{"id":"W2937852761","doi":"10.31937/si.v9i2.991","title":"Visualisasi Data Penjualan dan Produksi PT Nitto Alam Indonesia Periode 2014-2018","year":2019,"lang":"en","type":"article","venue":"Ultima InfoSys Jurnal Ilmu Sistem Informasi","topic":"Data Mining and Machine Learning Applications","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Dashboard; Production (economics); Visualization; Quarter (Canadian coin); Business; Data visualization; Computer science; Database; Marketing; Geography; Economics; Data mining","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001205098,0.0004073053,0.0002908683,0.003989522,0.0004770035,0.00251403,0.0004443357,0.0002439277,0.01411346],"category_scores_gemma":[0.003514762,0.0002752762,0.0003280156,0.006535731,0.0003472912,0.001837183,0.00103136,0.0006592966,0.004485831],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001122385,"about_ca_system_score_gemma":0.001327658,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01075092,"about_ca_topic_score_gemma":0.0129445,"domain_scores_codex":[0.9992597,0.00008476303,0.00007715863,0.000137016,0.0003672774,0.00007407003],"domain_scores_gemma":[0.9972383,0.0007019997,0.0004536874,0.0002842699,0.001084572,0.0002372791],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.001594334,0.0004026704,0.2159211,0.002054592,0.0001013159,0.001510843,0.01055405,0.004919355,0.01256602,0.005693614,0.1074773,0.6372048],"study_design_scores_gemma":[0.00004513988,0.0004079657,0.4699315,0.0005893972,0.00008550986,0.0009494801,0.01430419,0.008145437,0.02756204,0.00207805,0.4757522,0.0001491311],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.753129,0.001928137,0.009409126,0.002234766,0.0003449202,0.0003457527,0.08740141,0.005182536,0.1400243],"genre_scores_gemma":[0.8877817,0.002486339,0.01452339,0.0001304037,0.00007193177,0.0002826993,0.05265706,0.0005979261,0.04146849],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01411346,"threshold_uncertainty_score":0.04721421,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02271163559400273,"score_gpt":0.2855543445041292,"score_spread":0.2628427089101265,"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."}}