Civet coffee, or Kape Alamid, is one of the most expensive type of coffee in the world, made from coffee beans that have been digested and excreted by civet cats. Due to its high value, counterfeit civet coffee has become a growing concern, highlighting the need for reliable methods to verify authenticity. This study developed a portable electronic nose (e-nose) system using MQ gas sensors—specifically MQ2, MQ3, MQ7, and MQ135—along with Artificial Neural Network (ANN) for feature extraction and Support Vector Machine (SVM) for classification to determine the authenticity of Philippine civet coffee based on aroma. Built on a Raspberry Pi platform, the system captured volatile organic compounds from ground coffee samples, processed and analyzed the data for classification, and used a confusion matrix to evaluate accuracy. The results demonstrated an accuracy of 83.33% in distinguishing authentic civet coffee from non-authentic civet coffee.