{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004194823,0.0009141541,0.0006088116,0.0003948202,0.0005182324,0.001590206,0.0007309748,0.0009012965,0.01013471],"category_scores_gemma":[0.001142434,0.0003359852,0.0004249328,0.0004501945,0.0004029557,0.001721938,0.0006962452,0.001222656,0.003148437],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003850748,"about_ca_system_score_gemma":0.0007623971,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003493586,"about_ca_topic_score_gemma":0.004044666,"domain_scores_codex":[0.9998241,0.00002583709,0.00001568057,0.00005294975,0.00005420594,0.00002715221],"domain_scores_gemma":[0.9996924,0.0001168832,0.00002106268,0.00002106364,0.0001318444,0.00001674584],"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":[0.0004170646,0.0002480219,0.003805293,0.0009494787,0.0001434786,0.0006988913,0.0006134597,0.1038999,0.03010096,0.01376348,0.01504136,0.8303186],"study_design_scores_gemma":[0.00007998292,0.000275434,0.004828022,0.0003984733,0.0001646074,0.0009065677,0.0006760292,0.8644795,0.03282021,0.01791097,0.07732836,0.0001317573],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1661661,0.0100711,0.725458,0.003714103,0.001815928,0.000259205,0.0007726058,0.006074395,0.08566854],"genre_scores_gemma":[0.6961513,0.007951657,0.2154475,0.0007148736,0.0002658031,0.0003035625,0.000947687,0.0006944215,0.07752318],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01013471,"threshold_uncertainty_score":0.03390396,"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."}}