{"id":"W4388002338","doi":"10.52303/jb.v5i2.115","title":"Aplikasi Sistem Pendukung Keputusan Menggunakan Algoritma C5 Untuk Menentukan Penerima Bantuan Sosial","year":2023,"lang":"en","type":"article","venue":"Jurnal Ilmiah Binary STMIK Bina Nusantara Jaya Lubuklinggau","topic":"Data Mining and Machine Learning Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"WiLAN (Canada)","funders":"","keywords":"Poverty; Underdevelopment; Unemployment; Settlement (finance); Selection (genetic algorithm); Social assistance; Business; Operations management; Economic growth; Computer science; Engineering; Economics; Artificial intelligence; Finance","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":[0.001599206,0.0009385259,0.0009274987,0.001058833,0.001769392,0.001155474,0.003716004,0.0003609445,0.00009796594],"category_scores_gemma":[0.0001365177,0.0009361123,0.0004744492,0.003075335,0.0002638261,0.001780203,0.001677891,0.001395667,0.001467205],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003408832,"about_ca_system_score_gemma":0.0004743421,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005015929,"about_ca_topic_score_gemma":0.00004088775,"domain_scores_codex":[0.9931058,0.0004395497,0.001254076,0.002000043,0.001393607,0.001806903],"domain_scores_gemma":[0.995481,0.000441964,0.0006564983,0.002369549,0.0002478677,0.0008031425],"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":[0.0007586414,0.003534156,0.01350883,0.000790954,0.001549421,0.004837314,0.0141158,0.01581251,0.08250973,0.1338867,0.210675,0.518021],"study_design_scores_gemma":[0.00502109,0.002801457,0.06520167,0.0006207095,0.0003744992,0.001291453,0.001589321,0.4036491,0.00463621,0.004179946,0.5064356,0.004198979],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9131055,0.00128127,0.03606521,0.01263604,0.00629195,0.001683938,0.0002852662,0.006475396,0.02217543],"genre_scores_gemma":[0.9772224,0.00060957,0.0147962,0.0008000057,0.001162887,0.0002400979,0.0007786388,0.0001806608,0.00420952],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.513822,"threshold_uncertainty_score":0.9998814,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01896281308359952,"score_gpt":0.273473503354432,"score_spread":0.2545106902708325,"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."}}