To avoid citizen disputes, hate speech on social media, such as Twitter, must be automatically detected. The current research in Indonesian Twitter focuses on developing better hate speech detection models. However, there is limited study on the explainability aspects of hate speech detection. The research aims to explain issues that previous researchers have not detailed and attempt to answer the shortcomings of previous researchers. There are 13,169 tweets in the dataset with labels like “hate speech” and “abusive language”. The dataset also provides binary labels on whether hate speech is directed to individual, group, religion, race, physical disability, and gender. In the research, classification is performed by using traditional machine learning models, and the predictions are evaluated using an Explainable AI model, such as Local Interpretable Model-Agnostic Explanations (LIME), to allow users to comprehend why a tweet is regarded as a hateful message. Moreover, models that perform well in classification perceive incorrect words as contributing to hate speech. As a result, such models are unsuitable for deployment in the real world. In the investigation, the combination of XGBoost and logical LIME explanations produces the most logical results. The use of the Explainable AI model highlights the importance of choosing the ideal model while maintaining users’ trust in the deployed model.

Heavy Equipment Study Program, Jakarta State Polytechnic, has a warehouse that aims to store spare parts. These spare parts are used to meet the practical needs of students when lectures take place every semester. During this time, to lend and report spare parts, heavy equipment warehouse technicians still use forms and spare parts cards. This method is less effective when the stakeholders such as the Head of Workshop and Head of Study Program want to know the amount of spare parts stock that is still owned by the warehouse or data warehouse cannot be seen in real time. From these problems, the warehouse needs a way to speed up the process. One method that can be used to overcome this problem is to create a web-based system or application that can carry out an inventory, record, process, and report the spare parts data contained in the heavy equipment warehouse. This writing aims to design and build warehouse management applications for heavy equipment spare parts. So, with this web-based application, stake holders can view reports from the warehouse with the right target, accurate, and efficient.

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