{"id":"W3016217016","doi":"10.30645/kesatria.v1i1.11","title":"Penerapan K-Means Dalam Mengelompokkan Nilai Tambah Industri Besar/Sedang Menurut Kabupaten/Kota","year":2020,"lang":"en","type":"article","venue":"KESATRIA Jurnal Penerapan Sistem Informasi (Komputer & Manajemen)","topic":"Data Mining and Machine Learning Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Government (linguistics); Quarter (Canadian coin); Centroid; Tertiary sector of the economy; Business; Service (business); Manufacturing; Geography; Marketing; Computer science","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001314373,0.001920479,0.001854887,0.001800443,0.001413067,0.0029083,0.001721641,0.001225282,0.01291441],"category_scores_gemma":[0.003346967,0.0006183827,0.001585253,0.002212126,0.0007622191,0.002193581,0.001031533,0.001478638,0.006274607],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008124812,"about_ca_system_score_gemma":0.002381818,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01156684,"about_ca_topic_score_gemma":0.01164865,"domain_scores_codex":[0.9985903,0.0002179337,0.0001200791,0.0006976447,0.0002516164,0.0001223598],"domain_scores_gemma":[0.9988486,0.0004654759,0.00007288201,0.00009526225,0.0004741951,0.00004369009],"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":[0.0006485138,0.000241756,0.003119883,0.0009993993,0.0003468372,0.0001928289,0.000530518,0.05270334,0.007481901,0.003781206,0.01842551,0.9115284],"study_design_scores_gemma":[0.0002911646,0.0008798097,0.01879894,0.0007688517,0.0004887088,0.000898296,0.002037347,0.8130586,0.02850637,0.01952328,0.1143072,0.0004414841],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0734129,0.009401906,0.8829904,0.001634351,0.001376729,0.0008454266,0.002805896,0.006706419,0.02082594],"genre_scores_gemma":[0.2765224,0.006543234,0.6607525,0.0007839138,0.0003765076,0.001539453,0.008531182,0.0009656016,0.04398516],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01291441,"threshold_uncertainty_score":0.04320306,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02825991049141221,"score_gpt":0.2370442916449788,"score_spread":0.2087843811535666,"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."}}