STREAM PROCESSING IMPLEMENTATION FOR VOTE COUNTING IN THE 2024 GENERAL ELECTION ELECTRONIC RECAPITULATION SYSTEM

• Main Author: Jason, Nathaniel
• Format: Final Project
• Language: Indonesia
• Online Access: https://digilib.itb.ac.id/gdl/view/76890
• Institution: Institut Teknologi Bandung

Stream processing is a data processing method by processing data as soon as it is generated. This method can be used to do vote counting in the 2024 General Election Electronic Recapitulation System. The mobile application for the Electronic Recapitulation System or Sirekap 2020 is a software used as a management tool and publishing the results of vote counting. In its implementation, the vote counting process is still considered inefficient due to the considerable amount of time required. Currently, the vote counting process is performed periodically and asynchronously by the software at specific time intervals. Each time vote counting is performed, the software must repeat the total vote counting process by aggregating all the data. This kind of process is considered inefficient as it repeats the vote counting process every time the software performs vote counting. A solution that can address this problem is the use of stream processing in the vote counting process. The implementation of stream processing can be done using Kafka software. There are several Kafka components that can be used to address the problem and support the solution, namely Kafka Broker, KafkaSQL, and Kafka Connect. The result of stream processing is an automated system for vote counting. Vote counting using the stream processing method has been successfully done with query execution times much faster than batch processing, especially as the amount of data increases.