Abstract

Sistem informasi akademik, khususnya pada modul Kartu Rencana Studi (KRS), kerap mengalami lonjakan trafik ekstrem di awal semester. Arsitektur konvensional yang sepenuhnya bergantung pada basis data relasional berpotensi mengalami masalah latensi akibat antrean input/output (I/O) disk pada kondisi konkurensi tinggi. Penelitian ini merancang dan mengimplementasikan prototipe arsitektur Cache-Aside menggunakan Redis sebagai cache layer pada sistem backend berbasis Python (Flask). Untuk mengevaluasi perilaku arsitektur, dibangun model simulasi di mana latensi akses basis data (500 ms) dan latensi akses cache (5 ms) ditetapkan berdasarkan karakteristik tipikal yang dilaporkan dalam literatur. Pengujian beban dilakukan menggunakan Locust dengan simulasi 1.000 pengguna konkuren dan spawn rate 100 pengguna per detik. Hasil simulasi menunjukkan bahwa arsitektur Cache-Aside mampu menurunkan waktu respons rata-rata dari 507,52 ms (tanpa cache) menjadi 9,12 ms (dengan cache), meningkatkan throughput dari 385,71 menjadi 483,03 request per detik, serta mempertahankan zero failure rate pada kedua skenario. Distribusi persentil menunjukkan konsistensi performa: p95 turun dari 520 ms menjadi 13 ms. Hasil ini mengonfirmasi bahwa pola Cache-Aside secara arsitektural efektif dalam mengalihkan beban kerja dari penyimpanan berbasis disk ke memori pada skenario lonjakan trafik akademik. Validasi lebih lanjut dengan infrastruktur MySQL dan Redis sesungguhnya diperlukan untuk mengonfirmasi parameter simulasi.

Keywords: cache-aside, Redis, sistem informasi akademik, simulasi beban, Locust, KRS

Citation

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Links

• Publisher Page: https://jurnal.polinema.ac.id/index.php/jip/article/view/9675
• Google Scholar: Kunjungi

Abstract

Direct Memory Access (DMA) plays a crucial role in reducing CPU load in high-speed data transfer systems. This literature review examines how DMA enables direct data transfer between memory and I/O devices without CPU intervention, thereby improving overall system efficiency. The primary issue identified across the literature is the occurrence of CPU bottlenecks in conventional transfer protocols such as Programmed I/O and Interrupt-driven I/O, which cause performance degradation in applications such as High-Performance Computing (HPC) and the Internet of Things (IoT). Through analysis of literature from various scientific sources, including IEEE and ACM journals, this review finds that DMA implementation has been reported to reduce CPU load by up to 80% in gigabit-speed transfer scenarios and significantly decrease processor time compared to conventional methods. These findings underscore the importance of DMA integration in the design of future computing systems, with recommendations for further research on hybrid architectures and DMA implementation in heterogeneous computing.

Keywords: Direct Memory Access, DMA, transfer data, CPU load, computer architecture

Citation

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Links

• Publisher Page: https://journal.futuresciencepress.com/index.php/intens/article/view/70
• Google Scholar: Kunjungi

Abstract

CentOS merupakan distribusi Linux berbasis RHEL yang dikenal karena kestabilan dan keamanannya di lingkungan server. Penelitian ini menggunakan metode studi literatur untuk menganalisis perkembangan, kelebihan, kekurangan, serta potensi pengembangan CentOS. Hasil kajian menunjukkan bahwa CentOS unggul dalam kestabilan sistem, efisiensi sumber daya, dan keamanan kernel, meskipun kurang fleksibel untuk integrasi cloud dan memerlukan konfigurasi manual. Dibandingkan Ubuntu, CentOS lebih stabil dan cocok untuk server enterprise, sedangkan Ubuntu unggul dalam kemudahan penggunaan. Potensi pengembangan CentOS mencakup penerapan pada e-Government, pendidikan, cloud computing, dan sistem monitoring otomatis, menjadikannya fondasi penting dalam transformasi digital modern.

Keywords: linux, centos, open source, keamanan, cloud computing

Citation

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F. S. Zakaria, M. F. S., and G. M. R., “Infrastruktur Jaringan Menggunakan Server Web Hosting CentOS 6 Sebagai Server Aplikasi Monitoring Perkebunan,” Prosiding Industrial Research Workshop and National Seminar, vol. 11, no. 1, pp. 78–82, Sept. 2020, doi: 10.35313/irwns.v11i1.1971.

E. D. Sitanggang, “Analisis Preboot Execution Environment Server Linux dengan Algoritma First Come First Serve,” LOFIAN: Jurnal Teknologi Informasi dan Komunikasi, vol. 1, no. 1, pp. 12–16, Sept. 2021, doi: 10.58918/lofian.v1i1.159.

A. P. Lalengke and I. Nurhaida, “Performance Analysis of CloudLinux-based Web Server at the Embassy of the Kingdom of Morocco in Jakarta,” SISFOKOM, vol. 10, pp. 250–258, 2021, doi: 10.32736/sisfokom.v10i2.1168.

I. Mayendra, H. Saputra, and U. Hasanah, “Rancang Bangun Local Cloud Server dengan NextCloud pada CentOS 7 di SRH Training Center,” JUTSI: Jurnal Teknologi dan Sistem Informasi, vol. 1, no. 1, pp. 39–44, Feb. 2021, doi: 10.33330/jutsi.v1i1.1045.

G. Singh, “Assessing the Vulnerabilities and Impacts of Open SSH Ports on CentOS 9 Virtual Machines Hosted on Mac ARM Computers CVE-2023-25136,” 2024, doi: 10.13140/RG.2.2.23838.14403.

Y. Mulyanto, E. S. Susanto, and Z. M. Putra, “Analisis Perbandingan Cloud Server Menggunakan CentOS 7 dan Ubuntu Server 22.04 Menggunakan Quality of Service,” Digital Transformation Technology, vol. 4, no. 1, pp. 445–451, 2024, doi: 10.47709/digitech.v4i1.4056.

S. Maolia and Rahma Eka Aprilliana, “Literature Review Jurnal Memahami Faktor-Faktor yang Mempengaruhi Popularitas Windows Dibandingkan Linux,” Jurnal Mahasiswa Teknik Informatika, vol. 3, no. 1, pp. 96–101, Apr. 2024, doi: 10.35473/jamastika.v3i1.2791.

A. Arini, A. Fiade, and R. Baharsyah, “Perbandingan Load Balancing Router MySQL dan HAProxy Menggunakan SysBench dan Cluster InnoDB pada Sistem Operasi CentOS,” Cyber Security dan Forensik Digital, vol. 8, no. 1, pp. 17–24, 2025, doi: 10.14421/csecurity.2025.8.1.5004.

Muhammad Anis Al Hilmi, Fauziah Herdiyanti, Renol Burjulius, and Sonty Lena, “Pengujian Keamanan Sistem Operasi Linux Studi Kasus: Celah Keamanan FTP pada Metasploitable2,” IKRA-ITH Informatika: Jurnal Komputer dan Informatika, vol. 8, no. 1, pp. 110–115, 2024, doi: 10.37817/ikraith-informatika.v8i1.3205.

I. K. S. Satwika and K. N. Semadi, “Perbandingan Performansi Web Server Apache dan Nginx dengan Menggunakan IPv6,” SCAN: Jurnal Teknologi Informasi dan Komunikasi, vol. 15, no. 1, pp. 10–15, Feb. 2020, doi: 10.33005/scan.v15i1.1847.

M. Lestandy, M. A. Hidayatullah, A. Faruq, M. Effendy, and M. Irfan, “Replication of Comparative Methods for Single Database Performance in CentOS Operation System with MaxScale,” Cyberspace: Jurnal Pendidikan Teknologi Informasi, vol. 4, no. 2, p. 74, 2020, doi: 10.22373/cj.v4i2.7753.

I. P. A. E. Pratama and P. B. S. W. Putra, “Pengujian IaC Berbasis DevOps dan Ansible Menggunakan Metode Black Box Testing,” Faktor Exacta, vol. 15, no. 2, pp. 84–91, 2022, doi: 10.30998/faktorexacta.v15i2.12039.

D. Hisyam, M. Fathinuddin, and U. Y. K. S. Hediyanto, Operating System Selection for Information System Practicums: A Comparative Study of Rocky Linux, CentOS Stream, and Ubuntu Using the Analytical Hierarchy Process. Universitas Telkom, 2025. Accessed: Dec. 8, 2025. [Online].

A. Wicitra, D. Utomo, and H. K. Wardana, “Membangun Infrastruktur Komputasi Awan Privat Single Cluster dan Multi Cluster dengan Menggunakan Linux CentOS,” Techné: Jurnal Ilmiah Elektro Teknik, vol. 13, no. 2, pp. 185–194, Oct. 2014.

D. Surya Rahmadani, T. Ariyadi, E. Novitasari, D. Pertiwiningsih, and K. Kunci, “Analisis Kinerja Web Server Berbasis Linux Ubuntu 22.04,” Jurnal Tera, vol. 5, no. 1, pp. 35–44, 2025.

M. Khaerudin, A. A. Hendharsetiawan, and T. Sumitra, “Perbandingan Optimasi Penggunaan Memory Untuk Aplikasi Desain Grafis pada Sistem Operasi Windows dan Linux,” Jurnal Sistem Informasi Universitas Suryadarma, vol. 12, no. 1, pp. 43–47, 2014, doi: 10.35968/jsi.v12i1.1326.

H. Saragih and B. W. Yudanto, “Sistem Informasi Lembaga Pemerintahan di Indonesia Melalui Electronic Government (E-Government) dengan Open Source Software,” Jurnal Teknik dan Ilmu Komputer, 2013.

F. R. Permana, “Analisis Implementasi Network Automation pada Sistem Operasi Server Menggunakan Python Berbasis Web,” D4, Politeknik Negeri Jakarta, 2024.

R. Fadhlurrahman, M. C. Saputra, and A. D. Herlambang, “Evaluasi Penerapan E-Government di Pemerintah Kota Batu Menggunakan Kerangka Kerja Pemeringkatan E-Government Indonesia (PeGI),” Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer, vol. 2, no. 12, pp. 5977–5982, Aug. 2018.

E. B. Harjono, “Analisa dan Implementasi dalam Membangun Sistem Operasi Linux Menggunakan Metode LSF dan Remaster,” Sinkron: Jurnal dan Penelitian Teknik Informatika, vol. 1, no. 1, Apr. 2017.

A. Azhar, H. Hendrawaty, M. Mulyadi, and C. Yusnar, “Workshop Administrasi Jaringan Komputer Berbasis Linux bagi Siswa SMK Negeri 1 Tanah Luas Kabupaten Aceh Utara,” Prosiding Seminar Nasional Politeknik Negeri Lhokseumawe, vol. 3, no. 1, p. 185, 2019.

T. Alida, “Implementasi Sistem Keamanan Email Berbasiskan Open Source Studi Kasus PPIN-BATAN,” Bachelor Thesis, UIN Syarif Hidayatullah Jakarta, 2023.

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Links

• Publisher Page: https://jifosi.upnjatim.ac.id/index.php/jifosi/article/view/558
• Google Scholar: https://scholar.google.com/scholar?cluster=15704843006237176776

Abstract

This study uses the Convolutional Neural Network (CNN) method with the AlexNet model to classify eye diseases based on medical images. The dataset includes labeled images of three types of eye diseases: cataract, glaucoma, and diabetic retinopathy. The experimental results show that the model achieved an accuracy of 75.18%, which indicates that CNN with the AlexNet architecture can classify eye diseases quite well. This research shows that deep learning can be used to help doctors or health professionals in diagnosing eye diseases through automatic image analysis. Although the accuracy still needs to be improved, this study can serve as a reference for developing an automated diagnostic system in the future. Further research is expected to increase accuracy, expand the dataset, and apply other deep learning techniques to improve the performance of eye disease detection.

Keywords: Classification of Eye Diseases, Convolutional Neural Network, Alexnet, Deep Learning

Citation

M. Ptito, M. Bleau, and J. Bouskila, “The retina: a window into the brain,” Cells, vol. 10, no. 12, MDPI, p. 3269, 2021.

K. Kansal and H. Khan, “Environmental factors and eye health: Protecting your vision in a changing world,” Int. J. Ophthalmol. Optom., vol. 5, pp. 33–35, 2023.

G. Arslan and Ç. B. Erdaş, “Detection of cataract, diabetic retinopathy and glaucoma eye diseases with deep learning approach,” Intell. Methods Eng. Sci., vol. 2, no. 2, pp. 42–47, 2023.

D. C. R. Novitasari, P. Wulandari, and D. Z. Haq, “Cervical Cancer Diagnosis System using Convolutional Neural Network ResidualNet,” Int. J. Comput., vol. 21, no. 1 SE-, pp. 61–68, Mar. 2022, doi: 10.47839/ijc.21.1.2518.

J. Sanghavi and M. Kurhekar, “Ocular disease detection systems based on fundus images: a survey,” Multimed. Tools Appl., vol. 83, no. 7, pp. 21471–21496, 2024.

D. Marcella, Y. Yohannes, and S. Devella, “Klasifikasi penyakit mata menggunakan Convolutional Neural Network dengan arsitektur VGG-19,” J. Algoritm., vol. 3, no. 1, pp. 60–70, 2022.

A. E. Suwanda and D. Juniati, “Klasifikasi Penyakit Mata Berdasarkan Citra Fundus Retina Menggunakan Dimensi Fraktal Box Counting Dan Fuzzy K-Means,” 2022.

J. Qiu, X. Lu, X. Wang, C. Chen, Y. Chen, and Y. Yang, “Research on image recognition of tomato leaf diseases based on improved AlexNet model,” Heliyon, vol. 10, no. 13, 2024.

G. V. Doddi, “Eye Disease Retinal Images,” Kaggle, 2022.
https://www.kaggle.com/datasets/gunavenkatdoddi/eye-diseases-classification

H. Eldem, E. Ülker, and O. Y. Işıklı, “AlexNet architecture variations with transfer learning for classification of wound images,” Eng. Sci. Technol. an Int. J., vol. 45, p. 101490, 2023.

T. Sivakumari and R. Vani, “Implementation of alexnet for classification of knee osteoarthritis,” in 2022 7th International Conference on Communication and Electronics Systems (ICCES), 2022, pp. 1405–1409.

S. Kavitha, B. Dhanapriya, G. N. Vignesh, and K. R. Baskaran, “Neural style transfer using vgg19 and alexnet,” in 2021 International Conference on Advancements in Electrical, Electronics, Communication, Computing and Automation (ICAECA), 2021, pp. 1–6.

G. Naidu, T. Zuva, and E. M. Sibanda, “A review of evaluation metrics in machine learning algorithms,” in Computer science on-line conference, 2023, pp. 15–25.

S. Sathyanarayanan and B. R. Tantri, “Confusion matrix-based performance evaluation metrics,” African J. Biomed. Res., vol. 27, no. 4S, pp. 4023–4031, 2024.

D. Z. Haq and C. Fatichah, “Ultrasound Image Synthetic Generating Using Deep Convolution Generative Adversarial Network For Breast Cancer Identification,” IPTEK J. Sci. Technol., vol. 34, no. 1, 2023, doi: http://dx.doi.org/10.12962/j20882033.v34i1.14968.

Links

• Publisher Page: https://ijconsist.org/index.php/ijconsist/article/view/160
• Google Scholar: https://scholar.google.com/scholar?cluster=9341093393877158497

Abstract

Domain Name System (DNS) is an essential component in internet infrastructure to direct domains to IP addresses or conversely. Despite its important role in delivering internet services, attackers often use DNS as a bridge to breach a system. A DNS traffic analysis system is needed for early detection of attacks. However, the available security tools still have many shortcomings, for example broken authentication, sensitive data exposure, injection, etc. This research uses DNS analysis to develop anomaly-based techniques to detect malicious traffic on the DNS infrastructure. To do this, We look for network features that characterize DNS traffic. Features obtained will then be processed using the Decision Tree algorithm to classifyincoming DNS traffic. We experimented with 2.291.024 data traffic data matches the characteristics of BotNet and normal traffic. By dividing the data into 80% training and 20% testing data, our experimental results showed high detection aacuracy (96.36%) indicating the robustness of our method.

Keywords: DNS traffic analysis, machine learning, decision tree

Citation

L. Watkins et al., “Using semi-supervised machine learning to address the Big Data problem in DNS networks,” 2017 IEEE 7th Annu. Comput. Commun. Work. Conf. CCWC 2017, no. January, 2017, doi: 10.1109/CCWC.2017.7868376. 

S. S. C. Silva, R. M. P. Silva, R. C. G. Pinto, and R. M. Salles, “Botnets: A survey,” Comput. Networks, vol. 57, no. 2, pp. 378–403, 2013, doi: 10.1016/j.comnet.2012.07.021. 

X. Li, J. Wang, and X. Zhang, “Botnet detection technology based on DNS,” Futur. Internet, vol. 9, no. 4, pp. 1–12, 2017, doi: 10.3390/fi9040055. 

S. Miller and C. Busby-Earle, “The role of machine learning in botnet detection,” 2016 11th Int. Conf. Internet Technol. Secur. Trans. ICITST 2016, pp. 359–364, 2017, doi: 10.1109/ICITST.2016.7856730. 

X. Dong, J. Hu, and Y. Cui, “Overview of botnet detection based on machine learning,” 2018, doi: 10.1109/ICMCCE.2018.00106. 

A. Feizollah, N. B. Anuar, R. Salleh, F. Amalina, R. R. Ma’arof, and S. Shamshirband, “A study of machine learning classifiers for anomaly-based mobile botnet detection,” Malaysian J. Comput. Sci., vol. 26, no. 4, pp. 251–265, 2013. 

M. Singh, M. Singh, and S. Kaur, “Issues and challenges in DNS based botnet detection: A survey,” Comput. Secur., vol. 86, pp. 28–52, 2019, doi: 10.1016/j.cose.2019.05.019. 

M. Stevanovic and J. M. Pedersen, “An analysis of network traffic classification for botnet detection,” in 2015 International Conference on Cyber Situational Awareness, Data Analytics and Assessment (CyberSA), 2015, pp. 1–8. 

M. Stevanovic, J. M. Pedersen, A. D’Alconzo, S. Ruehrup, and A. Berger, “On the ground truth problem of malicious DNS traffic analysis,” Comput. Secur., vol. 55, pp. 142–158, 2015. 

H. R. Zeidanloo, A. B. Manaf, P. Vahdani, F. Tabatabaei, and M. Zamani, “Botnet detection based on traffic monitoring,” in ICNIT 2010 – 2010 International Conference on Networking and Information Technology, 2010, pp. 97–101, doi: 10.1109/ICNIT.2010.5508552. 

S. Y. Yerima and M. K. Alzaylaee, “Mobile Botnet Detection: A Deep Learning Approach Using Convolutional Neural Networks,” arXiv. 2020. 

J. Wu, “Artificial Neural Network Based DGA Botnet Detection,” 2020, doi: 10.1088/1742-6596/1578/1/012074. 

E. B. Beigi, H. H. Jazi, N. Stakhanova, and A. A. Ghorbani, “Towards effective feature selection in machine learning-based botnet detection approaches,” 2014, doi: 10.1109/CNS.2014.6997492. 

S. Saad et al., “Detecting P2P botnets through network behavior analysis and machine learning,” 2011, doi: 10.1109/PST.2011.5971980. 

Y. M. Mahardhika, A. Sudarsono, and A. R. Barakbah, “An implementation of Botnet dataset to predict accuracy based on network flow model,” 2017, doi: 10.1109/KCIC.2017.8228455. 

J. Pang, R. De Prisco, J. Hendricks, B. Maggs, A. Akella, and S. Seshan, “Availability, usage, and deployment characteristics of the domain name system,” Proc. 2004 ACM SIGCOMM Internet Meas. Conf. IMC 2004, no. January, pp. 1–14, 2004, doi: 10.1145/1028788.1028790. 

A. Alenazi, I. Traore, K. Ganame, and I. Woungang, “Holistic Model for HTTP Botnet Detection Based on DNS Traffic Analysis,” 2017, doi: 10.1007/978-3-319-69155-8_1. 

M. Abedini et al., “A generalized framework for medical image classification and recognition,” IBM J. Res. Dev., vol. 59, no. 2/3, p. 1, 2015.

Links

• Publisher Page: https://juti.if.its.ac.id/index.php/juti/article/view/1054
• Google Scholar: https://scholar.google.com/scholar?cluster=8696158275109397624

Abstract

Software requirements specification (SRS) is an essential part of software development. SRS has two features: functional requirements (FR) and non-functional requirements (NFR). Functional requirements define the needs that are directly in contact with stakeholders. Non-functional requirements describe how the software provides the means to carry out functional requirements. Non-functional requirements are often mixed with functional requirements. This study compares four primarily used machine learning methods for classifying functional and nonfunctional requirements. The contribution of our research is to use the PROMISE and SecReq (ePurse) dataset, then classify them by comparing the FastText+SVM, FastText+CNN, SVM, and CNN classification methods. CNN outperformed other methods on both datasets. The accuracy obtained by CNN on the PROMISE dataset is 99% and on the Seqreq dataset is 94%.

Keywords: CNN; FastText; Requirements Classification; Software Requirements; SVM

Citation

Haque MA, Rahman MA, Siddik MS. Non-Functional Requirements Classification with Feature Extraction and Machine Learning: An Empirical Study. In: 1st International Conference on Advances in Science, Engineering and Robotics Technology 2019 (ICASERT 2019) IEEE; 2019. p. 1–5.

Hakim L, Rochimah S, Fatichah C. Evaluasi kombinasi hipernin dan sinonim untuk klasifikasi kebutuhan non-functional berbasis ISO/IEC 25010. Jurnal Teknologi Informasi dan Ilmu Komputer 2019 10;6:491–500.

Gu Y, Zhang S, Qiu L, Wang Z, Zhang L. A layered KNN-SVM approach to predict missing values of functional requirements in product customization. Applied Sciences 2021 3;11:2420.

Osman MH, Zaharin MF. Ambiguous Software Requirement Specification Detection: An Automated Approach. In: Proceedings – International Conference on Software Engineering IEEE Computer Society; 2018. p. 33–40.

Vanicek J. Software quality requirements. Agric Econ 2008;52:177–185.

Supriyono S. Penerapan ISO 9126 dalam pengujian kualitas perangkat lunak pada E-book. MATICS 2019 10;11:9.

Shreda QA, Hanani AA. Identifying non-functional requirements from unconstrained documents using natural language processing and machine learning approaches. IEEE Access 2021;4:1–22.

Solomin AA, Ivanova Bolotova YA. Modern approaches to multiclass intent classification based on pre-trained transformers. Scientific and Technical Journal of Information Technologies, Mechanics and Optics 2020;4(1):532–538.

Li LF, Jin-An NC, Kasirun ZM, Piaw CY. An empirical comparison of machine learning algorithms for classification of software requirements. International Journal of Advanced Computer Science and Applications 2019;10(11):258–263.

Tóth L, Vidács L. Study of the performance of various classifiers in labeling non-functional requirements. Information Technology and Control 2019;48(3):432–445.

Abdel Qader A. A novel intelligent model for classifying and evaluating non-functional security requirements form scenarios. Indonesian Journal of Electrical Engineering and Computer Science 2019 sep;15(3):1578–1585.

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Rahman MA, Haque MA, Tawhid MNA, Siddik MS. Classifying Non-Functional Requirements Using RNN Variants for Quality Software Development. In: MaLTeSQuE 2019 – Proceedings of the 3rd ACM SIGSOFT International Workshop on Machine Learning Techniques for Software Quality Evaluation, co-located with ESEC/FSE 2019 Association for Computing Machinery, Inc; 2019. p. 25–30.

Fahmi AA, Siahaan D. Algorithms comparison for non-requirements classification using the semantic feature of software requirement statements. IPTEK The Journal for Technology and Science 2021 1;31:343.

Tiun S, Mokhtar UA, Bakar SH, Saad S. Classification of Functional and Non-Functional Requirement in Software Requirement Using Word2vec and Fast Text. In: Journal of Physics: Conference Series, vol. 1529 Institute of Physics Publishing; 2020. p. 42077.

Rahimi N, Eassa F, Elrefaei L. An ensemble machine learning technique for functional requirement classification. Symmetry 2020 10;12:1–26.

Canedo ED, Mendes BC. Software requirements classification using machine learning algorithms. Entropy 2020 9;22:1–20.

Baker C, Deng L, Chakraborty S, Dehlinger J. Automatic Multi-Class Non-Functional Software Requirements Classification Using Neural Networks. In: Proceedings – International Computer Software and Applications Conference, vol. 2 IEEE Computer Society; 2019. p. 610–615.

Houmb SH, Islam S, Knauss E, Jurjens J, Schneider K. Eliciting security requirements and tracing them to design: An integration of common criteria, heuristics, and UMLsec. Requirements Eng 2020;15:63–93.

Dekhtyar A, Fong V. RE Data Challenge: Requirements Identification with Word2Vec and TensorFlow. In: Proceedings-2017 IEEE 25th International Requirements Engineering Conference, RE 2017 Institute of Electrical and Electronics Engineers Inc.; 2017. p. 484–489.

Hey T, Keim J, Koziolek A, Tichy WF. NoRBERT: Transfer Learning for Requirements Classification. In: in Proceedings of the IEEE International Conference on Requirements Engineering; 2020. p. 169–179.

Links

• Publisher Page: https://iptek.its.ac.id/index.php/jts/article/view/13005
• Google Scholar: https://scholar.google.com/scholar?cluster=1908119983044836934