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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 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 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.
Co-Authors Afkar Ayyasy Agil Dwi Saputra Agung Nilogiri Arifianto, Deni Azhar, Reza Fachruddin Camerino, Wellen Cetta Ugama Putra Devi Putri Prianggi Dimas Pradana Dody Wahyu Wirawan Dwi Putri Pramudiya Eko Wahyudi Fadholy, Rizal Agung Ginanjar Abdurrahman Ikrimatul Wilda Lorenza Ilanka Cahya Dewi Imam Rosadi Isnawati Lujeng Lestari Isnawati Lujeng Lestari Leny Septianingrum Liliek Susilowati Mike Yurike Dita Deviyati Moh Dasuki Moh. Dasuki Moh. Mahdi Sya’bani Mufarida, Nely Ana Mukti Sintawati Nurhalimah Nurhalimah Oktavianto, Hardian Prabhu, Savari Putranto, Bima Arya Qurrota A’yun Rahman, Miftahur Reza Chaidir 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