{"id":"W4387216697","doi":"10.59697/jik.v4i1.351","title":"PEMANFAATAN DUA METODE CLUSTERING DAN ASSOCIATION RULE TERHADAP PRESTASI BELAJAR BERDASARKAN NILAI MATA PELAJARAN SISWA","year":2020,"lang":"en","type":"article","venue":"Jurnal Informatika Kaputama (JIK)","topic":"Data Mining and Machine Learning Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Kootenay Association for Science & Technology","funders":"","keywords":"Centroid; Cluster analysis; Association rule learning; Apriori algorithm; Computer science; Student achievement; Process (computing); k-means clustering; A priori and a posteriori; Educational data mining; Mathematics education; Data mining; Artificial intelligence; Psychology; Academic achievement","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":[0.001391304,0.001188329,0.001329763,0.002082322,0.001568815,0.003122964,0.0009015035,0.0009681074,0.00950577],"category_scores_gemma":[0.002700157,0.0005952195,0.0009101753,0.003405473,0.0006418328,0.002294721,0.001179136,0.001271683,0.004376781],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008453405,"about_ca_system_score_gemma":0.001953056,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006009631,"about_ca_topic_score_gemma":0.006097811,"domain_scores_codex":[0.9989102,0.0001671455,0.0001301265,0.0002820043,0.0004036379,0.0001068227],"domain_scores_gemma":[0.9988549,0.0003174823,0.00009788729,0.00008199634,0.0006025703,0.00004510502],"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":[0.0007005156,0.0003785334,0.009846187,0.001532126,0.0002724519,0.001344904,0.001697114,0.01853695,0.01512028,0.01694043,0.02400424,0.9096264],"study_design_scores_gemma":[0.0003407237,0.001172039,0.04478804,0.00171954,0.001077841,0.006166894,0.01092778,0.2595367,0.08202169,0.06595259,0.5255428,0.0007533693],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2947868,0.0284702,0.5424339,0.009161941,0.003097478,0.0009591359,0.004580392,0.005918012,0.1105922],"genre_scores_gemma":[0.5463791,0.02053507,0.350545,0.0009700675,0.0005421772,0.0008315707,0.006769444,0.0005928872,0.0728348],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00950577,"threshold_uncertainty_score":0.03179997,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01614067702170933,"score_gpt":0.2476258125023209,"score_spread":0.2314851354806115,"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."}}