One form of information and communication technology development is a smartphone. Today's popular smartphone products are the iPhone and the social media used to share opinions is Twitter. One of the topics that is often discussed on Twitter is related to iPhone reviews which can refer to different aspects. Therefore, aspect-based sentiment analysis can be applied to iPhone reviews to get more detailed results. This study applies TF-IDF feature extraction as a weighting vocabulary and the Support Vector Machine classification method. This study also uses hyperparameter tuning to optimize parameters to get the best performance. The results of this study obtained the highest accuracy performance results by using the Support Vector Machine classification on the linear kernel and TF-IDF feature extraction on the camera aspect with accuracy 98.07%, battery aspect with accuracy 97.52%, design aspect with accuracy 96.82%, price aspect with accuracy 98.62%, and specification aspect with accuracy 97.07%. As well as getting an increase in the results of the highest accuracy performance by using hyperparameter tuning on the linear kernel for the camera aspect with accuracy 98.07%, battery aspect with accuracy 97.52%, design aspect with accuracy 97.02%, price aspect with accuracy 98.82%, and specification aspect with accuracy 97.22%.