{"id":"W4387217162","doi":"10.59697/jik.v5i1.318","title":"IMPLEMENTASI DATA MINING PENGELOMPOKAN JUMLAH DATA PRODUKTIVITAS UBINAN TANAMAN PANGAN BERDASARKAN JENIS UBINAN DENGAN METODE CLUSTERING DIKAB LANGKAT (STUDI KASUS : BADAN PUSAT STATISTIK LANGKAT)","year":2021,"lang":"id","type":"article","venue":"Jurnal Informatika Kaputama (JIK)","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":"Horticulture; Mathematics; 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":[0.005897699,0.001475465,0.001483374,0.00348703,0.001657677,0.006458311,0.002241923,0.00135488,0.007126751],"category_scores_gemma":[0.01427845,0.001117582,0.002174143,0.005165248,0.0007411964,0.004356937,0.002379519,0.002443312,0.007306277],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001712816,"about_ca_system_score_gemma":0.004600246,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009973546,"about_ca_topic_score_gemma":0.01357702,"domain_scores_codex":[0.9945788,0.001346585,0.0005858389,0.001285385,0.001903554,0.0002998905],"domain_scores_gemma":[0.9889141,0.004536101,0.0006846506,0.00185495,0.003663053,0.000347226],"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":[0.0007626907,0.0007734763,0.0457366,0.002241744,0.0005576185,0.0005232684,0.002331096,0.02591865,0.0272975,0.01770418,0.02962649,0.8465268],"study_design_scores_gemma":[0.0003098053,0.000823467,0.05745769,0.001256965,0.001030917,0.001857469,0.007771161,0.3813995,0.1451431,0.08173547,0.3207689,0.0004456255],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09719436,0.00313195,0.8341205,0.006918775,0.0004634632,0.001809555,0.01143492,0.01279805,0.03212842],"genre_scores_gemma":[0.233248,0.002902785,0.7274575,0.0008467776,0.0002027504,0.001199261,0.01284658,0.0008802554,0.02041614],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009973546,"threshold_uncertainty_score":0.0311904,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09280023390331626,"score_gpt":0.3454898411828032,"score_spread":0.2526896072794869,"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."}}