02. Unidade Acadêmica de Educação a Distância e Tecnologia (UAEADTec)
URI permanente desta comunidadehttps://arandu.ufrpe.br/handle/123456789/2876
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Resultados da Pesquisa
Item Pesquisa de satisfação nas ouvidorias gerais dos estados: aplicação e modelos(2020-09-14) Almeida, Nilson Braga de; Ceolin, Alessandra Carla; http://lattes.cnpq.br/7810633996702948; http://lattes.cnpq.br/2458153938745233The legislation in force has required the public sector to provide instruments for the citizen to evaluate the public services provided. This article aims to analyze the satisfaction survey models adopted by the state general ombudsmen with their users, considering aspects such as form of application and elaborated questions. For this, a questionnaire was applied with all the states and the DF, obtaining a sample with 23 federative entities, whose results were analyzed through a qualitative approach. The information collected allowed us to identify that, from this sample, nine general ombudsmen (39.13%) do not apply satisfaction surveys, having as main reason aspects related to technological support; while the others (60.87%) apply, however, without a defined standard as to the questionnaire model adopted. The main difficulty in the elaboration of the surveys involves the definition of questions and the majority of the ombudsmen follow their results, including through indicators, aiming at improvements.Item Avaliação de algoritmos multi-classe para classificação de solicitações enviadas a Ouvidoria Geral do Estado de Pernambuco(2021-03-29) Carvalho, Luiz Henrique Teixeira; Ferreira, Jeneffer Cristine; http://lattes.cnpq.br/3000364145302421The Ombudsman’s Office is a public agency that covers the entire state of Pernambuco and every day receives several requests with the most varied themes involving all other organs of the state, with that in certain times of the year, these requests can come to burden the resources of State. The main objective of this work is to apply the multi-class classification algorithms to the data obtained from the transparency portal, and to try to predict requests sent to the Ombudsman’s Office of the State of Pernambuco To obtain data from the Ombudsman’s Office of the State of Pernambuco, data scraping was carried out on the Pernambuco Transparency Portal of Pernambuco. Data for the years 2017, 2018 and 2019 were obtained. The algorithms Decision Tree, Random Forest, Bagging and kNN were applied to the ombudsman data. The results showed that the automatic data classification algorithms, particularly the Decision Tree, Random Forest, Bagging algorithms achieved 55 percent and 32 percent in the type and organ classes respectively, taking advantage of one hit every two attempts in the type class and one hit every three attempts in the organ class. The algorithms were also evaluated about their performance in time of model creation and training, with the Decision Tree algorithm as the most performative.