{"id":"W3197688988","doi":"10.32938/jitu.v1i2.1006","title":"Jaringan Saraf Tiruan Memprediksi Nilai Pemelajaran Siswa Dengan Metode Backpropagation ( Studi kasus : SMP Negeri 1 Salapian)","year":2021,"lang":"en","type":"article","venue":"Journal of Information and Technology","topic":"Computer Science and Engineering","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Kootenay Association for Science & Technology","funders":"","keywords":"Backpropagation; Artificial neural network; Value (mathematics); Gradient descent; Mean squared error; Mathematics; Approximation error; Sample (material); Process (computing); Statistics; Artificial intelligence; Computer science","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":[0.0005736923,0.0007369754,0.0004776487,0.0005528761,0.0008068325,0.002076388,0.0004894047,0.0008532375,0.01650497],"category_scores_gemma":[0.001521283,0.0002912118,0.0003757164,0.0007142567,0.0005421979,0.0022393,0.001010069,0.001379728,0.009524406],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005896622,"about_ca_system_score_gemma":0.0009585041,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00164097,"about_ca_topic_score_gemma":0.002127556,"domain_scores_codex":[0.9997042,0.00004843343,0.00002323365,0.00008293055,0.0001062556,0.00003487192],"domain_scores_gemma":[0.9996601,0.00009392046,0.00003351815,0.00002394701,0.0001651159,0.0000233688],"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":[0.0004150922,0.0002186998,0.003571742,0.00134207,0.0000841391,0.001390322,0.002056351,0.008645356,0.01620409,0.03682855,0.04237592,0.8868676],"study_design_scores_gemma":[0.00009943858,0.0006465963,0.0160619,0.001427172,0.0002196878,0.005691792,0.003680177,0.04807233,0.04646748,0.04162177,0.8357455,0.0002661495],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.2464592,0.06206321,0.2013788,0.02375179,0.00780331,0.0004919804,0.001090859,0.003458243,0.4535026],"genre_scores_gemma":[0.5603151,0.0430479,0.08907241,0.00222997,0.001083964,0.0002547314,0.0009303179,0.0004250026,0.3026406],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01650497,"threshold_uncertainty_score":0.05521464,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005033859593636048,"score_gpt":0.1969428689553825,"score_spread":0.1919090093617465,"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."}}