{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00238463,0.001310303,0.001289592,0.002271884,0.0005098913,0.002446206,0.0009488034,0.000614805,0.004657536],"category_scores_gemma":[0.002401842,0.0005366228,0.000989125,0.002257761,0.0003419713,0.001616119,0.0008408421,0.001170628,0.002146902],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005645466,"about_ca_system_score_gemma":0.00120151,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003064643,"about_ca_topic_score_gemma":0.002358362,"domain_scores_codex":[0.9988829,0.0001997275,0.0001212556,0.0003383956,0.0003897635,0.00006792029],"domain_scores_gemma":[0.9987825,0.0005234078,0.00008323955,0.00007700457,0.0004950368,0.00003887914],"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":[0.0007140306,0.0002380479,0.004118817,0.0008997203,0.0002320558,0.0004712842,0.0003713297,0.02325924,0.02235896,0.003917213,0.005603325,0.937816],"study_design_scores_gemma":[0.0001985468,0.0009541157,0.01655797,0.0004736057,0.000563496,0.002260277,0.0009379719,0.768114,0.1122788,0.01633529,0.08112774,0.0001981],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1479746,0.01348677,0.8100057,0.001275311,0.0003139879,0.0004702068,0.002563577,0.00902201,0.01488792],"genre_scores_gemma":[0.3036262,0.006935119,0.6639188,0.0002137357,0.0001139225,0.0004919175,0.005265289,0.000739248,0.01869588],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004657536,"threshold_uncertainty_score":0.01558101,"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."}}