{"id":"W4404578729","doi":"10.62951/bridge.v2i4.249","title":"Prediksi Pengaruh Kegiatan MBKM terhadap Mahasiswa menggunakan Metode K-Nearest Neighbor","year":2024,"lang":"en","type":"article","venue":"Bridge","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":"Computer science","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":[0.0005695001,0.0006710034,0.0007657558,0.0008560907,0.0006745147,0.001480414,0.0006734857,0.0005152264,0.004162143],"category_scores_gemma":[0.001872762,0.0003233209,0.0005445165,0.001034567,0.0003015544,0.001787981,0.0007745062,0.0007495939,0.001405291],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003571531,"about_ca_system_score_gemma":0.0008917778,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004804937,"about_ca_topic_score_gemma":0.005309057,"domain_scores_codex":[0.9994923,0.00009387864,0.00004994523,0.0001494375,0.0001591031,0.00005535487],"domain_scores_gemma":[0.9994936,0.0002009587,0.0000406453,0.00004227587,0.0001980868,0.00002434789],"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":[0.000448524,0.000280717,0.01344996,0.0008544262,0.0001062522,0.0004425022,0.0007359168,0.051484,0.01305243,0.009447589,0.004509131,0.9051886],"study_design_scores_gemma":[0.00008321687,0.000629805,0.02073288,0.0004253398,0.0002461431,0.002115246,0.002923094,0.8795962,0.03162679,0.02215436,0.03926259,0.0002042791],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4131367,0.00822337,0.5512924,0.000943714,0.0005131236,0.0002013033,0.001321928,0.001624233,0.02274326],"genre_scores_gemma":[0.7742047,0.002981485,0.2103361,0.00008416358,0.00006622495,0.000161281,0.001360076,0.0001962832,0.01060966],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004804937,"threshold_uncertainty_score":0.0139237,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02010417519584809,"score_gpt":0.2862554780101067,"score_spread":0.2661513028142586,"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."}}