Jurnal Algoritma, Logika dan Komputasi

 Many motors suddenly stop working due to oil leaks in carburetor motors. Lubricant oil leaks can result in decreased engine performance, piston damage to breaking down in the middle of the road. Therefore, to prevent early engine damage, lubricant oil leak notification is needed. Leakage at a certain level can still be tolerated, but up to a certain level, the leak can no longer be tolerated, so good notification is needed to determine the severity of the oil leak. To determine how severe the oil leak is in the engine, precise measurements are needed. One solution offered is to use fuzzy Tsukamoto in determining lubricant oil leaks Based on temperature and origin parameters in the actual conditions that occur. In this study, it will be determined how much lubricant oil leakage occurs in motorcycles that have a carburetor system. Where the input to be used is temperature and smoke. With the respective membership degrees are normal, moderate and severe. While the output produced is in the form of repair recommendations, where for moderate repair suggestions it has a value that determines how much it needs to be repaired. While normal output does not need to be repaired, while the highest output is a suggestion to be repaired immediately without delay. The use of Tsukamoto fuzzy in determining the severity of leaks produces a fairly good accuracy of 96.67% of motorcycles that experience lubricant oil leaks can be detected correctly. 3.33% of errors lie in other causes that are not as specific.

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