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All Journal JUTI: Jurnal Ilmiah Teknologi Informasi Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)
Bintang Nuralamsyah
Institut Teknologi Sepuluh Nopember
Computer Science & IT Dentistry Education Engineering Library & Information Science

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Decomposing Monolithic to Microservices: Keyword Extraction and BFS Combination Method to Cluster Monolithic’s Classes Siti Rochimah; Bintang Nuralamsyah
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 7 No 2 (2023): April 2023
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v7i2.4866

Abstract

Abstract Microservices architecture is widely used because of the ease of maintaining its microservices, as opposed to encapsulating functionality in a monolithic, which may negatively impact the development process when the application continues to grow. The migration process from a monolithic architecture to microservices became necessary, but it often relies on the architect's intuition only, which may cost many resources. A method to assist developers in decomposing monolithic into microservices is proposed to address that problem. Unlike the existing methods that often rely on non-source code artifacts which may lead into inaccurate decomposition if the artifacts do not reflect the latest condition of the monolith, the proposed method relies on analyzing the application source code to produce a grouping recommendation for building microservices. By using specific keyword extraction followed by Breadth First Search traversal with certain rules, the proposed method decomposed the monolith's component into several cluster whose majority of cluster’s members have uniform business domain. Based on the experiment, the proposed method got an 0.81 accuracy mean in grouping monolithic's components with similar business domain, higher than the existing decomposition method's score. Further research is recommended to be done to increase the availability of the proposed method.
A Dual-Network iTransformer Model for Robust and Efficient Time Series Forecasting Ary Mazharuddin Shiddiqi; Bagaskoro Kuncoro Ardi; Bilqis Amaliah; I Komang Ari Mogi; Agung Mustika Rizki; Bintang Nuralamsyah; Ilham Gurat Adillion; Moch. Nafkhan Alzamzami
JUTI: Jurnal Ilmiah Teknologi Informasi Vol.23, No.2, July 2025
Publisher : Department of Informatics, Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j24068535.v23i2.a1264

Abstract

Time-series forecasting plays a crucial role in various fields, including economics, healthcare, and meteorology, where accurate predictions are essential for informed decision-making. As data volume and complexity continue to grow, the need for efficient and reliable forecasting methods has become more critical. iTransformer, a recent innovation, improves interpretability while effectively handling multivariate data. In this study, the author proposes Dual-Net iTransformer, a novel approach that integrates iTransformer with a dual-network framework to enhance both accuracy and efficiency in time-series forecasting. This research aims to evaluate and compare the performance of traditional methods, iTransformer, and Dual-Net iTransformer, highlighting the advantages of the proposed model in improving forecasting outcomes.
Co-Authors Agung Mustika Rizki Ary Mazharuddin Shiddiqi Bagaskoro Kuncoro Ardi Bilqis Amaliah I Komang Ari Mogi Ilham Gurat Adillion Moch. Nafkhan Alzamzami Siti Rochimah