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All Journal JITK (Jurnal Ilmu Pengetahuan dan Komputer) Jurnal Ilmiah Multidisiplin Indonesia
Shintami Chusnul Hidayati
Institut Teknologi Sepuluh November
Agriculture, Biological Sciences & Forestry Computer Science & IT Earth & Planetary Sciences Engineering Physics

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THE IMPACT OF COLOR AND CONTRAST ENHANCEMENT FOR DIAGNOSING GASTROINTESTINAL DISEASES BASED DEEP LEARNING Gregorius Guntur Sunardi Putra; Chastine Fatichah; Shintami Chusnul Hidayati; Rusdiyana Ekawati
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 11 No. 1 (2025): JITK Issue August2025
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v11i1.6229

Abstract

Endoscopy is a crucial tool for diagnosing digestive tract diseases—colon cancer and polyps using a camera with LED lighting, but often results in low-quality images with poor contrast and luminance. This study evaluates the performance of two contrast-based image quality enhancement—Contrast Limited Adaptive Histogram Equalization (CLAHE) and Improved Adaptive Gamma Correction with Weighting Distribution (IAGCWD)—along with various color space transformations (RGB, HSV, YCbCr, CIELAB, Grayscale) in deep learning-based digestive tract diseases detection system. The detection system using EfficientNetV2S model and Quadratic Weighted Kappa (QWK) loss function to obtain the balance of prediction results for each class. The experiment shows that CLAHE is able to achieve 79% accuracy which is superior in clarifying important information in endoscopy images. CLAHE performs well due to its ability to reduce noise and enhance contrast. The classification model with HSV and CLAHE on KVASIR is able to recognize all classes well. RGB, HSV, and YCbCr color spaces have stable performance in most tests. This study contributes insights for enhancing endoscopic image quality to support both computer-aided and clinical diagnosis.
Application of Hybrid CNN-LSTM Architecture with Optuna Optimization for Weather Image Captioning Sulaeman Salasa; Shintami Chusnul Hidayati; Muhamad Hilmil Muchtar Aditya Pradana
Jurnal Ilmiah Multidisiplin Indonesia (JIM-ID) Vol. 5 No. 03 (2026): Jurnal Ilmiah Multidisplin Indonesia (JIM-ID), March 2026
Publisher : Sean Institute

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Abstract

Automating the description of weather phenomena through visual imagery is a crucial step in supporting efficient meteorological monitoring systems. This study aims to compare the performance of two Deep Learning architectures, ResNet101-LSTM and VGG16-LSTM, in generating automatic image captions for various weather conditions. The research methodology involves extracting visual features using Residual Learning and VGG-Net, which are subsequently processed by Long Short-Term Memory (LSTM) units for text generation. Hyperparameter optimization was conducted using the Optuna framework to ensure both models operate at their peak configurations. The results indicate that ResNet101-LSTM provides superior linguistic accuracy, achieving a BLEU-1 score of 0.7553, a BLEU-4 score of 0.4593, and a METEOR score of 0.7264. Qualitatively, this model is capable of identifying environmental details with higher precision compared to VGG16-LSTM. However, loss curve analysis reveals that VGG16-LSTM demonstrates better convergence stability (good fit), whereas ResNet101-LSTM shows signs of slight overfitting. This study concludes that while ResNet101-LSTM is superior in accuracy according to standard NLP evaluation metrics, additional regularization techniques are required to maintain its performance stability on validation data.
Co-Authors Chastine Fatichah Gregorius Guntur Sunardi Putra Muhamad Hilmil Muchtar Aditya Pradana Rusdiyana Ekawati Sulaeman Salasa