{"id":"W2494384153","doi":"10.55601/jsm.v17i1.269","title":"Perancangan Pengenalan Karakter Alfabet menggunakan Metode Hybrid Jaringan Syaraf Tiruan","year":2016,"lang":"id","type":"article","venue":"Jurnal SIFO Mikroskil","topic":"Computer Science and Engineering","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Tellabs (Canada)","funders":"","keywords":"Computer science; Backpropagation; Artificial intelligence; Humanities; Artificial neural network; Art","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":[0.0005310681,0.0008633425,0.0006071028,0.0007293664,0.0005587293,0.001897663,0.0007240995,0.0007572449,0.00853444],"category_scores_gemma":[0.001189503,0.0003696735,0.0005840058,0.0006231103,0.0004636881,0.001740519,0.0008609092,0.0008688184,0.002705869],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005385741,"about_ca_system_score_gemma":0.0008013177,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002781609,"about_ca_topic_score_gemma":0.00411444,"domain_scores_codex":[0.9996402,0.00004905424,0.00002513949,0.00008651625,0.0001523457,0.00004677686],"domain_scores_gemma":[0.9995404,0.0001125903,0.00003048204,0.00004816353,0.0002326959,0.00003562278],"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":[0.0004941364,0.0002100358,0.004604736,0.0008217558,0.0002092994,0.0006305237,0.0007048209,0.07171264,0.1606831,0.01770421,0.008133817,0.7340909],"study_design_scores_gemma":[0.00006738859,0.0005199219,0.01430731,0.000270423,0.0002867739,0.001254098,0.001511976,0.6880186,0.1556484,0.03445891,0.1034409,0.0002153504],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1360466,0.00341326,0.8009113,0.001330444,0.0004823419,0.0001784075,0.0003816458,0.003272543,0.0539836],"genre_scores_gemma":[0.7144484,0.002918764,0.2215835,0.0003352416,0.0001587278,0.0002093254,0.0005360166,0.0004836442,0.05932632],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00853444,"threshold_uncertainty_score":0.02855051,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01285428321980945,"score_gpt":0.2174391336991923,"score_spread":0.2045848504793828,"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."}}