Software development management system is important in application development. A proper software development management system will create a team that can adapt to system requirements and changes during application development. Various software development management systems are developed and widely implemented in software development, one of which is Agile Scrum. This study aims to implement well-documented Scrum for end-to-end application development, including the development of servers and mobile applications that we develop. We developed a bus application called SICITRA, with the main feature of being able to help passengers share their travel information with those closest to them. Scrum is used because it has agility which can make application development faster and more organized, and there is a close relationship between everyone involved in the project. The results of this study are that by using well-documented Scrum, we can make it easier to track progress, become a guide during system development, become history and evaluate Scrum implementation during development.

Alter ego is a condition of someone who creates a new character with a conscious state. Original character role play is a game to create new imaginary characters that is used as research material for identification alter ego accounts. The negative effects of playing alter ego are stress, depression, and multiple personalities. Current research only focuses on the phenomenon and impacts of a role-playing game. We propose a new method to detect accounts of alter ego players in social media, especially Twitter. We develop an application to analyze the characteristics of alter ego accounts. Psychologists can use this application to discover the characteristics of alter ego accounts that are useful for analyzing personality so that the results can be used to appropriately handle alter ego players. Most user profiles, tweets, and platforms are used to detect account Twitter. This research proposes a new method using bio features as input data. We crawled and collected 565 bios from Twitter for one month. We observe the data to search for unique words and collect them into a classification dictionary. In this research, we use the cosine similarity method because this method is popular for detecting text and has a good performance in many cases. This research could identify alter ego accounts and other types of Twitter accounts. From the detection results of alter ego accounts, it is possible to analyze the characteristics of Twitter accounts. We use a sampling technique that takes 30% of the data as testing data. According to the results of the experiment cosine similarity obtained an accuracy of 0.95.