KOMPUTASI KLASIFIKASI PADA BERITA ONLINE BERBAHASA ARAB DENGAN PENDEKATAN SEMANTIK BERBASIS FRAME

Maulana Ihsan Ahmad, NIM.: 23201011009 (2024) KOMPUTASI KLASIFIKASI PADA BERITA ONLINE BERBAHASA ARAB DENGAN PENDEKATAN SEMANTIK BERBASIS FRAME. Masters thesis, UIN SUNAN KALIJAGA YOGYAKARTA.

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Abstract

This study focuses on the development of an automatic classification method for Arabic online news using a frame-based semantic approach. The background of this research is driven by the need to improve the efficiency and accuracy of Arabic news classification, as Arabic is known for its complex language structure and rich morphology. The main challenge in classifying Arabic news lies in understanding the deep linguistic context and properly organizing news into appropriate categories. This research aims to explore the application of frame semantics theory in the classification process of Arabic online news to enhance the comprehension of news meaning and automate the categorization of news articles. The research method involves using web scraping techniques to collect data from various Arabic news portals and employing machine learning algorithms, such as Support Vector Machine (SVM), supported by Term Frequency-Inverse Document Frequency (TF-IDF) vectorization for feature extraction. The data collected consists of 170,934 news articles from 11 different categories. The results show that the application of frame semantics improves both the efficiency and accuracy of news classification compared to conventional approaches. Frame semantics enables deeper meaning identification through the analysis of relationships between phrases and keywords in the news context. In conclusion, the integration of frame semantics in computational linguistics provides significant contributions to addressing the complexity of the Arabic language and enhancing the accuracy and efficiency of online news classification. This research has the potential to be applied in the development of digital news management systems across various Arabic-language platforms.

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