Before the zoning policy, students or their parents tend to choose a recognized school with high educational quality despite its distance. New Student Admissions or Penerimaan Peserta Didik Baru (PPDB) is a school zoning enrollment system that aims to reduce the student travel distance. The online-based PPDB system requires home location input supplemented with legal documents as validation mechanism. However, falsifying home residence or enrollment fraud could not be identified by the PPDB system. This study examines the possible fraud cases from the PPDB enrollment ranks data. The ranks data forms a graph relationship between the registrant and the school. Every data contains a longitude-latitude point, and it is the main factor for accepting based on PPDB policy. The process is trying to analyze the connection between distance gap distribution derived from the ranks data, with the concurrent fraud cases. Because the distance gap distribution still has a missing value on several gap points, it is useful to use KDE (Kernel Density Estimation) to estimate those unknown values. KDE will result in estimated distance gap distribution. The distance gap distribution is affected by the residence location that is plotted on a geo map. When there's an uncommon location of some registrant it will create fluctuation on the distance gap distribution. The gap distribution value exceeds the estimated distance gap distribution from this situation and will be detected as an enrollment fraud. The process to detect enrollment fraud is handled with a graph algorithm. The graph algorithm traverses the graph data and gets ranked registrant from a school. The data are grouped every two meters and check whether its count does not exceed the estimated distance gap distribution. The graph algorithm builds over the PPDB system and tests several manipulated residence locations. It could detect those manipulated data and has a fast process since it only took less than one second.