<![CDATA[The purpose of this research is to propose a classification model using the decision tree method that can detect the internal status of UPS batteries by using resistance measurements from a battery tester. Resistance data of UPS batteries were collected from measurements conducted under various conditions. This study focuses on supervised learning models, where the data is processed to form a decision tree using the C4.5 classification algorithm. The test results show that the classification of UPS battery internal status can be achieved with high accuracy. The data presentation results were obtained from 100 units of battery systems in UPS, with two conditions identified: 38 units of batteries with a Normal condition and a presentation data accuracy rate of 100%, and 62 units of batteries with a Fault condition and a presentation data accuracy rate of 100%. By using this classification model, users can monitor the performance of the internal battery in the UPS system and take necessary actions to maintain stable UPS system operation and usage.]]>
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