{"id":"W4399542840","doi":"10.62828/jpb.v3i2.104","title":"8. SEGMENTASI TINGGI BADAN DAN BERAT BADAN KADET MAHASISWA MENGGUNAKAN K-MEANS CLUSTERING","year":2024,"lang":"id","type":"article","venue":"TNI Angkatan Udara","topic":"Data Mining and Machine Learning Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Innovation Cluster (Canada)","funders":"","keywords":"Humanities; Physics; Philosophy","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":[0.00104874,0.001265555,0.000913824,0.002474808,0.002400231,0.003895553,0.001166423,0.001631519,0.00755757],"category_scores_gemma":[0.002069863,0.0005438376,0.001152638,0.002508729,0.0006556757,0.00157134,0.00114542,0.0009257354,0.005843533],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001751053,"about_ca_system_score_gemma":0.002736707,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02673496,"about_ca_topic_score_gemma":0.02873638,"domain_scores_codex":[0.9987202,0.0001311389,0.0001099175,0.0003272223,0.0005160744,0.0001955002],"domain_scores_gemma":[0.9984376,0.0002094291,0.0001127685,0.0001328723,0.001031352,0.00007609683],"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":[0.0008605985,0.0002883389,0.02646676,0.001230916,0.0002536211,0.0005990479,0.004012837,0.0431142,0.06819193,0.01296996,0.032215,0.8097968],"study_design_scores_gemma":[0.0001233738,0.0006254204,0.1362785,0.00062264,0.0006551519,0.001462869,0.01045657,0.4048064,0.1763319,0.03422307,0.2337423,0.0006717124],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2329099,0.002774292,0.6904552,0.002365571,0.000604077,0.001200526,0.005496431,0.009221528,0.05497249],"genre_scores_gemma":[0.4568713,0.001998048,0.4663616,0.0003033969,0.0001195782,0.0005939426,0.00589742,0.0008303765,0.06702437],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02673496,"threshold_uncertainty_score":0.05315864,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01682313875548924,"score_gpt":0.2804938767749291,"score_spread":0.2636707380194399,"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."}}