{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","sts","scholarly_communication","open_science","research_integrity"],"consensus_categories":["metaepi_narrow","open_science"],"category_scores_codex":[0.008641819,0.001491594,0.001607022,0.0008886784,0.00575246,0.002904639,0.01978868,0.0002707639,0.0001489017],"category_scores_gemma":[0.001192586,0.001600232,0.0002699174,0.003133755,0.0004134625,0.0060163,0.03374936,0.003607232,0.0001691001],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009532066,"about_ca_system_score_gemma":0.002005215,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005121407,"about_ca_topic_score_gemma":0.0008224136,"domain_scores_codex":[0.9858479,0.001296602,0.003083663,0.003709043,0.003520036,0.002542739],"domain_scores_gemma":[0.9825228,0.001206661,0.002561233,0.01220961,0.0003935463,0.001106135],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0004191297,0.0023787,0.01652834,0.001250265,0.002919437,0.0007888607,0.0266303,0.008664949,0.0003995891,0.01414297,0.2189392,0.7069383],"study_design_scores_gemma":[0.001716749,0.0008053316,0.007229527,0.0002355862,0.0005077726,0.000678723,0.005891558,0.2568888,0.00002985578,0.00002559882,0.7244555,0.001535005],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2337314,0.01443666,0.3583315,0.06627549,0.03805354,0.02070991,0.09095852,0.009699022,0.167804],"genre_scores_gemma":[0.8684278,0.0003639754,0.06875107,0.001286161,0.002346443,0.0005465542,0.03756744,0.000434416,0.02027611],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7054032,"threshold_uncertainty_score":0.9997833,"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."}}