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All Journal CAUCHY: Jurnal Matematika Murni dan Aplikasi JUITA : Jurnal Informatika JITK (Jurnal Ilmu Pengetahuan dan Komputer) Computer Science and Information Technologies
Aris Thobirin
Ahmad Dahlan University
Computer Science & IT Engineering Mathematics

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DEEP BELIEF NETWORK (DBN) IMPLEMENTATION FOR MULTIMODAL CLASSIFICATION OF SENTIMENT ANALYSIS Hilmi Hibatullah; Aris Thobirin; Sugiyarto Surono
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 10 No. 3 (2025): JITK Issue February 2025
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v10i3.6257

Abstract

In sentiment analysis, the use of multimodal data, consisting of a combination of images and text, is becoming increasingly important for understanding digital context. However, the main challenge lies in effectively integrating these two types of data into a single learning model. Deep Belief Network (DBN), with its capability to learn hierarchical data representations, is utilized to explore optimal strategies for multimodal sentiment analysis. The dataset includes 34,034 images from the FERPlus dataset to train the model in classifying emotions based on facial expressions, as well as 999 text and image samples obtained through crawling X. Experiments were conducted by comparing the performance of DBN with 2, 3, and 4 hidden layers across different test data sizes (10%-50%). The results indicate that the 3-hidden-layer configuration achieved the best performance, with a highest accuracy of 76% at a 20% test data size. Additionally, testing different learning rates (10⁻⁴ to 10⁻⁷) produced consistent results, but the fastest computation time was achieved with a learning rate of 10⁻⁴. Based on these findings, DBN with a 3-hidden-layer configuration and a learning rate of 10⁻⁴ is considered a more efficient alternative for multimodal sentiment analysis based on text and images.
Performance Analysis of Resampling Techniques for Overcoming Data Imbalance in Multiclass Classification Anggit Larasati; Sugiyarto Surono; Aris Thobirin; Deshinta Arrova Dewi
JUITA: Jurnal Informatika JUITA Vol. 13 Issue 1, March 2025
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30595/juita.v13i1.25270

Abstract

In the digital era, the development of modern technology has brought significant transformation to the medical world. The main objective of this research is to identify the performance of deep learning models in classifying kidney disease. By integrating the Convolutional Neural Network model, the performance of the classification process can be analyzed effectively and efficiently. However, data imbalance dramatically affects the performance evaluation of a model, requiring data resampling techniques. This research applies two resampling techniques, bootstrap-based random oversampling and random undersampling, to training data and adds data augmentation to increase image variations to prevent model overfitting. The architecture uses MobileNetV2, which compares hyperparameter fine-tuning in three optimizers. This research shows that the performance of MobileNetV2, which implements the bootstrap-based random oversampling technique, has the highest accuracy compared to random undersampling and no resampling methods. The oversampling technique with the RMSprop optimizer produced the highest accuracy, namely 95%. With precision, recall, and F-1 score, respectively, 0.93, 0.95, 0.94. The accuracy of oversampling with the Adam and Nadam optimizer is 94%. So, the contribution of this research is by applying bootstrap-based oversampling techniques and adding data augmentation to produce good model performance to be used to classify medical images.
DeepSMOTE for Tea Leaf Disease Classification Using DenseNet-121 Transfer Learning Nikita Putri Insani; Sugiyarto Surono; Aris Thobirin
CAUCHY: Jurnal Matematika Murni dan Aplikasi Vol 11, No 2 (2026): CAUCHY: JURNAL MATEMATIKA MURNI DAN APLIKASI
Publisher : Mathematics Department, Maulana Malik Ibrahim State Islamic University of Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18860/cauchy.v11i2.41693

Abstract

Data imbalance is a common problem in image classification that causes models to tend to be biased towards the majority class, so that the performance of the minority class decreases. To overcome this problem, this study applies the DeepSMOTE approach as a deep learning-based oversampling technique that produces synthetic images through interpolation in the latent space of the autoencoder. The balanced training data images were used to train the DenseNet-121 model through a transfer learning approach. Evaluations were carried out on three approaches, DeepSMOTE as the main approach, as well as two comparators, namely no augmentation and conventional augmentation. The results showed that DeepSMOTE had the highest accuracy, which was 97.73%, higher than the 8.99% non-augmentation approach and 4.49% from the conventional augmentation. F1-score value on minority classes such as Anthracnose increased by 0.2833 in the non-augmentation approach and 0.1318 in the conventional augmentation approach.These findings confirm that data balance plays an important role in improving model performance and that DeepSMOTE is able to provide a more even distribution of data thus supporting better generalization of image classification tasks.
Hybrid feature fusion from multiple CNN models with bayesian-optimized machine learning classifiers Dewi Rismawati; Sugiyarto Surono; Aris Thobirin
Computer Science and Information Technologies Vol 6, No 3: November 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/csit.v6i3.p315-325

Abstract

Information technology advancements have created big data, necessitating efficient techniques to retrieve helpful information. With its capacity to recognize and categorize patterns in data, especially the growing amount of picture data, deep learning is becoming a viable option. This research aims to develop a medical image classification model using chest X-Ray with four classes, namely Covid-19, Pneumonia, Tuberculosis, and Normal. The proposed method combines the advantages of deep learning and machine learning. Three pre-trained CNN models, VGG16, DenseNet201, and InceptionV3, extract features from images. The features generated from each model are fused to enhance the relevant information. Furthermore, principal component analysis (PCA) was applied to reduce the dimensionality of the features, and Bayesian optimization was used to optimize the hyperparameters of the machine learning algorithms support vector machine (SVM), decision tree (DT), and k-nearest neighbors (k-NN). The resulting classification model was evaluated based on accuracy, precision, recall, and F1-score. The results showed that FF-SVM, which is the proposed model, achieved an accuracy of 98.79% with precision, recall, and F1-score of 98.85%, 98.82%, and 98.84%, respectively. In conclusion, fusing feature extraction from multiple CNN models improved the classification accuracy of each machine-learning model. It provided reliable and accurate predictions for lung image diagnosis using chest X-Ray.
Co-Authors Anggit Larasati Deshinta Arrova Dewi Dewi Rismawati Hilmi Hibatullah Nikita Putri Insani Sugiyarto Surono