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All Journal Media Statistika Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Jurnal Berkala Epidemiologi JUITA : Jurnal Informatika BAREKENG: Jurnal Ilmu Matematika dan Terapan JTAM (Jurnal Teori dan Aplikasi Matematika) MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Jurnal Berdaya Mandiri Computer Science and Information Technologies Jurnal Ilmiah Matematika International Journal of Engagement and Empowerment (IJE2) Bulletin of Applied Mathematics and Mathematics Education
Sugiyarto Surono, Sugiyarto
Department of Mathematics, Faculty of Mathematics and Science Ahmad Dahlan University, Yogyakarta
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Journal : JTAM (Jurnal Teori dan Aplikasi Matematika)

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Chi-Square Feature Selection with Pseudo-Labelling in Natural Language Processing Afriyani, Sintia; Surono, Sugiyarto; Solihin, Iwan Mahmud
JTAM (Jurnal Teori dan Aplikasi Matematika) Vol 8, No 3 (2024): July
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jtam.v8i3.22751

Abstract

This study aims to evaluate the effectiveness of the Chi-Square feature selection method in improving the classification accuracy of linear Support Vector Machine, K-Nearest Neighbors and Random Forest in natural language processing when combined with classification algorithms as well as introducing Pseudo-Labelling techniques to improve semi-supervised classification performance. This research is important in the context of NLP as accurate feature selection can significantly improve model performance by reducing data noise and focusing on the most relevant information, while Pseudo-Labelling techniques help maximise unlabelled data, which is particularly useful when labelled data is sparse. The research methodology involves collecting relevant datasets, thus applying the Chi-Square method to filter out significant features, and applying Pseudo-Labelling techniques to train semi-supervised models. In this study, the dataset used in this research is the text data of public comments related to the 2024 Presidential General Election, which is obtained from the Twitter scrapping process. The characteristics of this dataset include various comments and opinions from the public related to presidential candidates, including political views, support, and criticism of these candidates. The experimental results show a significant improvement in classification accuracy to 0.9200, with precision of 0.8893, recall of 0.9200, and F1-score of 0.8828. The integration of Pseudo-Labelling techniques prominently improves the performance of semi-supervised classification, suggesting that the combination of Chi-Square and Pseudo-Labelling methods can improve classification systems in various natural language processing applications. This opens up opportunities to develop more efficient methodologies in improving classification accuracy and effectiveness in natural language processing tasks, especially in the domains of linear Support Vector Machine, K-Nearest Neighbors and Random Forest well as semi-supervised learning.
Fuzzy Support Vector Machine Using Function Linear Membership and Exponential with Mahanalobis Distance Sukeiti, Wiwi Widia; Surono, Sugiyarto
JTAM (Jurnal Teori dan Aplikasi Matematika) Vol 6, No 2 (2022): April
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jtam.v6i2.6912

Abstract

Support vector machine (SVM) is one of effective biner classification technic with structural risk minimization (SRM) principle. SVM method is known as one of successful method in classification technic. But the real-life data problem lies in the occurrence of noise and outlier. Noise will create confusion for the SVM when the data is being processed. On this research, SVM is being developed by adding its fuzzy membership function to lessen the noise and outlier effect in data when trying to figure out the hyperplane solution. Distance calculation is also being considered while determining fuzzy value because it is a basic thing in determining the proximity between data elements, which in general is built depending on the distance between the point into the real class mass center. Fuzzy support vector machine (FSVM) uses Mahalanobis distances with the goal of finding the best hyperplane by separating data between defined classes. The data used will be going over trial for several dividing partition percentage transforming into training set and testing set. Although theoretically FSVM is able to overcome noise and outliers, the results show that the accuracy of FSVM, namely 0.017170689 and 0.018668421, is lower than the accuracy of the classical SVM method, which is 0.018838348. The existence of fuzzy membership function is extremely influential in deciding the best hyperplane. Based on that, determining the correct fuzzy membership is critical in FSVM problem.
Co-Authors Afriyani, Sintia Ahmad, Fandy Ambar Lukitaningsih Aris Thobirin, Aris Azizan, Ahamad Tarmizi Bidul, Winarsi J. Bilqisa, Nazla H. Dewi, Deshinta Arrova Dina Arumsari, Mela Eko Prasetyo Eliyanto, Joko Endang Darmawan Fahrian Hipmi, Ahmad Firdausy, Salma Nadya Hariadi Wijaya, Made Haryati, Anisa Eka Haryati, Annisa Eka Ibad., Irsyadul Ibraheem, Kais Ismail Irsalinda, Nursyiva Khang Wen, Goh khusnul khotimah Kurniawan, Tri Basuki Kusnul Khotimah Laely, Dera Kurnia Larasati, Anggit Lianti, Desi Maulidya, Thasya Alwa Muhamad Safiih Lola Mujhid, Almuzhidul Nur Rahmatiya Abas, Siti Pangestu, Raka Puguh Wahyu Prasetyo, Puguh Rahmadani, Lanova Ade Rismawati, Dewi Saidon, Hasnul Jamal Salsabila, Sona Regina Siti Mariah, Siti Solihin, Iwan Mahmud Subrata, Arsyad Cahya Sukeiti, Wiwi Widia Susanto, Moh. Rusnoto Sya'idah, Irma Binti Umi Mahmudah Wardani, Rahmania Ayu Wen, Goh Kang Widianti, Risa Wijayanti, Dian Eka Wiwit, Ilma Woro Supadmi Yudi Ari Adi, Yudi Ari