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All Journal Jurnal Teknologi Informasi dan Ilmu Komputer Jurnal Biosains Pascasarjana Martabe : Jurnal Pengabdian Kepada Masyarakat Explore IT : Jurnal Keilmuan dan Aplikasi Teknik Informatika Jurnal Informatika dan Rekayasa Perangkat Lunak Jurnal Pengabdian Masyarakat IPTEKS JUSTINDO (Jurnal Sistem dan Teknologi Informasi Indonesia) Discovery : Jurnal Ilmu Pengetahuan Jurnal Cafetaria Journal of Community Development Journal of Computer Science and Informatics Engineering Jurnal Ilmu Komputer The Journal of System Engineering and Technological Innovation Smart Techno (Smart Technology, Informatic and Technopreneurship) Jurnal Aplikasi Sistem Informasi dan Elektronika Modem : Jurnal Informatika dan Sains Teknologi ABDIMASTEK JUSTINDO (Jurnal Sistem dan Teknologi Informasi Indonesia) Computing and Information System Journal
Arifianto, Deni
Universitas Muhammadiyah Jember
Religion Agriculture, Biological Sciences & Forestry Arts Humanities Biochemistry, Genetics & Molecular Biology Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Health Professions Immunology & microbiology Industrial & Manufacturing Engineering Mathematics Mechanical Engineering Nursing Public Health Social Sciences Other

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Journal : computing and information system journal

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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 Abadi, Taufan Abd. Rohman Fahruddin Abdullah, M. Hasan Achmad David Mico Adi Cahyanto, Triawan Adi Sulistyono Agil Dwi Saputra Agung Nilogiri Ahmad Amrul Muyassir Ahmad Haris aji brahma nugroho, aji brahma Aji, Alif Syadillah Amaliah, Ulfi Rizqi Andani Chacha Cahya Dewi Astria Hindratmo Ayu, Wanda Afrilia A`yun, Qurrota Dandy Tri Prasetyo Dasuki, Moh. Dasuki, Mohammad Dhea Intan Septiara Eko Wahyudi Elok Rahmawati Erna Cholida, Ferdy Maulana Fadila Oktaviana, Kadek Fariz, Muhammad Ivan Gatot Susanto haris, achmad Hartono, Ahmad Dedi Himawan Ganjar Prabowo Khailla Savana, Bella Risma Khusnul Ain Miftah Chatibul Umam Moh Dasuki, Moh Dasuki Muchtar, Nabillah Ufairoh Muharom, Lutfi Ali Murwanti, Retno Nanang Saiful Rizal Nanda Kurnia Wardati Nitya S, Putu Nirvanda Octavia, Chendrasari Wahyu Onny Purnamayudhia Pangestu, Dwi Saka Pater, Dewi Lusiana Pradana Aryanto, Risqi Pradina K, Renny Qurrota A'yun Qurrota A`yun Qurrota A’yun Rahayu, Yeni Dwi Ramadhayanti Saputro, Safitri Renny Pradina K Rivansyah, Muhammad Saifudin, Ilham Saputro, Safitri Ramadhayanti Sari, Wina Ayunda setiyandi, aditiya Sinta Bella Criska Suharso, Wiwik Sulistyo, Henny Wahyu Suryani Dyah Astuti Susanto, Tony Dwi Suwondo, Ampar Jaya Tony Dwi Susanto Trias Setyowati Triawan Adi Cahyanto Umam, Miftah Chatibul Umilasari, Reni Wahju Dyah Laksmi Wardhani Wahyuni Adriani, Sri Warisaji, Taufik Timur Wiwik Suharso Yaqubi, Ahmad Khalil Yeni Dwi Rahayu Yonatan Yuliasih Kripsiandita Zakiyyah, Amalina Maryam Zakiyyah, Amalina Maryam