Meshesha, Million (PhD)Wondimu, Fasika2019-05-082023-11-182019-05-082023-11-182019-02-05http://etd.aau.edu.et/handle/12345678/18211Yet it is true ideally that ATM service thought to be in service 24 hours a day, 7 days a week, and 365 days a year; But, frequently it has been seen on these self-service machine out of service. This study therefore aims to find the reasons for ATMs out of service, taking Dashen bank ATMs as a case. The research follows a hybrid knowledge discovery process (KDP) model which has six steps. The first step is understanding the problem, which helps to describe the problem and identify attributes for data collection from purposely selected 14 ATMs within periods, December 31, 2017 – May 04, 2018. This is followed by understanding the data and prepare dataset for data mining tasks. Preparation of data set includes constructions of new attributes from the existing dataset, removing unnecessary attributes and inconsistence values. From the collected dataset of ProView ATMs real time monitoring console as per the objective of this research 44 events describing out of service happening were identified. The crucial step in KDP is data mining which enables to design a predictive model. In this study WEKA knowledge discovery tool is used to identify the reasons for ATM out of service. Experimental analysis shows that J48 decision tree registers the best result with accuracy of 52.7059 %. Finally, using the best predictive model of J48 decision tree we designed a prototype on NetBeans IDE 8.2 platform. This prototype was evaluated based on ISO 9241-11 UT test features by domain experts. According to the UT test the prototype was effective, efficient and hold on user satisfaction. However, an automatic integration of the model with ProView will help to detect and solve the problem immediately for enhancing ATM in service.enDesigning A ModelIdentifying the ReasonATM Out of ServiceCase of Dashen BankSignificanceThesis