This document summarizes a study that aimed to evaluate how well Mel-Frequency Cepstral Coefficients (MFCC) features extracted from Indonesian speech signals relate to four emotions: happy, sad, angry, and fear. Nearly 300 speech signals were collected from actors speaking Indonesian sentences with different emotions. Using support vector machine classification, the study found that the Teager energy feature and the first MFCC coefficient were most crucial for prediction, achieving 86% accuracy. Additional initial MFCC features increased accuracy slightly, but more than four features had negligible effects.