{"id":"W4390512626","doi":"10.33322/juke.v1i2.33","title":"Desain Model Artificial Intelligence Untuk Peningkatan Customer Experience &amp; Penjualan Tenaga Listrik Melalui Penambahan Fitur Virtual Customer Support Pada Aplikasi PLN Mobile","year":2023,"lang":"en","type":"article","venue":"Jurnal Energi dan Ketenagalistrikan","topic":"Information Retrieval and Data Mining","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Positive Living North","funders":"","keywords":"Computer science; Customer experience; Customer intelligence; Customer service; Service (business); Service quality; Customer advocacy; Business; Marketing","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":[0.0006190346,0.0005163569,0.0002620484,0.0007262861,0.0003634586,0.002939905,0.000696935,0.000578531,0.006796872],"category_scores_gemma":[0.001927925,0.0001750467,0.0003921287,0.0006714925,0.0004351761,0.002268104,0.0008853471,0.0007344787,0.001611636],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001372166,"about_ca_system_score_gemma":0.001026911,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006412031,"about_ca_topic_score_gemma":0.00801182,"domain_scores_codex":[0.9995276,0.0001249546,0.00002523887,0.0001314976,0.0001451231,0.00004560155],"domain_scores_gemma":[0.9993659,0.0003376135,0.00005604298,0.00006271872,0.0001453944,0.00003245177],"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":[0.000839607,0.001584936,0.06940195,0.000968284,0.0002107545,0.001466279,0.005699402,0.174464,0.01446294,0.1232307,0.01846549,0.5892056],"study_design_scores_gemma":[0.0000369074,0.0003848684,0.01750033,0.0002169448,0.0001102966,0.0004567077,0.002806019,0.8901817,0.007899135,0.02871493,0.05162077,0.00007135123],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.4830374,0.001472379,0.3519907,0.002999942,0.0002146854,0.000694444,0.001675719,0.0019426,0.1559721],"genre_scores_gemma":[0.8997194,0.0007818338,0.06619994,0.000226378,0.00002669775,0.0003221705,0.001175022,0.00006317885,0.03148546],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.006796872,"threshold_uncertainty_score":0.0227378,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04755414396880296,"score_gpt":0.2953006809715089,"score_spread":0.2477465370027059,"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."}}