{"id":"W4384573862","doi":"10.59697/jsik.v6i2.179","title":"DATA MINING DALAM PENGELOMPOKKAN JUMLAH DATA PRODUKTIVITAS TANAMAN PANGAN MENGGUNAKAN METODE CLUSTRING K-MEANS ( STUDI KASUS : BADAN PUSAT STATISTIK KOTA BINJAI)","year":2022,"lang":"id","type":"article","venue":"Jurnal Sistem Informasi Kaputama (JSIK)","topic":"Data Mining and Machine Learning Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Kootenay Association for Science & Technology","funders":"","keywords":"Physics; Horticulture; Humanities; Forestry; Geography; Biology","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":[0.005118166,0.001797871,0.002311836,0.004927792,0.001991592,0.007133277,0.002128361,0.001717505,0.006813863],"category_scores_gemma":[0.01467689,0.001103817,0.002727612,0.009392253,0.001087148,0.004167843,0.002816661,0.003011544,0.006117844],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001787841,"about_ca_system_score_gemma":0.003827382,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007258664,"about_ca_topic_score_gemma":0.009469508,"domain_scores_codex":[0.9953439,0.00106738,0.0005649542,0.00120618,0.001615267,0.0002024451],"domain_scores_gemma":[0.9913908,0.004382407,0.0007136896,0.001033003,0.002224073,0.0002559366],"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":[0.0007836905,0.0004806703,0.03688966,0.005130873,0.001072478,0.0005521729,0.003479153,0.02767988,0.02020528,0.01739213,0.02673015,0.8596038],"study_design_scores_gemma":[0.0003114649,0.0009161424,0.0753402,0.002902196,0.001835474,0.002331522,0.01207188,0.2754669,0.09327487,0.1174633,0.4173826,0.0007034845],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1414188,0.01628381,0.769131,0.007762929,0.00112298,0.001583369,0.01648115,0.01012609,0.03608988],"genre_scores_gemma":[0.2661697,0.01019878,0.6839709,0.0009541836,0.0002989069,0.001529256,0.01485203,0.00138414,0.02064206],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007258664,"threshold_uncertainty_score":0.02706772,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08366420916862938,"score_gpt":0.3164769839475122,"score_spread":0.2328127747788828,"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."}}