{"id":"W3019826397","doi":"10.59697/jsik.v1i2.744","title":"DATA MINING UNTUK MENENTUKAN KORELASI (CONFIDENCE DAN SUPPORT) JURUSAN SISWA PADA TINGKAT SEKOLAH MENENGAH TERHADAP INDEKS PRESTASI KUMULATIF (IPK) DI PERGURUAN TINGGI SEBAGAI SOLUSI TEPAT PEMILIHAN PROGRAM STUDI DI PERGURUAN TINGGI","year":2017,"lang":"id","type":"article","venue":"Jurnal Sistem Informasi Kaputama (JSIK)","topic":"Data Mining and Machine Learning Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Kootenay Association for Science & Technology","funders":"","keywords":"Humanities; Physics; Philosophy","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","research_integrity"],"consensus_categories":["metaepi_narrow","open_science"],"category_scores_codex":[0.00635908,0.002594121,0.002475991,0.0007134131,0.009103415,0.01032501,0.0156182,0.001117089,0.00008597099],"category_scores_gemma":[0.002374034,0.002511459,0.0008209079,0.001172779,0.001258347,0.00800809,0.0144161,0.00406977,0.0002314402],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000786496,"about_ca_system_score_gemma":0.002185981,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008126248,"about_ca_topic_score_gemma":0.001230815,"domain_scores_codex":[0.9831495,0.0008579959,0.004325989,0.004045778,0.00362816,0.003992571],"domain_scores_gemma":[0.9789745,0.001134126,0.006046225,0.01076435,0.001055018,0.002025768],"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":[0.0002698485,0.0018907,0.2381078,0.001429222,0.001594504,0.0004482023,0.02669543,0.00155487,0.0005877125,0.002655051,0.00773936,0.7170272],"study_design_scores_gemma":[0.003892025,0.002395994,0.2105693,0.002652062,0.001154421,0.0008782772,0.005699171,0.2880641,0.0004712559,0.00004985437,0.4799492,0.004224344],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8854296,0.001216004,0.008313227,0.005479975,0.005883856,0.006397477,0.0009799192,0.003423237,0.08287667],"genre_scores_gemma":[0.9680048,0.0003409203,0.01499115,0.0005581831,0.001385979,0.0003692954,0.002598148,0.0003388667,0.01141269],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7128029,"threshold_uncertainty_score":0.9986794,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0600760371404558,"score_gpt":0.3364232073136976,"score_spread":0.2763471701732418,"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."}}