{"id":"W4322098178","doi":"10.29100/jipi.v7i4.3237","title":"DATA MINING K-MEDOIDS DAN K-MEANS UNTUK PENGELOMPOKAN POTENSI PRODUKSI KELAPA SAWIT DI INDONESIA","year":2022,"lang":"id","type":"article","venue":"JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika)","topic":"Data Mining and Machine Learning Applications","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"Encana (Canada)","funders":"","keywords":"Physics; Forestry; Horticulture; Biology; Mathematics; Geography","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":[0.001922764,0.001481577,0.001960629,0.002185964,0.001031431,0.002063284,0.001333204,0.001238322,0.002857504],"category_scores_gemma":[0.005012476,0.0006459921,0.002616739,0.002940911,0.0005493092,0.001358068,0.000882165,0.001692473,0.001792122],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001259583,"about_ca_system_score_gemma":0.002133948,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01184105,"about_ca_topic_score_gemma":0.01241837,"domain_scores_codex":[0.9984617,0.0003486579,0.0001789753,0.0005620915,0.0003328758,0.0001156492],"domain_scores_gemma":[0.997583,0.001330105,0.000258095,0.0001577166,0.0005852327,0.00008587588],"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":[0.001421696,0.000583761,0.03588271,0.003115407,0.0009335607,0.0005006003,0.001285108,0.2442964,0.01655947,0.005293652,0.0251027,0.6650248],"study_design_scores_gemma":[0.0002009348,0.0008404369,0.0396603,0.0006088113,0.0006025523,0.0006830218,0.002677917,0.8471026,0.02473263,0.02179871,0.06068798,0.0004040822],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2376148,0.009027756,0.7167335,0.003954944,0.0008936319,0.001094581,0.01550912,0.003861307,0.01131046],"genre_scores_gemma":[0.5231071,0.004082218,0.4467464,0.000511656,0.0002740257,0.001227174,0.01267296,0.0002645687,0.01111395],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01184105,"threshold_uncertainty_score":0.02354425,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02938817875815523,"score_gpt":0.2744174585292508,"score_spread":0.2450292797710955,"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."}}