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All Journal Jurnal Teknik Informatika (JUTIF)
Odi Nurdiawan
Informatics Management, STMIK IKMI Cirebon, Indonesia
Computer Science & IT

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Optimizing Multimodal Health Chatbots through the Integration of Medical Text and Images Raditya Danar Dana; Mulyawan Mulyawan; Agus Bahtiar; Odi Nurdiawan
Jurnal Teknik Informatika (Jutif) Vol. 7 No. 2 (2026): JUTIF Volume 7, Number 2, April 2026
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2026.7.2.5328

Abstract

This study is motivated by the growing need for image-classification systems that remain accurate despite variations in image quality commonly found in real-world environments. Differences in image resolution often lead to decreased performance of Convolutional Neural Network (CNN) models, particularly in scenarios involving limited acquisition devices. This research aims to analyze the effect of image-resolution variations on CNN robustness by applying an adaptive augmentation strategy. An experimental approach was employed by manipulating independent variables namely image-resolution levels and augmentation techniques and observing their impact on accuracy, validation stability, and model generalization. The results show that medium-resolution images (128×128 px) combined with adaptive augmentation produce the best performance, yielding the highest validation accuracy and reduced overfitting compared to other configurations. The urgency of this study lies in its practical contribution to developing efficient image-classification models suitable for resource-constrained environments. Scientifically, the findings provide a structured mapping of the relationship between resolution, augmentation, and model stability, offering a foundation for designing more robust CNN architectures adaptable to real-world data variability.
Implementation of IndoBERT for Sustainability Impact Assessment in University Collaboration Information Systems Ryan Hamonangan; Raditya Danar Dana; Yudhistira Arie Wijaya; Odi Nurdiawan
Jurnal Teknik Informatika (Jutif) Vol. 7 No. 3 (2026): JUTIF Volume 7, Number 3, June 2026
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2026.7.3.5330

Abstract

University collaboration plays a critical role in enhancing institutional quality and supporting global sustainability agendas. However, many higher education institutions face challenges in managing Memorandum of Understanding (MoU), Memorandum of Agreement (MoA), and Implementation Agreement (IA) documents, particularly in monitoring implementation and assessing their alignment with sustainability goals. This study introduces a University Collaboration Information System enhanced with IndoBERT-based Natural Language Processing (NLP) to automate sustainability impact assessment. A synthetic corpus of 30 annotated collaboration documents was developed, covering multi-label Sustainable Development Goals (SDG) classification and span-level Named Entity Recognition (NER). Two approaches were evaluated: (1) baseline TF-IDF + Support Vector Machine (SVM) for SDG classification and rule-based NER, and (2) fine-tuned IndoBERT for both tasks. Experimental results show that IndoBERT significantly outperforms the baselines, achieving an average F1-score of 0.93 for SDG classification (+16.3%) and 0.96 for NER (+18.5%). The system integrates these models to generate automated entity extraction, sustainability dashboards, and document monitoring features. This work contributes to the advancement of informatics by demonstrating the effectiveness of Transformer-based NLP in processing institutional documents and by providing an integrated information-system framework that strengthens the role of NLP within the field of computer science.
Co-Authors Agus Bahtiar Mulyawan Mulyawan Raditya Danar Dana Ryan Hamonangan Yudhistira Arie Wijaya