Article Per Year (5 Year)
p-Index From 2021 - 2026
0.408
P-Index
This Author published in this journals
All Journal JUITA : Jurnal Informatika JITK (Jurnal Ilmu Pengetahuan dan Komputer)
Aris Thobirin
Ahmad Dahlan University
Computer Science & IT

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
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.
Co-Authors Anggit Larasati Deshinta Arrova Dewi Hilmi Hibatullah Sugiyarto Surono