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Esther Irawati Setiawan
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insyst@istts.ac.id
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Kampus Institut Sains dan Teknologi Terpadu Surabaya (d/h Sekolah Tinggi Teknik Surabaya) Ngagel Jaya Tengah 73-77, Surabaya, Indonesia
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Insyst : Journal of Intelligent System and Computation
Published by Sekolah Tinggi Teknik Surabaya
ISSN : 26219220     EISSN : 27221962     DOI : https://doi.org/10.52985/insyst
Core Subject : Science,
Computer Science & IT
The Intelligent System and Computation Journal will be published for 2 editions in a year, every April and October. The Intelligent System and Computation Journal is an open access journal where full articles in this journal can be accessed openly. Review in this journal will be conducted with a blind review system. All articles in this journal will be indexed by Google Scholar. The topics contained in this journal consist of several fields (but not limited to): Algorithms and complexity Artificial Intelligence Big Data Analytics Biomedical Instrumentation Computational logic Computer Vision and Biometric Data and Web Mining Digital Signal Processing Image Processing Information Retrieval & Information Extraction Intelligence Embedded Systems Machine Learning Mathematics and models of computation Natural Language Processing Parallel & Distributed Computing Pattern Recognition Programming languages and semantics Speech Processing Virtual Reality & Augmented Reality
Arjuna Subject : Ilmu Komputer (semua kategori) - Kecerdasan Buatan
Articles 3 Documents
 1 
Search results for , issue "Vol 7 No 2 (2025): INSYST: Journal of Intelligent System and Computation" : 3 Documents clear
Comparison of Random Forest and SVM Algorithms in Credit Risk Evaluation Based on Debtor Occupation Prayesy, Putri Armilia; Pujakesuma, Angga
Intelligent System and Computation Vol 7 No 2 (2025): INSYST: Journal of Intelligent System and Computation
Publisher : Institut Sains dan Teknologi Terpadu Surabaya (d/h Sekolah Tinggi Teknik Surabaya)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52985/insyst.v7i2.431

Abstract

Credit is one of the main sources of income for banking institutions and plays a crucial role in supporting long-term profit growth. However, credit distribution is inherently associated with risks, especially the risk of default when borrowers fail to meet their repayment obligations as agreed. One effective strategy to minimize such risks is to conduct a comprehensive and accurate creditworthiness assessment of prospective borrowers before loan approval is granted. This study aims to evaluate the performance of three classification algorithms—Random Forest, Support Vector Machine (SVM), and Artificial Neural Network (ANN)—in predicting credit risk based on the borrower’s occupation. The dataset used consists of 1,314 loan records with an imbalanced distribution between performing and non-performing loans. The experimental results show that the Random Forest algorithm achieved the highest accuracy at 97%, followed by Support Vector Machine at 73% and Artificial Neural Networks at 64%. While ANN is capable of capturing complex patterns through multilayered learning, Random Forest proved to be the most effective and robust in handling the given dataset. These findings clearly indicate that Random Forest can serve as a reliable method for financial institutions to enhance credit risk evaluation and minimize potential losses arising from loan defaults.
A Hierarchical Multi-Label Classification Approach for the Automated Interpretation of Spinal MRI Series Cahyadi, David; Pramana, Edwin; Limantara, Rudi; Wiguna, I Gusti Lanang Ngurah Agung Artha; Deslivia, Maria Florencia; Liando, Ivan Alexander
Intelligent System and Computation Vol 7 No 2 (2025): INSYST: Journal of Intelligent System and Computation
Publisher : Institut Sains dan Teknologi Terpadu Surabaya (d/h Sekolah Tinggi Teknik Surabaya)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52985/insyst.v7i2.438

Abstract

Manually selecting MRI slices is a significant bottleneck in clinical workflows. This issue is worsened by inconsistent naming conventions and variable acquisition protocols across institutions and radiologists, often leading to redundant efforts and potential oversights during medical image data preprocessing. This study introduces a fully automated, four-level hierarchical classification system specifically designed to intelligently filter and select clinically relevant spinal MRI slices directly from raw DICOM series. Our primary objective is to streamline the initial stages of radiological assessment, ensuring that only pertinent images are presented for subsequent analysis and review. We thoroughly evaluated the performance of modern, efficient deep learning architectures, including EfficientViT, MobileNetV4, and RepViT, benchmarking them against a robust ResNet-18 baseline. The proposed pipeline systematically refines its analysis through a structured hierarchy: it first broadly identifies the anatomical region, then precisely classifies the spine location and specific view (axial, sagittal, or coronal). Subsequently, it categorizes the imaging contrast, and finally, confirms the presence of the spinal cord. Our comprehensive experimental results reveal that the EfficientViT-based model achieved the highest end-to-end F1-score of 0.8357, demonstrating robust accuracy across all classification levels. Furthermore, its average inference speed of 9.17 ms per image highlights its computational efficiency. This automated pipeline offers an effective and computationally efficient solution for speeding up initial medical image preprocessing, ensuring subsequent analytical tasks are performed on accurately selected, clinically relevant data.
Multi View Neural Network for Software Effort Estimation Prediction Setiawan, Boy; Subekti, Agus
Intelligent System and Computation Vol 7 No 2 (2025): INSYST: Journal of Intelligent System and Computation
Publisher : Institut Sains dan Teknologi Terpadu Surabaya (d/h Sekolah Tinggi Teknik Surabaya)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52985/insyst.v7i2.442

Abstract

Software Effort Estimation (SEE) is a critical challenge in software project management, dating back to the early years of software engineering. Accurate estimation of the effort required for software development is essential for project planning, resource allocation, and risk management. Incorrect effort estimates can result in poor resource distribution, cost overruns, missed deadlines, and even complete project failure. This issue is increasingly urgent today as software systems are deeply embedded in almost every product and service, amplifying the need for reliable and accurate predictions. Over the years, several methods for SEE have been proposed, ranging from algorithmic models to expert judgment. More recently, machine learning (ML) approaches such as Case-Based Reasoning (CBR), Support Vector Machines (SVM), Decision Trees (DT), and Neural Networks (NN) have gained attention for their ability to model complex, nonlinear relationships inherent in SEE tasks. In this study, we propose a novel approach based on multi-view learning with NN (MVNN), which leverages multiple views from existing datasets, thus improving performance and generalization, particularly when the available data is small and scarce. The effectiveness of the MVNN model is validated through empirical comparisons with existing SEE models, demonstrating its potential to enhance SEE accuracy and improve prediction reliability.

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