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All Journal bit-Tech ILKOMNIKA: Journal of Computer Science and Applied Informatics Jurnal Pendidikan Sains dan Komputer Algoritme Jurnal Mahasiswa Teknik Informatika Informatics and Enginering Dedication Jurnal Informatika Teknologi dan Sains (Jinteks) MDP Student Conference
Yoannita, Yoannita
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Agriculture, Biological Sciences & Forestry Humanities Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Immunology & microbiology Materials Science & Nanotechnology Mathematics Physics Other

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Machine Learning Classification of Liver Disease Using Clinical Data with SVM and PCA Johan, Daniel; Yoannita, Yoannita
bit-Tech Vol. 8 No. 3 (2026): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i3.3494

Abstract

Liver disease remains a major global health problem that requires early and accurate diagnosis to prevent severe clinical complications and mortality. In recent years, Support Vector Machine (SVM) combined with Principal Component Analysis (PCA) has been widely applied for liver disease classification. However, existing studies are often limited by small or moderately sized datasets, a lack of systematic comparison among SVM kernel functions, and insufficient discussion of clinical relevance and data representativeness. These limitations restrict model generalizability and hinder practical clinical adoption. To address these gaps, this study evaluates a PCA–SVM classification framework using a large-scale Liver Disease Patient Dataset comprising 30,691 clinical records, thereby improving robustness and population representativeness. The main contribution of this research lies in a systematic and controlled comparison of four SVM kernel functions linear, radial basis function (RBF), polynomial, and sigmoid—under identical preprocessing and dimensionality reduction conditions. PCA is applied to reduce feature redundancy while preserving over 97% of clinically relevant information, supporting efficient learning without increasing model complexity. Experimental results indicate that kernel selection has a substantial impact on diagnostic performance. The RBF kernel consistently outperforms other kernels, achieving an accuracy of 83.63% and an area under the ROC curve of 92.09%, while maintaining strong generalization on unseen data. From a clinical perspective, these findings demonstrate that the proposed PCA–SVM framework has significant potential as a clinical decision support tool for early liver disease screening based on routine laboratory data, offering a balance between predictive performance, computational efficiency, and practical applicability.
Waste Classification Using Convolutional Neural Network with ShuffleNetV2 Architecture Fahlevhi, Muhammad Alif; Yoannita, Yoannita
ILKOMNIKA Vol 8 No 1 (2026): Volume 8, Number 1, April 2026
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28926/ilkomnika.v8i1.848

Abstract

Internet of Things (IoT)-based smart waste sorting systems require classification algorithms that are not only accurate but also efficient in resource utilization. However, the majority of previous studies tend to focus on heavy-architecture Deep Learning models (such as VGG or ResNet) that burden edge devices, or utilize lightweight models that are limited to a few class categories. This research contributes to filling this gap by evaluating the effectiveness of the ShuffleNetV2 architecture, a lightweight CNN that optimizes Memory Access Cost (MAC), to classify 9 complex waste categories (Biological, Clothes, Glass, Plastic, Shoes, Battery, Metal, Paper, Cardboard). The research dataset was compiled through the curation and combination of three public Kaggle repositories, which were reprocessed using Roboflow, producing 19,906 augmented images to ensure visual domain variance. Empirical evaluation results show that the model achieved an accuracy of 94% with an average F1-Score of 0.93. The efficiency advantage is evidenced by the compact model size (4.99 MB) and low estimated computational load (0.30 GFLOPs) compared to conventional models. These findings indicate that ShuffleNetV2 offers an optimal performance trade-off, making it a feasible solution for implementation on mobile devices and low-power embedded systems.
Co-Authors Alviando, Muhammad Restu Athar, Muhammad Farid Azarya, Philips Denny Daniel Udjulawa Derry Alamsyah Dicky Pratama Fahlevhi, Muhammad Alif gasim, Gasim Johan, Daniel Krisna Putra, Jelvin Leovincent, Axel M Ezar Al Rivan Marzali, Azizurrahman Maximilliano, Wesley Oktaviani, Ayu Sri Pandi Pandi, Pandi Piscessara, Veldy Putra, Ronaldo Timoti, Gerry Jeven Tinaliah, Tinaliah Triana Elizabeth, Triana Wiwik Handayani Yohannes, Yohannes