{"id":"W4391938375","doi":"10.59697/jtik.v6i2.299","title":"DATA MINING PENGELOMPOKAN INDUSTRI KECIL DAN MENENGAH BERDASARKAN HASIL PRODUKSI MENGGUNAKAN METODE CLUSTERING DI KABUPATEN LANGKAT","year":2022,"lang":"id","type":"article","venue":"JTIK (Jurnal Teknik Informatika Kaputama)","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; Mathematics; Statistics","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","research_integrity"],"consensus_categories":["open_science"],"category_scores_codex":[0.005165749,0.001178027,0.001208305,0.0009870417,0.00413817,0.002387462,0.01066022,0.0003416384,0.000304607],"category_scores_gemma":[0.0007083326,0.001253295,0.000288229,0.002493748,0.000271988,0.004985438,0.01608103,0.00409912,0.0004459718],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007702522,"about_ca_system_score_gemma":0.001293872,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000475849,"about_ca_topic_score_gemma":0.0001297426,"domain_scores_codex":[0.9902033,0.0009032154,0.002409591,0.001884946,0.00260992,0.001989083],"domain_scores_gemma":[0.9906562,0.000605136,0.001990336,0.005561018,0.0003013957,0.0008858911],"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":[0.0002623851,0.001234056,0.008677363,0.0007303911,0.001365614,0.0003005847,0.05744709,0.02035535,0.001135248,0.001745282,0.2524091,0.6543375],"study_design_scores_gemma":[0.00174823,0.0005833152,0.005376228,0.0002571898,0.000234808,0.0008340579,0.006466297,0.3376626,0.0002191346,0.00003311797,0.6450994,0.001485637],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6257701,0.005630861,0.1025473,0.04105095,0.01374907,0.008566604,0.01672679,0.006874305,0.179084],"genre_scores_gemma":[0.9387975,0.0002742971,0.03666686,0.002098149,0.001905293,0.0006967661,0.008262549,0.0003144109,0.01098422],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6528519,"threshold_uncertainty_score":0.9989917,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06936193266199289,"score_gpt":0.2984205822249268,"score_spread":0.229058649562934,"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."}}