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All Journal Jurnal SOLMA JOURNAL OF APPLIED INFORMATICS AND COMPUTING INTI Nusa Mandiri Amalee: Indonesian Journal of Community Research & Engagement International Journal of Engineering, Science and Information Technology Jurnal Teknologi Sistem Informasi dan Sistem Komputer TGD Jurnal Teras Pengabdian Masyarakat Scientific: Journal of Computer Science and Informatics
Afril Efan Pajri
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Humanities Astronomy Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Public Health Social Sciences Transportation Other

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Journal : JOURNAL OF APPLIED INFORMATICS AND COMPUTING

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Sentiment Analysis of President Prabowo's Performance on Twitter (X) with a Comparative Study of SVM, XGBoost, and AdaBoost Maruf, Anang; Pajri, Afril Efan; Liana, Putri
Journal of Applied Informatics and Computing Vol. 10 No. 1 (2026): February 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i1.12138

Abstract

This study was conducted to understand how Twitter (X) users respond to President Prabowo's performance through machine learning-based sentiment analysis. Data was collected using a dataset crawling approach, then processed through a series of pre-processing stages such as cleansing, case folding, tokenisation, stopword removal, and stemming before being converted into a numerical representation with TF-IDF. The class imbalance problem was addressed by applying SMOTE so that the model could learn more evenly. Three classification algorithms, SVM, XGBoost, and AdaBoost, were tested with the help of GridSearchCV to obtain the best parameter configuration. The research evaluation showed that the XGBoost algorithm was able to provide the best performance with an accuracy of 0.8443, followed by the SVM algorithm with an RBF kernel, which achieved an accuracy of 0.8135. The AdaBoost algorithm came in third with an accuracy of 0.7868. These findings indicate that the boosting approach, especially XGBoost, is better able to handle complex language patterns and high-dimensional text data characteristics. Overall, this study provides an overview of public opinion trends on social media and can be used as a reference for the development of sentiment analysis models in future research.
Classification Of Student Depression Using Support Vector Machine Modelling and Backward Elimination Sabar, Rohmat Abidin; Pajri, Afril Efan; Budiani, Jauhara Rana
Journal of Applied Informatics and Computing Vol. 10 No. 1 (2026): February 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i1.12203

Abstract

Depression among university students has become a serious mental health concern that can negatively affect academic performance and overall well-being. Early detection of Depression is essential to provide timely support and preventive interventions. This study proposes a machine learning approach to classify student Depression using a Support Vector Machine (SVM) combined with Backward Elimination (BE) for feature selection. The dataset used in this research was obtained from a public repository and consists of 502 student records with multiple psychological and demographic attributes. Data preprocessing included categorical encoding and Min–Max normalization, followed by an 80:20 split for training and testing. Experimental results show that the baseline SVM model achieved an accuracy of 0.9208, while the application of Backward Elimination improved the performance to 0.9604. In addition, precision, recall, and F1-score also showed notable improvements, indicating a reduction in misclassification, particularly for non-depressed students. These findings demonstrate that integrating feature selection with SVM can enhance classification performance and provide a more efficient model for supporting early Depression detection among university students.
Co-Authors Arum , Arum Fatmawati Eka Fadlilatus Saadah Fatichatur Rochmaniyah Fikram, Muhammad Ita Aristia Sa'ida Jauhara Rana Budiani, Jauhara Rana Liana, Putri M. Iqbal Tawakkal Maruf, Anang Muhammad Sholihin Sabar, Rohmat Abidin Sahri Sahri Sahri Siti Shoimah Suci Mulyani Sutriawan Sutriawan Taufik Azhary Tawakkal, M. Iqbal