Analysis of Boarding House Payment Patterns Using Data Visualization Techniques to Identify Delay Factors

Abstract

In an increasingly competitive business environment, the ability of Micro, Small, and Medium Enterprises (MSMEs) to survive heavily depends on effective cash flow management. Boarding house businesses, as a form of MSMEs in the service sector, face crucial challenges due to late rental payments by tenants. Management practices that are often reactive and intuitive have proven less effective in identifying the root causes of such issues. This study aims to apply an analytical approach using data visualization techniques to analyze rental payment patterns at Kost Green, Semarang. The main objective is to discover significant temporal patterns and identify tenant profile factors that strongly correlate with late payment behavior. The methodology employed is exploratory data analysis with a quantitative and visual approach, using primary data in the form of historical rental payment transactions over a one-year period, covering attributes such as tenant status and room type. The analysis process begins with a data preprocessing stage, in which a key analytical feature, Days_Late, is engineered to measure the duration of delays. The analysis is conducted using the Python programming language supported by the Pandas, Matplotlib, and Seaborn libraries. The findings reveal the existence of high-risk tenant segments (students) and critical time periods (certain months of the year) when delays tend to increase. The outcome of this research is a visual analytical report that provides a strong foundation for Kost Green management to make data-driven decisions, design more proactive and segmented billing strategies, and ultimately improve payment discipline and maintain healthy business cash flow.