{"id":"W2992213878","doi":"10.30865/komik.v3i1.1677","title":"ANALISIS METODE K-MEANS PADA PENGELOMPOKAN PERGURUAN TINGGI MENURUT PROVINSI BERDASARKAN FASILITAS YANG DIMILIKI DESA","year":2019,"lang":"en","type":"article","venue":"KOMIK (Konferensi Nasional Teknologi Informasi dan Komputer)","topic":"Data Mining and Machine Learning Applications","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Innovation Cluster (Canada)","funders":"","keywords":"Workforce; Cluster (spacecraft); Government (linguistics); Agency (philosophy); Indonesian; Higher education; Quality (philosophy); Geography; Political science; Library science; Business; Sociology; Computer science; Social science; Physics","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":[0.001717279,0.000935049,0.0009198253,0.002216482,0.000904773,0.00233559,0.0006141991,0.0005747815,0.007204509],"category_scores_gemma":[0.00551424,0.0002553259,0.0008261021,0.003331576,0.0004279958,0.001373964,0.0006121808,0.0009792574,0.00203557],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007055703,"about_ca_system_score_gemma":0.001439336,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0102207,"about_ca_topic_score_gemma":0.009486219,"domain_scores_codex":[0.9986238,0.0003182257,0.0001254058,0.000348132,0.0004525242,0.0001320189],"domain_scores_gemma":[0.9973816,0.001527076,0.000159668,0.0001328019,0.0007395878,0.00005924813],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.001253693,0.0004062119,0.0966887,0.002086301,0.0008472964,0.0005922771,0.004205563,0.02940419,0.01443324,0.005351434,0.02137585,0.8233552],"study_design_scores_gemma":[0.0002202748,0.00137616,0.3914379,0.001159683,0.0009599675,0.001981666,0.03230414,0.347367,0.05126744,0.02001287,0.151269,0.0006438776],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6711927,0.004551691,0.2888848,0.001846252,0.000687368,0.0006251822,0.006996599,0.00250338,0.02271189],"genre_scores_gemma":[0.8529339,0.001285783,0.131162,0.0001167974,0.00006463641,0.0004367891,0.004209692,0.0003572579,0.009433057],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0102207,"threshold_uncertainty_score":0.02410144,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01302203122856045,"score_gpt":0.2415866640379005,"score_spread":0.2285646328093401,"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."}}