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The purpose of this research is to develop a new model by integrating the impact and readiness of the model that has been done before. The steps are taken a start from adopting, combining and adapting the previous model, namely by identifying the impact of ICT readiness in the countryside. The researchers developed the model based on the input-process-output logic and the processional and causal model of the IS impact models. This study has identified 9 variables and 45 indicators that have a relationship between impact and readiness. The findings of this study are the limited number of human resources who control ICT, the inequality of networks, the adequacy of institutional needs and the existence of budget support from authorized institutions and information systems that have not accommodated all service needs and have not been implemented in all villages. The conclusion of this study is combination model by integrating four preparedness model variables and five variables from the success model. In addition to the development process, clarity of coherent relationships between models, variables, indicators, and questions from each indicator are also presented in this study.

Named Entity Recognition (NER) or Named Entity Recognition and Classification (NERC) is one of the main components of an information extraction task that aims to detect and categorize named entities in a text. NER is generally used to detect people's names, place names, and organization of a document, but can also be extended to identify genes, proteins, and others as needed. NER is useful in many NLP (Natural Language Processing) applications such as question-answering, summaries, and dialog systems because it can reduce ambiguity. NER also deals with other information extraction tasks such as relation detection, event detection, and temporal analysis. To avoid this need to train data source. The data train can be taken from various sources of news/articles crawled on the internet. The news will then be annotated by users with various labels. The news/article sources are in the thousands, while to make this training by using file is manual. And sometimes there is an error because this manual was made when it will form the NER model as needed. This research will be made so that training files can be assisted by using applications so that the error rate can be smaller or there will be no errors.

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