902,0.0008900883,0.185698,0.002974318,0.001749469,0.000199734,0.06386301,0.0003778831,0.007257279],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5533254,"threshold_uncertainty_score":0.9998817,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09280023390331626,"score_gpt":0.3454898411828032,"score_spread":0.2526896072794869,"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."}}