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All Journal Jurnal Ilmiah Kursor Desimal: Jurnal Matematika
Awang, Mohd Khalid
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Computer Science & IT Decision Sciences, Operations Research & Management Education Mathematics Social Sciences

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Performance evaluation of clustering algorithms for protein sequence data Ardaneswari, Gianinna; Aminah, Siti; Awang, Mohd Khalid; Laksmitara, Anindya; Azkiya, Azkal; Razi, Fakhrur; Joshua Situmeang, Jason Nimrod
Desimal: Jurnal Matematika Vol. 8 No. 3 (2025): Desimal: Jurnal Matematika
Publisher : Universitas Islam Negeri Raden Intan Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24042/djm.v8i3.202528462

Abstract

Protein sequence data analysis is a fundamental task in bioinformatics, supporting the exploration of biological variations and the identification of functional relationships among proteins. This study presents a performance analysis of four clustering algorithms, which include Balanced Iterative Reducing and Clustering using Hierarchies (BIRCH), Density-Based Spatial Clustering of Applications with Noise (DBSCAN), Agglomerative Hierarchical Clustering, and Spectral Clustering, applied to protein sequence datasets. Feature extraction was conducted using the Discere package in Python, generating 27 numerical attributes from protein sequences. The optimal number of clusters for BIRCH, Agglomerative, and Spectral Clustering was determined using the Elbow method, while DBSCAN parameters (MinPts, Eps) were tuned using the sorted k-distance plot. Clustering performance was assessed using the Silhouette Score. Among the algorithms, DBSCAN produced the highest silhouette score of 0.8105, whereas BIRCH achieved a strong balance between clustering quality, with a score of 0.7405, and computational efficiency. Agglomerative clustering provided moderate results with a score of 0.6779, while Spectral clustering yielded the lowest score of 0.6310 but demonstrated flexibility in capturing complex structures. These findings provide a benchmark comparison of clustering methods for protein sequence data, offering practical insights into algorithm selection based on data characteristics and performance trade-offs.
Convolutional layer exertion on few-shot learning for brain tumor classification Sunarko, Victor Immanuel; Puspaningrum, Eva Yulia; Widiastuty, Riana Retno; Hadi, Surjo; Awang, Mohd Khalid; Mas Diyasa, I Gede Susrama
Jurnal Ilmiah Kursor Vol. 13 No. 2 (2025)
Publisher : Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21107/kursor.v13i2.430

Abstract

Brain tumors, though relatively rare, pose a significant threat due to their critical location within the brain, impacting essential bodily functions. Accurate and timely diagnosis is vital, but traditional diagnostic methods are time-intensive and rely heavily on large labeled datasets. This study addresses these challenges by proposing a Few-Shot Learning (FSL) framework enhanced with Convolutional Neural Networks (CNNs) to classify brain tumors using MRI images. By employing the Matching Network architecture, the model leverages limited training data through an N-way-K-shot setup. Training results demonstrated accuracy levels of 71.58% (1-shot) and 82.89% (5-shot) for 1-layer CNNs, 66.65% (1-shot) and 84.03% (5-shot) for 3-layer CNNs, and 63.43% (1-shot) and 84.94% (5-shot) for 5-layer CNNs. However, validation accuracy revealed overfitting concerns, with the highest performance at 51.56% (1-layer, 1-shot). These results underscore the potential of FSL in medical imaging while highlighting the need for advanced augmentation and feature representation techniques to improve generalization.
Co-Authors Ardaneswari, Gianinna Azkiya, Azkal Eva Yulia Puspaningrum Fakhrur Razi Hadi, Surjo Joshua Situmeang, Jason Nimrod Laksmitara, Anindya Mas Diyasa, I Gede Susrama Siti Aminah Sunarko, Victor Immanuel Widiastuty, Riana Retno