{"id":"W3135071512","doi":"","title":"Pengelompokan Provinsi di Indonesia Menggunakan Algoritma Partitioning Around Medoids (PAM) Terhadap Indikator Pembentuk Indeks Pembangunan Manusia (IPM) Tahun 2020","year":2021,"lang":"id","type":"article","venue":"","topic":"Data Mining and Machine Learning Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Medoid; Quarter (Canadian coin); Liberian dollar; Cluster analysis; Geography; Business; Statistics; Mathematics; Finance","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001007337,0.001026486,0.00093441,0.0006661409,0.0006481023,0.002514615,0.0006246617,0.0006955644,0.007444094],"category_scores_gemma":[0.001640395,0.0004400428,0.0009897273,0.0007821997,0.0005288752,0.00110974,0.000851928,0.001221279,0.002121143],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006732839,"about_ca_system_score_gemma":0.001019538,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004667131,"about_ca_topic_score_gemma":0.005126012,"domain_scores_codex":[0.999638,0.0001153017,0.0000260997,0.0001093651,0.00007582682,0.00003543566],"domain_scores_gemma":[0.9995607,0.0002645006,0.00003006728,0.00003254054,0.00008895804,0.00002329696],"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":[0.000538279,0.0001312727,0.004208529,0.0005932639,0.0002063398,0.0002483531,0.0005193499,0.1646282,0.005107375,0.01484083,0.01544415,0.793534],"study_design_scores_gemma":[0.000118709,0.0003321882,0.007768667,0.0003069015,0.0002030763,0.0007644225,0.001049051,0.8810428,0.007187718,0.02987853,0.07121564,0.0001322965],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08537514,0.009597717,0.8661415,0.002982825,0.0009210198,0.0002955289,0.0009690549,0.002029056,0.03168818],"genre_scores_gemma":[0.3942573,0.007735758,0.5648448,0.0005288465,0.000328641,0.0005287474,0.001701863,0.0005238992,0.0295501],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007444094,"threshold_uncertainty_score":0.02490294,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01479335152922614,"score_gpt":0.2590098776813126,"score_spread":0.2442165261520865,"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."}}