{"id":"W2779556253","doi":"10.26798/jiko.2016.v1i1.10","title":"PENERAPAN NAÃVE BAYES UNTUK PREDIKSI KELAYAKAN KREDIT","year":2016,"lang":"id","type":"article","venue":"JIKO (Jurnal Informatika dan Komputer)","topic":"Data Mining and Machine Learning Applications","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Kootenay Association for Science & Technology","funders":"","keywords":"Agricultural science; Environmental 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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","scholarly_communication","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001279899,0.0008564241,0.0007983258,0.0006289628,0.000962977,0.001826795,0.003946396,0.0003750723,0.0002190192],"category_scores_gemma":[0.0001958444,0.0006394074,0.0004137776,0.001028036,0.0003483626,0.005067439,0.001889356,0.0009600262,0.003822695],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002702944,"about_ca_system_score_gemma":0.0005621955,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001078968,"about_ca_topic_score_gemma":0.00001800225,"domain_scores_codex":[0.9943191,0.0002780618,0.001751877,0.0009967005,0.00120723,0.001446961],"domain_scores_gemma":[0.9945596,0.0005826251,0.001195687,0.002367519,0.0004180908,0.0008764407],"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":[0.00005684885,0.0003501138,0.003115156,0.0002007356,0.0003753469,0.00004649808,0.008586854,0.000303144,0.000786837,0.02987431,0.2668313,0.6894729],"study_design_scores_gemma":[0.002471144,0.001103072,0.01881391,0.001068795,0.0001165597,0.0006864434,0.0002205229,0.08319094,0.001153568,0.0005684135,0.8889939,0.001612788],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07064717,0.0005042814,0.877187,0.01447474,0.005529209,0.0009799735,0.0005409757,0.001461532,0.02867515],"genre_scores_gemma":[0.9133435,0.0007758603,0.06153701,0.004135757,0.004513,0.0001966792,0.000380854,0.0001681823,0.01494912],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8426964,"threshold_uncertainty_score":0.9996057,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01193546187938579,"score_gpt":0.2459626361589483,"score_spread":0.2340271742795625,"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."}}