{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001779519,0.001281236,0.0007371114,0.001298524,0.0006618602,0.002154358,0.001843261,0.0008212266,0.01101939],"category_scores_gemma":[0.004434782,0.0006994725,0.001285915,0.001306972,0.0002510764,0.001876556,0.0008761319,0.001507705,0.00618147],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006910879,"about_ca_system_score_gemma":0.001704978,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007789158,"about_ca_topic_score_gemma":0.01019008,"domain_scores_codex":[0.9989158,0.0001262736,0.0001465361,0.0003329589,0.0003735226,0.000104821],"domain_scores_gemma":[0.9980974,0.0006296028,0.00009409857,0.0003254254,0.0007664381,0.00008705807],"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":[0.001051742,0.0005403406,0.01526966,0.00106592,0.0003288311,0.0006154344,0.0006914103,0.02906212,0.03743929,0.004556575,0.05095031,0.8584284],"study_design_scores_gemma":[0.0002652543,0.0005485541,0.01443117,0.0002627209,0.0003456037,0.0008817273,0.00103523,0.6192694,0.1690527,0.01217974,0.1815764,0.000151489],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08668791,0.000974176,0.8058547,0.001259584,0.0003733099,0.001103276,0.0115649,0.07367277,0.01850923],"genre_scores_gemma":[0.2036084,0.0006737639,0.7564197,0.0003281442,0.00004727575,0.0007219878,0.01551935,0.002174524,0.02050678],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01101939,"threshold_uncertainty_score":0.03686357,"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."}}