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Atmojo, Wahyu Tisno
Universitas Pradita
Religion Arts 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 Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Mechanical Engineering Nursing Public Health Social Sciences Transportation Other

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Journal : Indonesian Applied Research Computing and Informatics

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Improving Thesis Title Classification Accuracy Using Ensemble Classifier and Modified Chi-Square Feature Selection Method Ritzkal; Wahyu Tisno Atmojo; Panji Novantara; Sabir Rosidin; Ahmad Dedi Jubaedi; Enggar Novianto
Indonesian Applied Research Computing and Informatics Vol. 1 No. 1: July (2025)
Publisher : PT. Teras Digital Nusantara

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Abstract

Text classification of academic documents, particularly thesis titles, poses challenges due to high dimensionality, sparsity, and topic heterogeneity. Conventional feature selection techniques, such as the standard Chi-Square, often fall short in capturing discriminative features effectively. This research aims to enhance classification accuracy by proposing a Modified Chi-Square feature selection method that integrates term frequency and class distribution information. The selected features are then classified using ensemble decision tree algorithms, including Random Forest, Gradient Boosting, and XGBoost. Experiments were conducted on a labeled dataset of thesis titles using TF-IDF for vector representation. Evaluation metrics such as accuracy, precision, recall, F1-score, and AUC were used to assess model performance. The results showed that the combination of Modified Chi-Square and XGBoost outperformed other models, achieving the highest accuracy of 93.8% and an AUC of 0.94. These findings demonstrate that the integration of advanced feature selection and ensemble learning techniques can significantly improve academic text classification performance, providing valuable implications for the development of intelligent digital repositories and recommendation systems.
Co-Authors Aditya Bima Adrielvino, Nathanael Abel Agus Dendi Rachmatsyah Ahmad Dedi Jubaedi Alfa Yohannis Andi Guna Andi Guna Andreas Indra Wardana Andrew Lowell Aprikasari, Manda Aprikasari, Manda Arisantoso Arisantoso Ayunda, Afifah Trista Belsana Butar Butar Benedicta, Laurence Butar Butar, Belsana Caesar Gracia Chandra, Frans Kenny Christopher Christopher Cynthia, Dessy Nia Cynthia, Maulidania Mediawati Darmawan, Umar Dazki, Erick Deasy Olivia Deasy Olivia Dermawan, Steven Destriana, Rachmat Dewi Monica Didik Setiyadi Dodi Aprilianto Dwi Nisfatul Hijjah Edison Siregar Eka Pandu Cynthia, Eka Pandu Endang Retnoningsih Enggar Novianto Ericka Keisya Erika Kristina Ernando, Ryan Felix Felix Feri Prasetyo Feri Prasetyo Halim, Onky Ardhana Hasna Tania Yasmine Heriyando, Damien Hugo Hisyam, Ahmad Abdilah hizkia imanuel Jonathan Juan Antoni Samuel Posumah Kelly Kirsten Audrey Kirsten Audrey, Kelly Kurniawan, Ericka Kesya Masriah Masriah - Masriah Masriah masriah masriah, masriah Master Edison Master Edison Master Edison Siregar Mayatopani, Hendra Muhadi Hariyanto Muhamad Filman Ghaida Firdaus Muhamad Irvansyah Muhammad Haikal Nadda Akilka Ulima Nurhajanti Muljadi Nadda Akilka Ulima Nurhajanti Muljadi Natanael Iwan Santosa Nazwa Wanda Tazkiya Niklas, Hubert Nofry Thrusmida Ocktavia, Shabila Ocktavia, Shabila Oki Sumistriani Panduwitama, Aldira Panji Novantara Posumah, Juan Qonita Azizah Qonita Azizah Rachmatsyah, Agus Dendi Rahmawati, Lailia Richardo, Albert Rido Dwi Kurniawan Ritzkal, Ritzkal Rohmat Indra Borman Sabir Rosidin Shella Cintiya Stevan Amadeo Prasetyo Susiana Tarisa Mareti, Gusti Teh Yu Ka Untoro, Elianna Katherine Valencia, Shanty Waris Widekso Waris Widekso Wellson, Matthew George Zaenab Islami