{"id":"W3019608078","doi":"10.59697/jsik.v3i2.768","title":"PENGKLASTERAN DOKUMEN DENGAN MENGGUNAKAN ALGORITMA SUPPORT VECTOR CLUSTERING","year":2019,"lang":"en","type":"article","venue":"Jurnal Sistem Informasi Kaputama (JSIK)","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; 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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0008129606,0.0003799219,0.0004170014,0.0002422074,0.0003966235,0.0008140336,0.001869407,0.0001479812,0.0001061258],"category_scores_gemma":[0.00004923605,0.0003547437,0.0001961645,0.0005186463,0.00004457967,0.001886238,0.0008090175,0.0006636354,0.001364182],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001432973,"about_ca_system_score_gemma":0.0001931288,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002704451,"about_ca_topic_score_gemma":0.00001587213,"domain_scores_codex":[0.9971653,0.00008263326,0.0007761382,0.0005672494,0.0006606511,0.0007480149],"domain_scores_gemma":[0.9977129,0.0001563136,0.0003908674,0.001237266,0.0001364268,0.0003662886],"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":[0.0001176859,0.0004358405,0.03259758,0.0006276089,0.0004482707,0.0001171603,0.01707867,0.006096631,0.004576987,0.06950278,0.008937227,0.8594636],"study_design_scores_gemma":[0.001760084,0.0006050684,0.03180567,0.0001286621,0.00003640017,0.0005703445,0.0003378985,0.1290905,0.0007881431,0.00008902549,0.8338467,0.0009414838],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.4082572,0.0001610701,0.3047787,0.004177136,0.004708958,0.002077371,0.0000585587,0.002670253,0.2731108],"genre_scores_gemma":[0.9715379,0.00001299995,0.01708531,0.0008342986,0.0002570433,0.00006539876,0.00009666311,0.00004130912,0.01006911],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8585221,"threshold_uncertainty_score":0.9998904,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007554466004666319,"score_gpt":0.2339956613068401,"score_spread":0.2264411953021738,"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."}}