A Text-Based Analysis of SDGs Goal 4 Representation in Informatics and Computer Science Syllabi - Indah Rahma Ilmiana; Muhammad Kunta Biddinika; Herman (Department of Electronic and Informatic Engineering Education, Faculty of Engineering, UNY, 2025)
Higher education curricula play an important role in supporting the achievement of Sustainable Development Goals (SDGs), particularly SDGs Goal 4: Quality Education, through the development of relevant and sustainable learning. The purpose of this study is to look at the connections between the SDGs Goal 4 targets and the computer science and informatics curricula at universities on the island of Java. 552 course syllabi from current APTIKOM member universities are examined in this study using a text-based document analysis design. The analysis focuses on three frequently accessible syllabus components: course name, course description, and learning outcomes. The Term Frequency-Inverse Document Frequency (TF-IDF) approach was used to represent the textual data after it had been pre-processed and mapped using SDGs keywords to create initial labels. Support Vector Machine (SVM) was used as the comparison model and K-Nearest Neighbors (KNN) as the baseline model for the classification procedure. The results showed a strong relationship between most curricula and Target 4.4, which stresses the development of technical and digital skills. In terms of performance, the KNN model achieved an accuracy of 83%, while the SVM model showed a higher accuracy of 86% with more consistent performance on high-dimensional data and imbalanced class distributions. In conclusion, the syllabus analysis methodology based on keyword mapping and machine learning can be used as a systematic exploratory framework for identifying the relationship between higher education curriculum and SDGs.