{"id":"W4387881271","doi":"10.61306/jnastek.v3i4.107","title":"Pengelompokan Tamu Hotel Dengan Menggunakan Metode K-Means Clustering","year":2023,"lang":"en","type":"article","venue":"Jurnal Nasional Teknologi Komputer","topic":"Customer Service Quality and Loyalty","field":"Business, Management and Accounting","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Kootenay Association for Science & Technology","funders":"","keywords":"Cluster analysis; Service (business); Business; Marketing; Resource (disambiguation); Computer science; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001329996,0.001089141,0.001010153,0.002245704,0.001417777,0.003411803,0.0009838593,0.0008084487,0.006901439],"category_scores_gemma":[0.002596355,0.000543544,0.001252932,0.003203261,0.0006425887,0.002442735,0.00114031,0.0009423273,0.002340924],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001104982,"about_ca_system_score_gemma":0.002301145,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01023175,"about_ca_topic_score_gemma":0.00866757,"domain_scores_codex":[0.9987302,0.0002888863,0.0001070176,0.0004122686,0.0003358628,0.0001256776],"domain_scores_gemma":[0.9989687,0.0003695697,0.00008363339,0.000104126,0.0004158937,0.00005809919],"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":[0.0006716866,0.0002764486,0.01797501,0.00239384,0.0003524221,0.0004506327,0.002639739,0.1018953,0.01894359,0.02629976,0.01194803,0.8161535],"study_design_scores_gemma":[0.0001071,0.0005345726,0.04217707,0.0008031448,0.0003803971,0.001430316,0.006894306,0.7593207,0.03652975,0.04830739,0.1030025,0.0005126482],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1222069,0.004738445,0.8426499,0.000925848,0.0004472747,0.0004407224,0.001724372,0.001743075,0.02512352],"genre_scores_gemma":[0.5643587,0.004115698,0.41034,0.000151475,0.0001555401,0.0005847944,0.002461098,0.0003615704,0.0174712],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01023175,"threshold_uncertainty_score":0.02308762,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0448347600490642,"score_gpt":0.2692797708549018,"score_spread":0.2244450108058376,"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."}}