{"id":"W3049554888","doi":"","title":"IMPLEMENTASI DATA MINING UNTUK MEMPREDIKSI POLA PEMBELIAN SEPEDA MOTOR PADA SHOWROOM CV. VIVA MAS MOTORS DENGAN METODE ALGORITMA C4.5","year":2018,"lang":"id","type":"article","venue":"","topic":"Data Mining and Machine Learning Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Kootenay Association for Science & Technology","funders":"","keywords":"Physics; Humanities; 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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","sts","scholarly_communication","open_science","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.002357086,0.001004036,0.000856403,0.0005171243,0.00178923,0.001692145,0.008848964,0.000338556,0.001341891],"category_scores_gemma":[0.0004899196,0.000996976,0.0002154864,0.001576608,0.0004677335,0.002262772,0.007152275,0.000922534,0.001916068],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002133848,"about_ca_system_score_gemma":0.0007074742,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006819079,"about_ca_topic_score_gemma":0.001421655,"domain_scores_codex":[0.9917369,0.0005867548,0.001362322,0.003168114,0.00127531,0.001870548],"domain_scores_gemma":[0.9898824,0.0006496813,0.0006483639,0.007470292,0.0003895705,0.0009596702],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00008951761,0.0009878873,0.01778292,0.0002135265,0.001170049,0.00009505387,0.009942967,0.00002039019,0.01942047,0.007071297,0.5038463,0.4393596],"study_design_scores_gemma":[0.001668764,0.00116392,0.009781638,0.0001892176,0.0004480434,0.000154565,0.003031105,0.2783844,0.004272751,0.000188703,0.6987906,0.001926244],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1650359,0.001119876,0.7709073,0.01473255,0.0085041,0.003153095,0.00845886,0.003198605,0.02488975],"genre_scores_gemma":[0.5159069,0.0001594478,0.4381288,0.002649956,0.003438589,0.0001460572,0.00340655,0.0002407386,0.03592292],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.4374333,"threshold_uncertainty_score":0.9997946,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0597598858227308,"score_gpt":0.3424612363255984,"score_spread":0.2827013505028677,"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."}}