Data Quality Measures and Data Cleaning for Pattern Analysis Angkot Transportation in Bandung City

Detecting and repairing `dirty' data is one of the perennial challenges in data analytics, and failure to do so can result in inaccurate analytics and unreliable decisions. To detect errors at an early stage and handle them efficiently, it is necessary to determine steps for cleaning and improving data quality. The data used in this study are data collected from previous studies. Data is collected through two sources, namely the Angkot mobile application and the GPS tracker microcontroller module. Some data cleaning tasks here are performed for geospatial data types. This paper provides an overview of data cleaning problems, data quality, cleaning approaches and requirements for public transportation pattern analysis.