Data is something that can be manipulated by irresponsible people and causing fraud. The use of log files, data theft, unauthorized person, and infiltration are things that should be prevented before the problem occurs. The authenticity of application data that evaluates the performance of company employees is very crucial. This paper will explain how to maintain the company’s big data as a valid standard service to assess employee performance database, based on employee performance of MariaDB or MySQL 5.0.11 with InnoDB storage engine. The authenticity of data is required for decent digital evidence when sabotage occurs. Digital forensic analysis carried out serves to reveal past activities, record time and be able to recover deleted data in the InnoDB storage engine table. A comprehensive examination is carried out by looking at the internal and external aspects of the Relational Database Management System (RDBMS). The result of this research is in the form of forensic tables in the InnoDB engine before and after sabotage occurs.

Nowadays is referred to as the era of big data where every individual or organization produces large amounts of data through various digital devices used. Higher education institutions continuously produce data and only strore it in various data formats and physical files, thus creating massive raw data. Collection of data created from a long time produces big data, so it is difficult to be treated manually or processed using conventional data processing applications. Higher education is an educational institution that always produces data from time to time continuously through various affairs in each of its parts. Multi campus institutions have physical locations that are far apart from one to another with more than one physical infrastructure. Each campus branch produces its own data, this requires a though about how to build a large data infrastructure, so that stakeholders can use various data from each branch of the campus for the analysis process of various needs of tertiary institutions. Multi campus big data infrastructure is the focus of this research, through the stages of big data sampling on multiple campuses using statistical and prototyping methods, so that data flow and data unity can be standardized in all branches of the campus by minimizing infrastructure constraints that are based on socioculture and spatial aspects.

Paguyuban bonsai ornamental plant is a premium ornamental plant, it becomes interesting if its marketing adopts information technology while aligning the goals of its members. The modern web concept is dynamic and responsive used to support the marketing activities of ornamental plant farmers through object oriented based design. This web design uses the Waterfall method using five main stages and fifteen subwork items. All sub items of work are estimated based on a triple time estimate that has a variable start time, actual time, and late time. Estimation of bonsai dynamic web marketing projects takes 115 days of 95%, the potential of the project to finish 111 days earlier by 2,5%, and the potential of the project to be 118 days late by 2,5%. Modelling using Waterfall produces a standart error of 0,0509 smaller than the standart deviation of 1,9, so it can be said that this model is very good and representative to be carried out according to the specified time. Bonsai ornamental plant marketing websites have use responsive concepts using PHP MVC Framework and dynamic concepts using bootstrap.