{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001666472,0.001360397,0.001426265,0.001937253,0.0009076392,0.003082725,0.001210358,0.0009084386,0.007899156],"category_scores_gemma":[0.00339123,0.0003882672,0.0008030311,0.003019298,0.000597003,0.002825454,0.0009054215,0.001563058,0.004066677],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006624843,"about_ca_system_score_gemma":0.001301541,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004734694,"about_ca_topic_score_gemma":0.004188886,"domain_scores_codex":[0.9985543,0.0003849767,0.000141804,0.0003703754,0.0004629395,0.00008565198],"domain_scores_gemma":[0.99875,0.000546173,0.00007733801,0.000128004,0.0004706007,0.00002776124],"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":[0.0001909754,0.00008902824,0.00103839,0.0005461654,0.000106045,0.0001112725,0.0002547849,0.0317838,0.008772839,0.004325822,0.006113456,0.9466675],"study_design_scores_gemma":[0.00009580193,0.0004591481,0.007267942,0.0003840796,0.0002590246,0.0009690151,0.001519345,0.8212095,0.05332771,0.03309769,0.08117984,0.0002309105],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.041623,0.006847037,0.9338384,0.001621615,0.0006807753,0.0001916329,0.001109726,0.003810101,0.01027762],"genre_scores_gemma":[0.2365236,0.006164624,0.7306651,0.0002694612,0.0002977625,0.000400634,0.002715051,0.000766568,0.02219722],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007899156,"threshold_uncertainty_score":0.02642536,"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."}}