{"id":"W2772995211","doi":"10.26798/jiko.2017.v2i1.57","title":"PENGARUH STEMMING TERHADAP EKSTRAKSI TOPIK MENGGUNAKAN METODE TF*IDF*DF PADA APLIKASI PDS","year":2017,"lang":"en","type":"article","venue":"JIKO (Jurnal Informatika dan Komputer)","topic":"Edcuational Technology Systems","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Kootenay Association for Science & Technology","funders":"","keywords":"Computer science; Process (computing); Social media; tf–idf; Information retrieval; Weighting; Word (group theory); Keyword extraction; Information extraction; Selection (genetic algorithm); World Wide Web; Artificial intelligence; Mathematics","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":[0.001424776,0.001651599,0.001267371,0.002810646,0.001338119,0.002720327,0.0008756259,0.0009509161,0.01833786],"category_scores_gemma":[0.00397837,0.0005092766,0.001042533,0.0043157,0.000485721,0.002566836,0.0008344981,0.001325827,0.01308348],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005576549,"about_ca_system_score_gemma":0.001659325,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003318393,"about_ca_topic_score_gemma":0.003589598,"domain_scores_codex":[0.9987429,0.000191907,0.000207287,0.0004036804,0.0003499715,0.0001043068],"domain_scores_gemma":[0.9983724,0.0007715816,0.00006408207,0.0001082555,0.0006541247,0.00002973024],"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":[0.0004395671,0.0001005827,0.001299428,0.001555729,0.00009495595,0.000538733,0.0008252603,0.003598818,0.05158732,0.003825324,0.008162495,0.9279719],"study_design_scores_gemma":[0.0003635681,0.001070144,0.01804829,0.001018289,0.0008932349,0.007141138,0.005154526,0.2543088,0.2833753,0.04028918,0.3879067,0.000430981],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.03967915,0.004920327,0.9309915,0.000512398,0.0008362533,0.0003937589,0.003862557,0.006240558,0.0125635],"genre_scores_gemma":[0.1110472,0.004303341,0.8543562,0.0002083852,0.0002117605,0.0006214384,0.008406843,0.001438654,0.01940614],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01833786,"threshold_uncertainty_score":0.06134629,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02767125856360691,"score_gpt":0.271732256020434,"score_spread":0.2440609974568271,"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."}}