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All Journal CAUCHY: Jurnal Matematika Murni dan Aplikasi Jurnal Fourier AdMathEduSt: Jurnal Ilmiah Mahasiswa Pendidikan Matematika Jurnal ELTIKOM : Jurnal Teknik Elektro, Teknologi Informasi dan Komputer INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi JITK (Jurnal Ilmu Pengetahuan dan Komputer) Computer Science and Information Technologies
Aris Thobirin
Universitas Ahmad Dahlan
Aerospace Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Mathematics

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Enhancing Multi-Class Classification of Non-Functional Requirements Using a BERT-DBN Hybrid Model Badzliana Aqmar Suris; Aris Thobirin; Sugiyarto Surono; Mohamed Naeem Antharathara Abdulnazar
INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi Vol 9 No 2 (2025): August 2025
Publisher : Universitas Nusantara PGRI Kediri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29407/intensif.v9i2.24637

Abstract

Background: Software requirements classification is essential to group Non-Functional Requirements (NFR) into several aspects, such as security, usability, performance, and operability. The main challenges in NFR classification are data limitations, text complexity, and high generalization needs. Objective: This research seeks to create a classification model using a hybrid of BERT and DBN, optimize hyperparameters, and improve data representation. Methods: A BERT and DBN-based approach is used, where DBN enhances BERT's ability to extract hierarchical features. Bayesian Optimization determines the optimal hyperparameters and data augmentation is applied to enrich the dataset variation. The model is tested on the PROMISE dataset consisting of 625 data. Results: The BERT-DBN model achieves 95% accuracy on the baseline configuration and 94% on the extensive configuration, better than the previous model, BERT-CNN. The model shows stability without any indication of overfitting. Conclusion: The combination of data augmentation, hyperparameter optimization, and DBN's ability to capture hierarchical patterns improves the accuracy of NFR classification, making it more effective than existing methods, and is expected to enhance text-based classification for software requirements.
Co-Authors Adi Setya Priambudi Age Samanta Putra Anindita Lusiana Anis Ni'matus Sholihah Annisa Mutiara Sukma Annisa Nurohmawati Aprilian Dwi Putranto Ardina Dwi Ervinawati Attarik Mohammad Aulya Rachma Dewi Badzliana Aqmar Suris Cindy Ainun Majid Dody Okkosila Guntur Prabowo Hanifah Hanifah Istiqomah Istiqomah Khafid Baihaqi Iskak Khusnul Khotimah Kurnia Prantauwati Luthfie Ghany Yahya Moh Rizki Yuniardi Basuni Mohamed Naeem Antharathara Abdulnazar Muhammad Fahrul Styawan Muhammad Raezia Nano Gunawan Nely Ulfatullatifah Nova Risky Amalia Novitasari Hariyanti Nurul Amalina Kurniasih Puguh Wahyu Prasetyo, Puguh Sherlyn Vironica Shoffi Arba Sari Sonya Madyo Ratri Sugiyarto Surono Tina Puji Rahayu Wijayanti, Dian Eka