Design Automatic Parking Application of Amikom Purwokerto University

 Purpose: This study aims to deal with parking problems in the area of Amikom University, Purwokerto. In addition, this research is designed to implement theoretical and practical knowledge that has been obtained in lectures.Design/methodology/approach: In research on parking design applications in the Amikom University area, Purwokerto, library study methods and literature study methods are used. The amount of data can add insight and can make it easier to process data in research.Findings/result: This application will be able to help more Amikom Purwokerto University residents, especially in the Faculty of Computer Science. The use of this application will help find parking areas in FIK areas such as Basement Parking, Front Parking and Field Parking. In addition, security will be helped by this application because if it is implemented, vehicles parked in the reserved area will be tidier and safer. In addition, security does not need to find an empty parking area for users.Originality/value/state of the art: This research focuses on parking system design like previous studies. However, this research focuses more on designing parking applications at Amikom Purwokerto University.

Sentiment Analysis Regarding Candidate Presidential 2024 Using Support Vector Machine Backpropagation Based

This research has the potential to make an important contribution to the development of computationally-based sentiment analysis, particularly in the political context. Anies Baswedan, Ganjar Pranowo, and Prabowo Subianto, three candidates for the presidency of Indonesia, are examined using a Backpropagation-based Support Vector Machine (SVM) methodology in this study. This approach is used to categorize emotions into three groups: neutral, adverse, and favorable. Between July 1 and July 30, 2023, data on tweets mentioning the three presidential contenders was gathered. After processing the data, SVM was used while lowering the backpropagation process. The study's findings demonstrate that the performance of the model in determining public sentiment is greatly enhanced by the application of backpropagation-based SVM techniques. For each presidential contender, the evaluation was conducted using the f1 score, recall, and precision metrics. The evaluation's findings indicate that while the model struggles to distinguish between favorable and negative feelings toward particular presidential contenders, it performs better when categorizing neutral feelings. The SVM model is more accurately able to identify popular sentiment toward the three presidential candidates when the backpropagation approach is used. The results of the sentiment analysis are also represented by word clouds for each presidential contender, giving an intuitive sense of the words that are frequently used in public discourse. This study sheds light on the possibilities of using Twitter data to analyze political sentiment using the backpropagation-based SVM algorithm.