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All Journal Jurnal Teknologi Informasi dan Ilmu Komputer ILKOMNIKA: Journal of Computer Science and Applied Informatics
Jannah, Maughfirotul
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Engineering

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Segmentasi Citra X-Ray Dada Menggunakan Metode Modifikasi Deeplabv3+ Wahyuningrum, Rima Tri; Jannah, Maughfirotul; Satoto, Budi Dwi; Sari, Amillia Kartika; Sensusiati, Anggraini Dwi
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 10 No 3: Juni 2023
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.2023106754

Abstract

COVID-19 is a disease that affects the human respiratory system. The latest developments in September 2022 the number of confirmed cases of COVID-19 worldwide reached 608,328,548 with 6,501,469 patients who died. While in Indonesia confirmed COVID-19 reached 6,408,806 with 157,892 patients who died. Reserve Transcription Polymerase Chain Reaction (RT-PCR) is the most widely used tool. However, the latest RT-PCR test report shows that the RT-PCR test is inadequate. As an alternative, radiographic images such as chest x-rays and CT scans can help detect this. Radiographic images, especially x-rays, need processing to be able to make a diagnosis. Computer Aided Diagnosis (CAD) is a computer assisted diagnosis system that can be used as supporting information in making a diagnosis. To make it easier to make a diagnosis, we need a deep learning model that can help with this. DeepLabV3+ is a method that can carry out the segmentation process. DeepLabV3+ which is an extension of DeepLabV3 with the aim of improving the segmentation results. DeepLabV3+ uses a modified Xception as the backbone. In this study, 1,500 chest x-ray image data were used which were then divided into 80% for training data and 20% for testing data. There are 4 test scenarios in this study, namely with a learning rate of 0.01 without CLAHE, a learning rate of 0,01 and using CLAHE, a learning rate of 0,0001 without CLAHE, and a learning rate of 0,0001 using CLAHE. Of the 4 scenarios the learning rate scenario is 0,01 and using CLAHE gets the highest evaluation results using the Dice Similarity Coefficient (DSC) of 96.91%. 
Microservices Architecture Reconstructed from Monolithic Moodle using Graph-Based Deep Learning Jannah, Maughfirotul; Yaqin, Muhammad Ainul
ILKOMNIKA Vol 8 No 2 (2026): Volume 8, Number 2, August 2026
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28926/ilkomnika.v8i2.883

Abstract

This research aims to reconstruct the microservices architecture of the Moodle monolithic system using the Graph-Based Deep Learning approach, especially the Graph Convolutional Network (GCN). The study focused on analyzing dependencies in 13,841 PHP files from the Moodle repository version MOODLE_403_STABLE. The main problem is the high complexity of dependencies in Moodle's monolithic architecture, which makes it difficult for traditional clustering methods to automatically determine the right service boundaries. The results showed that the Wide GCN configuration (with a wider layer) converted the negative modularity (-0.29007) to positive (0.11676) in the global population and achieved the highest modularity value of 0.24794 in the assessment module. The study's main finding is that the use of Wide GCN consistently improves cohesion by up to tenfold compared to monolithic conditions, although there is a trade-off in the form of increased coupling. The contributions of this study include: (1) empirical evidence regarding the advantages of GNN in systems with dense dependencies, (2) systematic comparisons between wide and standard configurations, and (3) simultaneous integration of four architecture quality metrics.
Co-Authors Ainul Yaqin Amillia Kartika Sari Budi Dwi Satoto Rima Tri Wahyuningrum Sensusiati, Anggraini Dwi