{"id":"W4205150246","doi":"10.46880/jmika.vol5no2.pp142-146","title":"RANCANGAN PENELITIAN MODEL HYBRID DETEKSI COVID-19 MENGGUNAKAN MARINE PREDATORS ALGORITHM (MPA) DAN INTERPOLASI LINIER","year":2021,"lang":"en","type":"article","venue":"METHOMIKA Jurnal Manajemen Informatika dan Komputerisasi Akuntansi","topic":"Data Mining and Machine Learning Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Kootenay Association for Science & Technology","funders":"","keywords":"Outbreak; Coronavirus disease 2019 (COVID-19); Government (linguistics); Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Interpolation (computer graphics); Computer science; Algorithm; Process (computing); Test (biology); Geography; Simulation; Operations research; Mathematics; Artificial intelligence; Medicine; Virology; Ecology; Biology; Pathology","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":[0.0006598542,0.0007532373,0.0009789977,0.0004306523,0.0004551926,0.001403143,0.001101774,0.001034558,0.004583679],"category_scores_gemma":[0.001942761,0.0003662839,0.0008015556,0.0003363384,0.0003697403,0.0006967603,0.0007976278,0.001283349,0.0005732518],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000610971,"about_ca_system_score_gemma":0.001378495,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01565912,"about_ca_topic_score_gemma":0.008864064,"domain_scores_codex":[0.9997731,0.00005385064,0.00001645352,0.00006652014,0.00004873684,0.00004131708],"domain_scores_gemma":[0.9994116,0.000342902,0.00003456786,0.0000204734,0.0001641129,0.00002649263],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002730458,0.00009393042,0.00303588,0.0001820906,0.00009454803,0.0001786821,0.00008221497,0.9033151,0.002065134,0.005047149,0.002547106,0.0830852],"study_design_scores_gemma":[0.000009610958,0.00002873166,0.0001321148,0.00001024961,0.00001025545,0.00001944202,0.000008484011,0.9981906,0.0003612735,0.0005840322,0.0006410309,0.000004206881],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.06343547,0.002713678,0.919699,0.001130707,0.0004163079,0.0001223808,0.0002707175,0.001418078,0.01079376],"genre_scores_gemma":[0.8107857,0.001448871,0.1676662,0.0004158482,0.0001300053,0.0003224084,0.0008222841,0.000166104,0.0182427],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01565912,"threshold_uncertainty_score":0.03113598,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0188164560062636,"score_gpt":0.2902868972471394,"score_spread":0.2714704412408758,"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."}}