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All Journal CAUCHY: Jurnal Matematika Murni dan Aplikasi Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Jurnal Ilmiah Kursor Jurnal Teknologi Informasi dan Ilmu Komputer JUITA : Jurnal Informatika INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi Jurnal Pemberdayaan: Publikasi Hasil Pengabdian Kepada Masyarakat Jurnal Ilmiah Matematika dan Pendidikan Matematika (JMP) Computer Science and Information Technologies Jurnal Ilmiah Matematika
Aris Thobirin, Aris
University of Ahmad Dahlan
Arts Humanities Computer Science & IT Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Environmental Science Languange, Linguistic, Communication & Media Mathematics Social Sciences Other

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Implementation of DenseNet121 Based on Convolutional Neural1 Network with Geometric Augmentation for Breast Cancer2 Histopathology Image Classification Ariani, Nabilah Evi; Surono, Sugiyarto; Thobirin, Aris
CAUCHY: Jurnal Matematika Murni dan Aplikasi Vol 11, No 1 (2026): CAUCHY: JURNAL MATEMATIKA MURNI DAN APLIKASI
Publisher : Mathematics Department, Universitas Islam Negeri Maulana Malik Ibrahim Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18860/cauchy.v11i1.37896

Abstract

The development of a robust deep learning architecture that is not easily affected by overfitting is an important factor in improving the performance of medical image classification systems. This study aims to assess the ability of the DenseNet121 architecture to classify histopathological images into two categories. The model utilizes pre-trained weights from ImageNet and is adjusted through fine-tuning, while geometric data augmentation techniques are performed to increase sample variation. The training process utilizes the AdamW optimizer and the Binary Cross-Entropy loss function, with performance assessment using binary classification metrics. The test results show that DenseNet121 achieved a training accuracy of 98.96%, a validation accuracy of 97.72%, and a testing accuracy of 97.09%, indicating consistent performance at each stage and no signs of overfitting. This finding indicates that DenseNet121 has great potential as an effective structure in histopathological image classification systems.
ResNet-50 and ResNeXt-50 for Multiclass Classification of Chronic Wound Images under Gaussian Blur Andhika, Reynaldi; Surono, Sugiyarto; Thobirin, Aris
CAUCHY: Jurnal Matematika Murni dan Aplikasi Vol 11, No 1 (2026): CAUCHY: JURNAL MATEMATIKA MURNI DAN APLIKASI
Publisher : Mathematics Department, Universitas Islam Negeri Maulana Malik Ibrahim Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18860/cauchy.v11i1.40323

Abstract

Chronic wound image classification is important for supporting the assessment of conditions such as diabetic foot ulcers (DFU) and pressure ulcers (PU). While convolutional neural network (CNN)–based approaches have shown promising results, most previous studies focus on binary classification and rarely evaluate robustness in multiclass chronic wound scenarios. This study investigates multiclass classification of chronic wound images, distinguishing DFU, PU, and Normal Skin, using ResNet-50 and ResNeXt-50 architectures. A total of 2,146 publicly available images were stratified at the image level into training (70%), validation (15%), and test (15%) sets. Both models were trained under an identical configuration using data augmentation and class-weighted loss. On clean test images, ResNet-50 and ResNeXt-50 achieved strong and comparable performance, with accuracies of 0.9877 and 0.9938 and macro-averaged F1-scores of 0.9866 and 0.9928, respectively. Robustness was evaluated by applying Gaussian blur at the inference stage to simulate image defocus. Under stronger blur (σ = 2.0), ResNeXt-50 maintained higher performance (accuracy 0.9723, macro-F1 0.9679) than ResNet-50 (accuracy 0.9200, macro-F1 0.9123). These results highlight the contribution of this study in evaluating robustness to blur in multiclass chronic wound image classification, while emphasizing that robustness is limited to resistance against image blur or defocus.
Hybrid Otsu Morphological Pre-processing for EfficientNetB4 Based Acute Lymphoblastic Leukemia Classification Audina, Maretta Mia; Surono, Sugiyarto; Thobirin, Aris; Wen, Goh Khang
CAUCHY: Jurnal Matematika Murni dan Aplikasi Vol 11, No 1 (2026): CAUCHY: JURNAL MATEMATIKA MURNI DAN APLIKASI
Publisher : Mathematics Department, Universitas Islam Negeri Maulana Malik Ibrahim Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18860/cauchy.v11i1.40730

Abstract

Image quality plays a crucial role in improving the performance of image-based classification models, particularly when raw images exhibit noise, uneven illumination, and unclear object boundaries. This study proposes a hybrid segmentation approach to enhance object separation by reducing background interference and refining object contours. The method combines Otsu thresholding for initial object–background separation with elliptical morphological operations to improve region consistency and boundary definition.The segmented grayscale images are replicated into three channels and resized to 224×224 pixels before being used as input to an EfficientNetB4-based classification model optimized with the AdamW optimizer and fine-tuning. Experimental results under identical data splits, training settings, and fine-tuning protocols show that the proposed segmentation-based method achieves a final test accuracy of 97%, outperforming the baseline model trained on raw images (95% test accuracy) using the same EfficientNetB4-AdamW configuration. These results demonstrate that incorporating segmentation in the preprocessing stage effectively enhances discriminative feature learning and improves overall classification performance.
Algoritma Support Vector Regression dan Analisis Long Short-Term Memory sebagai Penanganan Missing Data Parinzka, Zellya; Surono, Sugiyarto; Thobirin, Aris
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 13 No 1: Februari 2026
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.2026131

Abstract

Time series multivariat adalah jenis data yang sering digunakan di berbagai bidang seperti keuangan, statistik, dan kesehatan karena dapat menjelaskan hubungan kompleks antar variabel. Namun, sering kali terdapat masalah seperti missing data yang dapat menjadi tantangan signifikan dalam proses analisis, mengurangi kualitas data dan akurasi model prediksi. Penelitian ini bertujuan untuk mengatasi masalah missing data time series multivariat dengan menggunakan teknik Support Vector Regression (SVR) untuk imputasi missing data dan Long Short-Term Memory (LSTM) sebagai analisis prediktif. SVR diterapkan untuk memprediksi missing data berdasarkan hubungan antar variabelnya, sementara LSTM digunakan untuk memodelkan pola temporal dalam data yang telah diimputasi. Evaluasi kinerja menunjukkan bahwa metode ini dapat meningkatkan kualitas data dan akurasi prediksi secara signifikan. Dengan menghasilkan metrik evaluasi RMSE 0.16, MSE 0.03, dan MAE 0.13, metode integratif ini tidak hanya menawarkan solusi yang efektif untuk menangani missing data, tetapi juga membantu memperkuat penerapan machine learning dalam analisis data time series multivariat. Selain itu, penelitian ini menunjukkan relevansi praktis dari integritas metode imputasi berbasi SVR dan analisis prediktif dengan LSTM, yang mampu dalam meningkatkan integritas data serta menghasilkan model prediksi yang akurat, sehingga berpotensi mendukung pengambilan keputusan berbasis data dalam berbagai bidang yang lebih luas dan realistis, khususnya pada analisis indikator kesehatan seperti Life Expectancy.   Abstract Multivariate time series is a type of data often used in various fields such as finance, statistics, and health because it can explain complex relationships between variables. However, there are often issues like missing data that can pose significant challenges in the analysis process, reducing data quality and model prediction accuracy. This research aims to address the missing data problem in multivariate time series by using Support Vector Regression (SVR) for imputing missing data and Long Short-Term Memory (LSTM) for predictive analysis. SVR is applied to predict missing data based on the relationships between the variables, while LSTM is used to model temporal patterns in the imputed data. Performance evaluation shows that this method can significantly improve data quality and prediction accuracy. With evaluation metrics of RMSE 0.16, MSE 0.03, and MAE 0.13, this integrative method not only offers an effective solution for handling missing data but also helps strengthen the application of machine learning in multivariate time series data analysis. Furthermore, this research demonstrates the practical relevance of the integrity of SVR-based imputation methods and predictive analysis with LSTM, which can enhance data integrity and produce accurate predictive models, thus potentially supporting data-driven decision-making in broader and more realistic fields, particularly in the analysis of health indicators such as Life Expectancy.
Co-Authors Abdillah, Alfiyyah 'Ainul Abdulnazar, Mohamed Naeem Antharathara Andhika, Reynaldi Ariani, Nabilah Evi Audina, Maretta Mia Dewi, Deshinta Arrova Eko Prasetyo Firdausy, Salma Nadya Goh Khang Wen Hanifa, Riza Aulia Ibad, Irsyadul Ida Ayu Putu Sri Widnyani khusnul khotimah Larasati, Anggit Mujhid, Almuzhidul Parinzka, Zellya Rahmadani, Lanova Ade Rismawati, Dewi Salsabila, Sona Regina Subrata, Arsyad Cahya Sugiyarto Surono, Sugiyarto Suris, Badzliana Aqmar Surono , Sugiyarto Wijayanti, Dian Eka