{"id":"W4413228311","doi":"10.51401/jinteks.v7i2.5886","title":"METODE MFCC-SVM UNTUK PENGENALAN TINGKAT EMOSI MANUSIA BERDASARKAN BERAGAM DATASET","year":2025,"lang":"id","type":"article","venue":"Jurnal Informatika Teknologi dan Sains (Jinteks)","topic":"Data Mining and Machine Learning Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Humanities; Physics; Art","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008976848,0.003168751,0.001755277,0.002142162,0.00127912,0.002309548,0.001388816,0.002326818,0.01890292],"category_scores_gemma":[0.003563274,0.0004695949,0.002107322,0.001513674,0.0002994539,0.001725767,0.001221074,0.001782779,0.02203187],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001181418,"about_ca_system_score_gemma":0.001250929,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02725436,"about_ca_topic_score_gemma":0.04601252,"domain_scores_codex":[0.9988505,0.0001468094,0.0001158134,0.0004097045,0.0002713042,0.0002058782],"domain_scores_gemma":[0.9992427,0.000193423,0.00003171622,0.0001294288,0.0003482471,0.00005448986],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.002006598,0.0006257936,0.01677719,0.00207809,0.0004004772,0.0009339378,0.0003087637,0.006846733,0.01646424,0.0009970752,0.534757,0.417804],"study_design_scores_gemma":[0.000381181,0.0009996888,0.08449493,0.001014313,0.0006469111,0.002080827,0.001639512,0.1949879,0.03948877,0.004094246,0.6697549,0.0004167378],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.2162311,0.02532244,0.06768075,0.00456393,0.007504682,0.001975521,0.5295438,0.07079291,0.07638491],"genre_scores_gemma":[0.1580981,0.003652851,0.05107911,0.001028043,0.0006760302,0.001135164,0.7332633,0.001483672,0.04958373],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02725436,"threshold_uncertainty_score":0.06323659,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01729348251920042,"score_gpt":0.2978298249908943,"score_spread":0.2805363424716939,"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."}}