SELF-LEARNING PERSONAL FINANCIAL ASSISTANT ANDROID APPLICATION USING MVVM ARCHITECTURAL PATTERN

SENTOSA, ARRIVAL DWI (2019) SELF-LEARNING PERSONAL FINANCIAL ASSISTANT ANDROID APPLICATION USING MVVM ARCHITECTURAL PATTERN. S1 thesis, Universitas Mercu Buana Jakarta.

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Abstract

Personal Financial Management is an essential thing for everyone. Some of the financial purposes are goal setting, money cash flow management, preparing for children education or just for an investment. Most of people don’t know how to manage it well then try to use an application to help manage it. But mostly such these applications are not user friendly and confusing for some of us because some of financial words that we don’t understand. To make a user friendly and good user experience of user when using personal financial application, it can be applied the Natural Language Processing (NLP). The utilization of NLP is able to use the application easier, since NLP can predict user input means, give the suggestion and prediction periodically to inform and approach the user to reach their own financial goal. To perform the best result of NLP Implementation, there are two methods that used: Intent Classification using SVM Algorithm and Named Entity Recognition Conditional Random Field (NERCRF). The dataset used are trained based on user input interaction inside the application and labeled based on features/intent in the application such as: Cash flow input, Budgeting, Goals and Financial suggestion. The prototype is developed for Android platform based on Model-View- ViewModel Software Architectural Pattern as a best practice of Android Development Architectural Pattern. Key words: Personal Financial Management, Intent Classification, Named Entity Recognition, SVM Algorithm, Android, Model-View-ViewModel

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