{"id":"W4402318875","doi":"10.47861/jkpu-nalanda.v2i5.1287","title":"Prediksi Disleksia pada Anak menggunakan Metode Naive Bayes","year":2024,"lang":"en","type":"article","venue":"Jurnal Kajian dan Penelitian Umum","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":"Naive Bayes classifier; Psychology; Computer science; Artificial intelligence; Support vector machine","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":[0.002274797,0.001116786,0.001316137,0.001591666,0.0009057865,0.003166406,0.0009290848,0.001176107,0.009189408],"category_scores_gemma":[0.007156515,0.000545042,0.000950662,0.001055285,0.0005377977,0.002343,0.0007102059,0.001401547,0.003287497],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008075224,"about_ca_system_score_gemma":0.001422885,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004445525,"about_ca_topic_score_gemma":0.003401749,"domain_scores_codex":[0.9984005,0.0004427834,0.0002534965,0.0003971595,0.0003728628,0.0001331837],"domain_scores_gemma":[0.9971803,0.001849391,0.000142855,0.0001114954,0.0006697135,0.00004612887],"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":[0.0008660519,0.0002869729,0.01422578,0.001429132,0.0003347085,0.0007309516,0.0005227439,0.04910863,0.007823497,0.02033166,0.01567139,0.8886685],"study_design_scores_gemma":[0.0002315652,0.0004705589,0.01002106,0.001470194,0.0004875939,0.002989393,0.001095422,0.8234277,0.01721106,0.09158894,0.05077668,0.0002297979],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09260558,0.01222332,0.8623696,0.002777707,0.001059643,0.0004983598,0.001971978,0.003192913,0.02330099],"genre_scores_gemma":[0.5304682,0.005639269,0.4416182,0.0008408806,0.0004887854,0.0005537656,0.003214811,0.000377908,0.01679818],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009189408,"threshold_uncertainty_score":0.03074157,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01030826851115983,"score_gpt":0.2689851265052104,"score_spread":0.2586768579940506,"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."}}