摘要：Data redundancy of candidate votes during general elections held once every 5 years in Indonesia is one of the reasons for election malpractice and rigging. Therefore, this study aims to determine the similarity of data object attributes using the Hierarchical of Grid Partition (HGP) to minimize data redundancy (Chenhan et al., 2018; Ding, 2021; Hamza & Recep, 2018; Nur & Karima, 2020; Yijie, 2021). One of such attributes includes having a double ID to prevent data manipulation. Other tools employed to measure or test for similarity are Flowchart measurement, Relation Model, and Class Diagram (Bin et al., 2018; Kaitlyn & Aspen, 2017; Wisal et al., 2019). Data were collected from a sample of 6,215 candidate votes stored in a SQL database to determine their Main Variable (Vu), Secondary Variable (Vs), and Alternate Variable (Va). The result showed 5,211 normal data (K=0) without similarity (single ID) at 83.85%. Furthermore, there are 922 (K=-1) Invalid data with 3 types of object attribute similarities (potentially fraud occurrence) at a medium similarity risk of 14.84%. There are 78 invalid data (K=-1) with 2 object similarities and 4 valid data (K=-1) with a similarity ratio of 0.06%. Results find similar, effective, optimal and accurate data.
关键词：Data Redundancy;Hierarchical Grid Partition (HGP);Data Variabel (Vu;Vs;Va);Result
