{"id":"W3129040632","doi":"","title":"JARINGAN SARAF TIRUAN UNTUK MEMPREDIKSI JUMLAH PENGANGGURAN DI KOTA BINJAI DENGAN MENGGUNAKAN METODE BACKPROPAGATION","year":2021,"lang":"id","type":"article","venue":"","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":"Government (linguistics); Backpropagation; Unemployment; Mathematics; Workforce; Unemployment rate; Statistics; Artificial neural network; Artificial intelligence; Computer science; Economics; Economic growth","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","sts","scholarly_communication","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00148934,0.0007783576,0.0007811646,0.0003344512,0.001352556,0.001691623,0.002340318,0.0003642867,0.0005206504],"category_scores_gemma":[0.0007444893,0.0008022611,0.0003224406,0.002129283,0.0002274232,0.001209261,0.001593623,0.001429887,0.002629781],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002171528,"about_ca_system_score_gemma":0.000807442,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008941992,"about_ca_topic_score_gemma":0.0005399371,"domain_scores_codex":[0.9936109,0.0008772913,0.001068287,0.002209628,0.001052738,0.00118111],"domain_scores_gemma":[0.9944037,0.0005421391,0.0004789456,0.003273395,0.0006348095,0.0006669904],"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":[0.0001109215,0.004172475,0.02206752,0.001020942,0.001861036,0.0006479659,0.03007111,0.002892876,0.1676386,0.2295666,0.12251,0.4174399],"study_design_scores_gemma":[0.004187455,0.0008855662,0.06581929,0.0006631719,0.0008979216,0.0006301139,0.00406024,0.3221557,0.1401544,0.002366205,0.4537222,0.00445772],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3586817,0.002398234,0.4650447,0.03323028,0.005328068,0.001975996,0.0003389505,0.003607956,0.1293941],"genre_scores_gemma":[0.9217429,0.0002170384,0.02739777,0.001055174,0.0007236825,0.00009926111,0.0008187424,0.0001206089,0.04782486],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5630612,"threshold_uncertainty_score":0.9999475,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02070062486407227,"score_gpt":0.2706158724232618,"score_spread":0.2499152475591896,"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."}}