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All Journal CAUCHY: Jurnal Matematika Murni dan Aplikasi JITK (Jurnal Ilmu Pengetahuan dan Komputer)
Aris Thobirin
Ahmad Dahlan University
Computer Science & IT 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.
Hybrid Otsu Morphological Pre-processing for EfficientNetB4 Based Acute Lymphoblastic Leukemia Classification Maretta Mia Audina; Sugiyarto Surono; Aris Thobirin; Goh Khang Wen
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.
Co-Authors Goh Khang Wen Hilmi Hibatullah Maretta Mia Audina Sugiyarto Surono