Analysis Of Licensing Data Using Naive Bayes And Decision Tree Algorithms To Evaluate The Performance Of Digital Public Services (Case Study: Invesment and One-Stop Integrated Services Office Of Medan City) - Parhusip, Nelviony; Muhammad Iqbal; Zulham Sitorus (Jurnal Multimedia dan Teknologi Informasi (Jatilima), 2025)
Digital public services have become increasingly essential with the rapid development of technology and information. Over the past six years, the digital transformation of public services at the Investment and One-Stop Integrated Services Office (DPMPTSP) of Medan City has been highly significant. This can be observed from the national implementation of Online Single Submission (OSS) and the “Si Cantik” online application by the Ministry of Communication and Information in 2018, the launch of the “Sipandu” digital service application in Medan City in 2022, and the inauguration of the Public Service Mall and “Si Medan Pantas” application in 2024. In this digital era, innovation in public service is a necessity that cannot be overlooked, especially in efforts to improve the efficiency and effectiveness of licensing processes. This study aims to evaluate digital public service performance by analyzing licensing data in Medan City. The methods applied in this research are Naive Bayes and Decision Tree algorithms, utilizing the Orange data mining tool to optimize the assessment of digital public service performance.The main findings of this study highlight the evaluation of public service performance and identify potential areas for innovation, ideas, or new insights to enhance future public service delivery..