{"id":"W3197894897","doi":"10.32938/jitu.v1i2.1472","title":"Penerapan Data Mining Korelasi Penjualan Spare Part Mobil Menggunakan Metode Algoritma Apriori (Studi Kasus: CV. Citra Kencana Mobil)","year":2021,"lang":"en","type":"article","venue":"Journal of Information and Technology","topic":"Data Mining and Machine Learning Applications","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Kootenay Association for Science & Technology","funders":"","keywords":"Spare part; Database transaction; Computer science; Database; Apriori algorithm; Association rule learning; Data mining; Operations research; Operations management; Engineering","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":[],"consensus_categories":[],"category_scores_codex":[0.0007377247,0.0001426408,0.0002776693,0.0004448935,0.0002389658,0.0002328221,0.001250634,0.0001320101,0.00001799374],"category_scores_gemma":[0.0004505131,0.0001271143,0.00003627362,0.0007928766,0.00008052473,0.00183352,0.0007137556,0.0004566775,0.00001863197],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003438267,"about_ca_system_score_gemma":0.0002620869,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001411007,"about_ca_topic_score_gemma":0.00001534874,"domain_scores_codex":[0.9985499,0.00004843874,0.0006900597,0.0002017897,0.0002772331,0.0002325978],"domain_scores_gemma":[0.9979438,0.00008515934,0.0006138469,0.0008827566,0.0003659356,0.000108564],"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":[0.000009912426,0.0001188607,0.003995636,0.00004848867,0.0001850001,0.00003852107,0.002409745,0.0002069687,0.0003901892,0.03443526,0.03585555,0.9223059],"study_design_scores_gemma":[0.00161555,0.0003925767,0.002229708,0.0001280838,0.00008067096,0.002221858,0.00579928,0.05432357,0.001362151,0.001892519,0.9295455,0.0004085354],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3315718,0.003457622,0.595001,0.05873748,0.0018354,0.0004993316,0.0002024121,0.000808057,0.007886906],"genre_scores_gemma":[0.719581,0.001180593,0.2774718,0.0009119582,0.0002180189,0.00002180304,0.000274368,0.00001573082,0.0003247546],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9218974,"threshold_uncertainty_score":0.5183569,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01884103927955952,"score_gpt":0.2671880761038298,"score_spread":0.2483470368242703,"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."}}