{"id":"W4387216776","doi":"10.59697/jik.v4i1.352","title":"PERANCANGAN SISTEM PENDETEKSI BERITA HOAX MENGGUNAKAN ALGORITMA LEVENSHTEIN DISTANCE BERBASIS PHP","year":2020,"lang":"en","type":"article","venue":"Jurnal Informatika Kaputama (JIK)","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":"Hoax; Levenshtein distance; Computer science; Word (group theory); The Internet; Precision and recall; Information retrieval; Plagiarism detection; Measure (data warehouse); Artificial intelligence; Data mining; World Wide Web; 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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000624344,0.0005000429,0.0006413691,0.0002400102,0.0004687549,0.0005310696,0.002502087,0.0002994727,0.00004681416],"category_scores_gemma":[0.0002256126,0.0004831444,0.0002808916,0.001182011,0.0001642558,0.002782425,0.0006399082,0.0008138799,0.0006220075],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002600011,"about_ca_system_score_gemma":0.0002675023,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003467684,"about_ca_topic_score_gemma":0.00002788609,"domain_scores_codex":[0.9960043,0.0001359847,0.001300637,0.0005857557,0.001185479,0.0007878552],"domain_scores_gemma":[0.9973992,0.0002001268,0.0006322994,0.0009607082,0.00032284,0.0004848971],"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":[0.0001167433,0.0002709249,0.006861954,0.0008463225,0.0006139292,0.0003861502,0.0271298,0.001395165,0.002745796,0.3589343,0.04572348,0.5549755],"study_design_scores_gemma":[0.00225271,0.0006444738,0.006066402,0.0002867153,0.00005085772,0.0005888994,0.00163546,0.2986329,0.005823834,0.002002899,0.6803815,0.001633298],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2861236,0.002144721,0.543978,0.08054437,0.003222548,0.002525771,0.0003061219,0.004448944,0.07670592],"genre_scores_gemma":[0.9530724,0.0000377079,0.04173841,0.00379665,0.0004293494,0.0000867953,0.0000342378,0.00004065176,0.0007637974],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6669488,"threshold_uncertainty_score":0.999762,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02357727693001437,"score_gpt":0.2246389803434465,"score_spread":0.2010617034134321,"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."}}