<![CDATA[Audio-based abusive language in short-form videos is difficult to moderate when the spoken content does not appear in captions. This study compares a deep neural network (DNN) trained on a 183-dimensional engineered feature vector with a convolutional neural network (CNN) trained on 64 by 87 Mel-spectrograms for classifying eight Indonesian abusive keywords. From 923 YouTube Shorts, 272 segments were manually curated, and 151 source clips were augmented into 906 samples standardized to 2 seconds at 22,050 Hz. Evaluation used stratified 5-fold cross-validation, a 70:30 stratified holdout, class-wise metrics, and confusion matrices. The DNN achieved a mean cross-validation accuracy of 87.08 percent with a standard deviation of 2.12 percentage points, and a holdout accuracy of 83.82 percent with a macro F1-score of 83.24 percent. The CNN reached a mean cross-validation accuracy of 81.33 percent with a standard deviation of 30.48 percentage points, but only 13.97 percent holdout accuracy and a 4.00 percent macro F1-score, indicating severe instability and class collapse. The engineered-feature DNN was therefore more stable in this small-data setting, although the results do not establish an intrinsic architectural superiority. Limitations include synthetic augmentation, class imbalance, single-rater curation, an isolated eight-word vocabulary, and no transfer-learning or full-video evaluation.]]>
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