Muhamad Aizat Bin Abdurahim (Malaysia)

Muhamad Aizat has developed a machine learning project that will detect a pin failure from testing product data that focused on enhancing current processes which is manual effort of debugging why the pin was fail and replace the pin. The process is lengthy where during the events of products fail in testing, a technician who is debugging the TIU will first address the issue by manually locate the failure pins. The criteria of the pin failure will be determined by test program, and it’s embedded into software that the machine runs. While testing, if the scenario happens, the software will notify user to check the pin of TIU if there is any pin failure. After that the technician will debug the pin and replace it. To improve this process, the project embedded machine learning to analyze the data by predicting the failure before it actually fails. It will tells the user that certain criteria of data will likely fail the testing and notifies the location of pin that going to cause the failure.