Implementasi Algoritma Random Forest untuk Menentukan Tingkat Keberhasilan Proyek pada Sistem Work Order (Studi Kasus: PT XYZ)

Abstract

PT XYZ is a company focused on technological innovation to provide modern, effective, and efficient solutions across various aspects of life. As a pioneer in the technology evolution industry, PT XYZ combines expertise in software development and the latest technologies to create positive transformation for society and businesses. In project implementation, PT XYZ faces challenges in determining project success levels objectively and measurably, particularly within the context of the work order system. This condition leads to less optimal strategic decision-making, increased risk of losses, and difficulties in conducting comprehensive, data-driven project evaluations. To address these issues, this study develops a web-based project success prediction system within the work order system by implementing the Random Forest algorithm and the Agile development approach. The Random Forest algorithm is developed using the Python programming language to classify project success levels based on several historical parameters, such as completion duration, budget, and profit percentage. The system is equipped with a user interface developed using PHP with the Laravel framework and a MySQL database, enabling efficient and integrated data processing and visualization. The results show that the implementation of the Random Forest algorithm improves prediction accuracy and provides recommendations that can support management in decision-making. The Agile approach also offers high flexibility in adapting the system to user requirements. Through this system, PT XYZ is expected to optimize work order management and proactively minimize the risk of project failure in a data-driven manner.