.png)
Process Design of Software Library Development for Deep Learning Module in Java Programming with Four-Phase Methodology: Preparation, Identification, Design, and Development
Abstract
Recent advances in deep learning have driven remarkable achievements across various domains, including computer vision, natural language processing, and medical diagnostics. However, prevailing DL libraries often expose monolithic and tightly coupled codebases, making it difficult for researchers to inject custom mathematical formulations into core training routines. To address this limitation, we introduce a modular software library that empowers users in both academia and industry to extend and modify training functions with minimal friction. This paper focuses on the preparatory stages of library development in Java Programming, presenting a four-phase methodology comprising Preparation (ideation, research questions, literature review), Identification (term extraction, goal definition, environment setup), Design (architecture modeling, class and attribute specification, task scheduling), and Development (component exploration, functionality construction). Through these sequential activities, we have produced eleven detailed design documents, including vision statements, quality-attribute scenarios, architectural decision records, and API specifications, that collectively capture the rationale and technical blueprint of our library. By sharing our step-by-step process, we aim to provide a replicable framework for future researchers undertaking the architectural design of specialized Deep Learning libraries.
Keywords
Full Text:
PDFReferences
E. D. Lusiana, S. Astutik, Nurjannah, and A. B. Sambah, “Using machine learning approach to cluster marine environmental features of Lesser Sunda Island,” Journal of Applied Data Sciences, vol. 6, no.1, pp.247-258, Jan. 2025.
A. Apandi, A. B. Mutiara, and Dharmayanti, “Generating image captions in indonesian using a deep learning approach based on Vision Transformer and IndoBERT architectures,” Journal of Applied Data Sciences, vol. 6, no.2, pp.1191-1202, May 2025.
V. Sumathi, B. L. Shivakumar, S. S. Maidin, and W. Ge, “Optimizing emergency logistics identification: utilizing a deep learning model in the big data era,” Journal of Applied Data Sciences, vol. 5, no.3, pp.1440-1448, Sep. 2024.
Y. Liu, “Research on deep learning-based algorithm and model for personalized recommendation of resources,” Journal of Applied Data Sciences, vol. 4, no.2, pp.68-75, May 2023.
S. A. Qureshi et al., “Breast cancer detection using mammography: Image processing to deep learning,” IEEE Access, vol. 13, pp. 60776–60801, Dec. 2024.
B. Jain, P. Pawar, D. Gada, and T. Patwa, “Comprehensive analysis of machine learning and deep learning models on prompt injection classification using natural language processing techniques,” International Research Journal of Multidisciplinary Technovation, vol. 7, no. 2, pp. 24–37, Feb. 2025.
H. Javed, S. El-Sappagh, and T. Abuhmed, “Robustness in deep learning models for medical diagnostics: security and adversarial challenges towards robust AI applications,” Artificial Intelligence Review, vol. 58, no. 1, Nov. 2024.
M. Mishra and J. G. Singh, “A comprehensive review on deep learning techniques in power system protection: trends, challenges, applications and future directions,” Results in Engineering, vol. 25, Jan. 2025.
B. Ozdemir and I. Pacal, “A robust deep learning framework for multiclass skin cancer classification,” Scientific Reports, vol. 15, no. 1, Art. no. 4938, Feb. 2025.
S. Imambi, K. B. Prakash, and G. R. Kanagachidambaresan, “PyTorch,” in EAI/Springer Innovations in Communication and Computing, 2021, pp. 87–104.
A. K. Jha, A. Ruwali, K. B. Prakash, and G. R. Kanagachidambaresan, “Tensorflow Basics,” in EAI/Springer Innovations in Communication and Computing, 2021, pp. 5–13.
O. C. R. Rachmawati, A. R. Barakbah, and T. Karlita, “Programming language selection for the development of deep learning library,” JOIV International Journal on Informatics Visualization, vol. 8, no. 1, p. 434, Mar. 2024.
M. N. Shodiq, D. H. Kusuma, M. G. Rifqi, A. R. Barakbah, and T. Harsono, “Neural network for earthquake prediction based on automatic clustering in Indonesia,” JOIV International Journal on Informatics Visualization, vol. 2, no. 1, pp. 37–43, Feb. 2018.
N. Ramadijanti, A. R. Barakbah, and F. A. Husna, “Automatic breast tumor segmentation using Hierarchical K-means on mammogram,” 2018 International Electronics Symposium on Knowledge Creation and Intelligent Computing (IES-KCIC), Bali, Indonesia, 2018, pp. 170-175.
M. Subhan, A. Sudarsono, and A. R. Barakbah, “Classification of radical web content in indonesia using web content mining and k-Nearest Neighbor algorithm,” EMITTER International Journal of Engineering Technology, vol. 5, no. 2, pp. 328–348, Jan. 2018.
O. C. R. Rachmawati, A. R. Barakbah, and T. Karlita, “The comparison of activation functions in feature extraction layer using sharpen filter,” Journal of Applied Engineering and Technological Science (JAETS), vol. 6, no. 2, pp. 1254–1267, Jun. 2025.
F. Yang, J. M. Caballero, and R. A. Juanatas, “Designing of Chinese ancient poetry app based on iOS platform augmented reality and machine learning,” 2022 7th International Conference on Business and Industrial Research (ICBIR), pp. 285–288, May 2022.
D. K. Wardani, O. C. R. Rahmawati, and W. M. Fatihia, “Perancangan aplikasi SICITRA menggunakan Unified Modelling Language,” Jurnal Transformatika, vol. 21, no. 1, Aug. 2023.
F. J. Agbo, S. S. Oyelere, J. Suhonen, and M. Tukiainen, “Design, development, and evaluation of a virtual reality game-based application to support computational thinking,” Educational Technology Research and Development, vol. 71, no. 2, pp. 505–537, Oct. 2022.
M. Fruhner and H. Tapken, “From persons to animals: transferring person re-identification methods to a multi-species animal domain,” in Proceedings of the 2024 9th International Conference on Multimedia and Image Processing, Osaka, Japan, 2024, pp. 39–43.
F. Pérez-García, R. Sparks, and S. Ourselin, “TorchIO: A Python library for efficient loading, preprocessing, augmentation and patch-based sampling of medical images in deep learning,” Computer Methods and Programs in Biomedicine, vol. 208, p. 106236, Jun. 2021.
D. N. Shibaeva, D. A. Asanovich, K. A. Malodushev, and D. A. Shamshura, “Development of a software package for systematization and analysis of research results of iron ore preparation characteristics: goals, objectives, first data,” Mining Industry Journal (Gornay Promishlennost), no. 6/2024, pp. 99–106, Dec. 2024.
Y. Romani, O. Tibermacine, and C. Tibermacine, "Towards Migrating Legacy Software Systems to Microservice-based Architectures: a Data-Centric Process for Microservice Identification," 2022 IEEE 19th International Conference on Software Architecture Companion (ICSA-C), Honolulu, HI, USA, 2022, pp. 15-19.
M. Zorzetti, I. Signoretti, L. Salerno, S. Marczak, and R. Bastos, “Improving agile software development using user-centered design and lean startup,” Information and Software Technology, vol. 141, p. 106718, Aug. 2021.
M. Kuhrmann et al., “What makes agile software development agile?,” IEEE Transactions on Software Engineering, vol. 48, no. 9, pp. 3523–3539, Jul. 2021.
O. C. R. Rachmawati, N. D. K. Wardani, N. W. M. Fatihia, N. A. Fariza, and N. H. Rante, “Implementing agile scrum methodology in the development of SICITRA mobile application,” Jurnal RESTI (Rekayasa Sistem Dan Teknologi Informasi), vol. 7, no. 1, pp. 41–50, Feb. 2023.
T. A. C. H. Chan, J. M.-B. Ho, and M. Tom, “Miro: promoting collaboration through online whiteboard interaction,” RELC Journal, vol. 55, no. 3, pp. 871–875, Mar. 2023.
E. Hendrawan, M. Meisel, and D. N. Sari, “Analysis and implementation of computer network systems using software Draw.io,” Asia Information System Journal, vol. 2, no. 1, May 2023.
W. Tamrat, “The notion of relevance in academic collaboration,” International Journal of African Higher Education, vol. 9, no. 3, pp. 133–151, Dec. 2022.
S. Davies, P. S. Hakkarainen, and K. Hakkarainen, “Idea generation and knowledge creation through maker practices in an artifact-mediated collaborative invention project,” Learning Culture and Social Interaction, vol. 39, p. 100692, Feb. 2023, doi: 10.1016/j.lcsi.2023.100692.
K. Doulougeri, J. D. Vermunt, G. Bombaerts, and M. Bots, “Challenge‐based learning implementation in engineering education: A systematic literature review,” Journal of Engineering Education, vol. 113, no. 4, pp. 1076–1106, Mar. 2024.
O. C. R. Rachmawati and Z. M. E. Darmawan, “The comparison of deep learning models for Indonesian political hoax news detection,” CommIT (Communication and Information Technology) Journal, vol. 18, no. 2, pp. 123–135, Aug. 2024.
N. Singh, “Framework of goal-driven risk management in software development projects using the socio-technical Systems approach,” FIIB Business Review, vol. 13, no. 4, pp. 437–451, Sep. 2021.
M. Loor, A. Tapia-Rosero, and G. D. Tre, “An open-source software library for explainable support vector machine classification,” 2022 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), pp. 1–7, Jul. 2022.
R. C. Martin, Clean architecture: A Craftsman’s Guide to Software Structure and Design. Prentice Hall, 2017.
K. L. Petry, E. OliveiraJr, and A. F. Zorzo, “Model-based testing of software product lines: Mapping study and research roadmap,” Journal of Systems and Software, vol. 167, p. 110608, Apr. 2020.
N. Damij and T. Damij, “An approach to optimizing Kanban board workflow and shortening the project management plan,” IEEE Transactions on Engineering Management, vol. 71, pp. 13266–13273, Nov. 2021.
I. Goodfellow, Y. Bengio, and A. Courville, Deep learning. MIT Press, 2016.
E. Gamma, R. Helm, R. Johnson, and J. Vlissides, Design patterns: Elements of Reusable Object-Oriented Software. Pearson Deutschland GmbH, 1995.
DOI: https://doi.org/10.47738/jads.v7i1.989
Refbacks
- There are currently no refbacks.
Journal of Applied Data Sciences
| ISSN | : | 2723-6471 (Online) |
| Publisher | : | Bright Publisher |
| Website | : | http://bright-journal.org/JADS |
| : | taqwa@amikompurwokerto.ac.id (principal contact) | |
| support@bright-journal.org (technical issues) |
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0