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All Journal RABIT: Jurnal Teknologi dan Sistem Informasi Univrab Journal of Innovation Information Technology and Application (JINITA)
Imam tahyudin
Amikom Purwokerto University
Computer Science & IT Decision Sciences, Operations Research & Management Engineering

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Automated Esophagitis Detection from Endoscopy Using Deep Learning Imam Kharits N; Imam tahyudin; Dhanar Intan Surya Saputra
Journal of Innovation Information Technology and Application (JINITA) Vol 7 No 2 (2025): JINITA, December 2025
Publisher : Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/jinita.v7i2.2835

Abstract

Gastro-esophageal reflux disease (GERD) is a widespread condition that often leads to severe complications, including esophagitis, which significantly affects patient health and quality of life. While endoscopy is the gold standard for diagnosing esophagitis, its reliance on specialized equipment and trained professionals can limit accessibility and efficiency. This study introduces an innovative approach to diagnosing esophagitis by leveraging Convolutional Neural Networks (CNN) for automated classification of endoscopic images. By utilizing the Kvasir dataset, which includes a comprehensive collection of gastrointestinal endoscopy images, the model is trained to distinguish between esophagitis and normal-Z-line conditions with remarkable accuracy. The CNN model achieved outstanding results, with an accuracy of 96.04%, precision of 98.94%, recall of 93.00%, and an F1-score of 95.88%, demonstrating its potential to outperform traditional diagnostic methods. These findings underscore the ability of CNN to not only enhance diagnostic precision but also to reduce human error, making the process faster, more reliable, and more accessible. This research contributes to the growing body of work in medical image analysis, suggesting that CNN-based models can transform clinical practices by supporting timely, accurate diagnoses while alleviating the burden on medical professionals. The integration of deep learning in this domain holds the promise of advancing healthcare accessibility and efficiency globally
PERANCANGAN APLIKASI INVENTORY BARANG BERBASIS WEBSITE SEBAGAI PENCATATAN BARANG PADA CV LANGGENG JAYA LESTARI: DESIGN OF A WEB-BASED INVENTORY APPLICATION FOR GOODS RECORDING AT CV LANGGENG JAYA LESTARI Nurika Sefira Lestari; Imam Tahyudin; Dinar Mustofa
Rabit : Jurnal Teknologi dan Sistem Informasi Univrab Vol 11 No 2 (2026): Juli
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36341/rabit.v11i2.8013

Abstract

This study aims to design and develop a web-based inventory system for CV Langgeng Jaya Lestari using the Design Thinking method, which consists of empathize, define, ideate, prototype, and test stages. The system was developed to improve manual inventory management into a more computerized and efficient process. The main features include recording incoming goods, outgoing goods, stock monitoring, and integrated inventory data management through a single dashboard. Data collection was carried out through observation and interviews with the company to identify system requirements. The results show that the inventory website improves efficiency and accuracy in managing stock data. System testing was conducted using alpha testing based on expert judgment involving the company leader of CV Langgeng Jaya Lestari and a website expert. The evaluation resulted in an average score of 85.14 out of 100, categorized as very good. This indicates that the system meets user requirements and is feasible to use, although further improvements are still needed for future development and optimization..
SYSTEMATIC LITERATURE REVIEW: PENGARUH KARAKTERISTIK DATASET TERHADAP EFEKTIVITAS VARIAN SYNTHETIC MINORITY OVERSAMPLING TECHNIQUE: SYSTEMATIC LITERATURE REVIEW: THE EFFECT OF DATASET CHARACTERISTICS ON THE EFFECTIVENESS OF SYNTHETIC MINORITY OVERSAMPLING TECHNIQUE VARIANTS Dwiki Likuisa; Imam Tahyudin; Berlilana
Rabit : Jurnal Teknologi dan Sistem Informasi Univrab Vol 11 No 2 (2026): Juli
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36341/rabit.v11i2.8200

Abstract

Class imbalance is a major challenge in machine learning, particularly in datasets with varying overlap and dimensionality. While SMOTE and its variants offer synthetic oversampling solutions, their comparative effectiveness across diverse data structures requires synthesis. This systematic literature review applies the PRISMA 2020 protocol using a single database source, ScienceDirect, analyzing 20 papers (2021 to 2025) that explicitly compare at least two SMOTE variants (SMOTE, Borderline-SMOTE, ADASYN, SMOTE-ENN) on public or well-documented benchmark datasets. Of 777 identified articles, a staged screening process yielded 20 papers meeting the inclusion criteria. Results show that SMOTE-ENN and noise-cleaning based approaches consistently outperform in multiclass datasets with high overlap, while ADASYN and adaptive-weighting approaches excel in datasets with extreme imbalance ratios but low overlap. Borderline-SMOTE and boundary-based variants provide stability under moderate imbalance with low-to-medium dimensionality. Furthermore, G-Mean, F1-Score, and AUC proved more informative than accuracy for imbalanced conditions. This review offers methodological recommendations for selecting oversampling strategies based on dataset characteristics.
Co-Authors Berlilana Dhanar Intan Surya Saputra Dinar Mustofa Dwiki Likuisa Imam Kharits N Nurika Sefira Lestari