PPT Komunikasi informasi akuntansi

Nanda Afrimelta , 2022

SUCI SAFITRI, 2020

AS

SISTEM INFORMATIKA LABORATORIUM KRSEHATAN

Sistem Informasi dari Perspektif Akuntan LINGKUNGAN INFORMASI Kita mulai dari studi SIA dengan pengakuan bahwa informasi merupakan sumber daya bisnis seperti sumber daya bisnis lainnya sebagai bahan baku, modal, dan tenaga kerja. Informasi sangat penting untuk kelangsungan hidup organisasi bisnis kontemporer. Setiap hari, terjadi arus informasi kepada para pengambil keputusan dan pengguna lain untuk memenuhi berbagai kebutuhan internal. Informasi mengalir keluar dari organisasi untuk pengguna eksternal, seperti pelangggan, pemasok, dan pemangku kepentingan yang memiliki kepentingan dalam perusahaan.

PROJETO DE OBRAS DE PONTES

2014

Software industry has been rapidly growing from the last couple of decades. Due to this growth and advancement many issues are going to be occurred. There are number of factors that affect the whole software development process. This research has focused on the most important risk factors that affect the overall software project and risk mitigation reviewed from the literature and my own experience. The main aim of this research is to collect the most important risk factors and risk mitigation for the development of intelligent network using an Artificial Intelligence technique known as Rule Based System. This is helpful for the project managers to make decisions. For the accomplishment of this task Firstly an exploratory survey has been conducted to prioritize the twenty requirements and fifty software risk mitigation factors. A sample of 200 respondents is selected out of which 100 are the university students and 100 are the IT professionals of a well reputed software house. Secondly a Rule Based technique is used to generate the risk mitigation intelligent network. Rules have been stored in the form of risk mitigation in relationship with risk factors. The system follows five steps of execution. First of all risk factors has been prioritized based upon the associations of risk factors and risk mitigation. Secondly, it creates new relationships between them. Thirdly, enter these relationships as rules into the Knowledge Base or Rule Base. At the fourth stage, an RBS engine executes these rules and finally an intelligent risk mitigation network is generated.