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All Journal EMITTER International Journal of Engineering Technology JOIV : International Journal on Informatics Visualization Technomedia Journal Journal of Applied Data Sciences El-Mujtama: Jurnal Pengabdian Masyarakat The Indonesian Journal of Computer Science Riau Jurnal Teknik Informatika
Tita Karlita
Politeknik Elektronika Negeri Surabaya
Religion Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Social Sciences

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Process Design of Software Library Development for Deep Learning Module in Java Programming with Four-Phase Methodology: Preparation, Identification, Design, and Development Barakbah, Ali Ridho; Rachmawati, Oktavia Citra Resmi; Karlita, Tita
Journal of Applied Data Sciences Vol 7, No 1: January 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v7i1.989

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.
Co-Authors Achmad Basuki Alde, Muhammad Riski Alfi Fadliana Ali Ridho Barakbah ANITA DAMAYANTI Arna Fariza Arya Brahmanta, Arya Asmara, Rengga Azahro Choirunisa, Nadia Bima Sena Bayu Dewantara Edelani, Renovita Edi Satriyanto Eko Mulyanto Yuniarno Fatihia, Wifda Muna Fauzi Nafi'Ubadah, Kriza Febrianto, Ardiansyah Indra Fitrah Maharani Humaira Haikal Yuniarta Krisgianto, Ricko I Ketut Eddy Purnama I Made Gede Sunarya Insani, Fawzan Isbat Uzzin Nadhori, Isbat Uzzin Lakmi Makarti, Lakmi Makarim, Ahmad Fauzi Maulana, Firja Hanif Maulana, Wahyu Ikbal Mauridhi Hery Purnomo Ni'Ma, Najma Akmalina Nikma Prastita, Valentia Priambodo, Joko Rachmawati, Oktavia Citra Resmi Ratna Kusumawati Ridho, Bistiana Syafina Riyanto Sigit, Riyanto Rokhana, Rika Saputra, Muhammad Krisnanda Vilovan Susetyoko, Ronny Tri Hadiah Muliawati, Tri Hadiah Yuniarti, Heny