<![CDATA[Dynamic skilu caused by ballast voids is one of the main causes of train derailments in Indonesia. Early detection of this condition still relies on measurement trains, which have limited inspection intervals. This study aims to test the accuracy of the third-generation void meter based on the Internet of Things (IoT), which has been redesigned to enhance the precision of measuring ballast voids, train speed, and rail temperature. The research methods include the collection of primary and secondary data, field testing of the device, and statistical analysis. Analysis was conducted using standard deviation tests, ANOVA, the Mann–Whitney test, and MAPE. Test results showed that the system can display data in real-time through the Blynk and ThingSpeak platforms and provide automatic notifications when measurement values exceed tolerance limits. The standard deviation of the device is lower than that of conventional devices, MAPE for all parameters is <10%, and the results of the ANOVA and Mann–Whitney tests show no significant differences between data groups. Thus, the third-generation void meter has proven to have high accuracy and consistency, making it suitable for continuous railway track inspections. Additionally, the void meter has been proven to be 59.70% more cost-effective than the densometer.]]>
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