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All Journal CAUCHY: Jurnal Matematika Murni dan Aplikasi JMM (Jurnal Masyarakat Mandiri) JTAM (Jurnal Teori dan Aplikasi Matematika) Jurnal Masyarakat Merdeka (JMM) Indonesian Journal of Combinatorics Jurnal Pengabdian Masyarakat IPTEKS JUSTINDO (Jurnal Sistem dan Teknologi Informasi Indonesia) Kontribusia : Research Dissemination for Community Development Community Development Journal: Jurnal Pengabdian Masyarakat Discovery : Jurnal Ilmu Pengetahuan Journal of Mathematics Education and Science Journal of Community Development BIOS : Jurnal Teknologi Informasi dan Rekayasa Komputer Journal of Social Outreach AL-UMRON: Jurnal Pengabdian kepada Masyarakat International Journal of Trends in Mathematics Education Research (IJTMER) Jurnal Aplikasi Sistem Informasi dan Elektronika Jurnal Pengabdian Indonesia CEJou Jurnal Pengabdian Masyarakat Manage ABDIMASTEK Computing and Information System Journal Teknologi : Jurnal Ilmiah Sistem Informasi
Umilasari, Reni
Universitas Muhammadiyah Jember
Religion Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Mathematics Mechanical Engineering Nursing Public Health Social Sciences Other

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Journal : Computing and Information System Journal

 1 
Effect Of Random Under Sampling and Random Over Sampling Method On Svm Performance Agil Dwi Saputra; Deni Arifianto; Reni Umilasari
Computing and Information System Journal Vol. 1 No. 2 (2025): Integration of Automation and Information Systems in Enhancing Organizational C
Publisher : IndoCompt Publisher

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Abstract

Imbalanced data is a common challenge in sentiment analysis, as it can cause the classification model to be biased towards the majority class and ignore important information from the minority class. This study aims to evaluate the effect of resampling methods, namely Random Under Sampling (RUS), and Random Over Sampling (ROS), on the performance of the Support Vector Machine (SVM) algorithm in handling imbalanced sentiment data. Data were collected from social media X (Twitter) with the topic of naturalization of soccer players in Indonesia. The research process includes preprocessing, TF-IDF weighting, and model testing using K-Fold Cross Validation with K = 2, 5, and 10. Evaluation was carried out based on the F1-score matrix, recall, precision, and accuracy. The results show that the ROS method provides the best performance, especially at K = 10 with an F1-score value of 0.80, recall 0.78, precision 0.84, and accuracy 0.85. and RUS shows a lower performance improvement. These results show that selecting an appropriate resampling method can improve the performance of the classification model when faced with imbalanced data.
THE EFFECT OF IMPERFECTIVE DATA SAMPLING METHOD ON SUPPORT VECTOR MACHINE ACCURACY Erna Cholida, Ferdy Maulana; Arifianto, Deni; Umilasari, Reni
Computing and Information System Journal Vol. 1 No. 3 (2025): Data Science, UI/UX, and E-Government for Decision Making
Publisher : IndoCompt Publisher

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Abstract

Sentiment analysis is used to understand the direction of public opinion, but problems arise due to the unbalanced distribution of sentiment data, where one class dominates. This imbalance causes classification models such as Support Vector Machine (SVM) to be biased towards the majority class, which results in decreased accuracy and generalizability of the model. This study aims to assess the effectiveness of two data balancing techniques, namely, SVM-SMOTE, and ADASYN, in improving SVM performance. The research data was taken from social media platform X (Twitter), and testing was conducted using the K-Fold Cross Validation method (K=2, 5, and 10) using evaluation metrics such as accuracy, precision, recall, and F1-score. The results show that without data balancing, the SVM model can only achieve an average accuracy of 76.34% and F1-score of 62.38%, which reflects the weakness in recognizing minority classes. The application of the two balancing methods successfully improved the model performance. ADASYN increased the F1-score to 67.94%, while SVM-SMOTE showed the most optimal results with 82.4% accuracy and 74.02% F1-score. These findings indicate that SVM-SMOTE is the most effective technique in handling data imbalance and improving sentiment classification accuracy equally.
Co-Authors Afkar Ayyasy Agil Dwi Saputra Agung Nilogiri Arifianto, Deni Aurelia Agustin Sans Azhar, Reza Fachruddin Camerino, Wellen Cetta Ugama Putra Chaidir, Reza Daffa, Clarence Daffa Rizqullah Dasuki, Moh. Deviyati, Mike Yurike Dita Dimas Pradana Dody Wahyu Wirawan Dwi Putri Pramudiya Eko Wahyudi Erna Cholida, Ferdy Maulana Fadholy, Rizal Agung Ginanjar Abdurrahman Ikrimatul Wilda Lorenza Ilanka Cahya Dewi Imam Rosadi Irawan, Dudi Isnawati Lujeng Lestari Leny Septianingrum Lestari, Isnawati Lujeng Liliek Susilowati Moh Dasuki Moh. Mahdi Sya’bani Muchtar, Nabillah Ufairoh Mufarida, Nely Ana Mukti Sintawati Nanang Saiful Rizal Nurhalimah Nurhalimah Oktavianto, Hardian Prabhu, Savari Prianggi, Devi Putri Putranto, Bima Arya Qurrota A’yun Rahman, Miftahur Rahman, MIftahur Reza Fachruddin Azhar S Slamin Sahiruddin Sahiruddin Saifudin, Ilham Savana, Bella Risma Khailla Septiara, Dhea Intan Sya?bani, Moh. Mahdi Teguh Mutawaffi H.F. Triawan Adi Cahyanto Wibisono, Yusub Winahyu, Pawestri Zakiyah, Amalina Maryam Zakiyyah, Amalina Maryam Zakiyyah, Amalina Maryam Zakiyyah, Amalina Masyam