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All Journal Jurnal Ilmu Komputer dan Informasi MATICS : Jurnal Ilmu Komputer dan Teknologi Informasi (Journal of Computer Science and Information Technology) Jurnal EECCIS SMARTICS Journal Applied Information System and Management ILKOM Jurnal Ilmiah JISKa (Jurnal Informatika Sunan Kalijaga) Internet of Things and Artificial Intelligence Journal Jurnal ilmiah teknologi informasi Asia International Journal of Electrical and Intelligent Engineering
Ajib Hanani
Universitas Islam Negeri Maulana Malik Ibrahim Malang
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Energy Engineering Languange, Linguistic, Communication & Media Library & Information Science

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Endoscopic Image Classification Using ConvNeXt for GERD and Polyp Identification Muhammad Faqih; Okta Qomaruddin Aziz; Ajib Hanani
Jurnal Ilmu Komputer dan Informasi Vol. 19 No. 2 (2026): Jurnal Ilmu Komputer dan Informasi (Journal of Computer Science and Informatio
Publisher : Faculty of Computer Science - Universitas Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21609/jiki.v19i2.1702

Abstract

Early and accurate detection of gastrointestinal abnormalities, such as gastroesophageal reflux disease (GERD) and intestinal polyps, is essential for preventing severe clinical complications. However, manual interpretation of endoscopic images is often constrained by inter-observer variability and time limitations. This study proposes a ConvNeXt-Tiny-based deep learning framework for multi-class classification of gastrointestinal endoscopic images. Experiments were conducted using the GastroEndoNet v3 dataset, which contains 4,006 images categorized into four classes: GERD, GERD Normal, Polyp, and Polyp Normal. A total of twelve experimental scenarios were designed to systematically evaluate the effects of dataset-provided augmentation, ImageNet-based normalization, and batch size on model performance. The optimal configuration, combining augmentation, normalization, and a batch size of 64, achieved a test accuracy of 99.75% and a macro-averaged F1- score of 0.9977, indicating stable convergence and strong generalization on unseen data. The results demonstrate that ConvNeXt-Tiny effectively captures disease-relevant visual patterns in endoscopic images while maintaining consistent performance across varying training conditions. Comparative evaluation with a transformer-based baseline further indicates that modern convolutional architectures remain competitive for gastrointestinal image classification tasks. The proposed framework establishes a reliable and lightweight baseline for automated gastrointestinal disease detection. Extensions to video-based endoscopy would require incorporating temporal information across consecutive frames, which is beyond the scope of the current image-based study.
Improving Moodle Performance Using HAProxy and MariaDB Galera Cluster Johan Ericka Wahyu Prakasa; Ajib Hanani; Fajar Rohman Hariri; Shoffin Nahwa Utama
Applied Information System and Management (AISM) Vol. 7 No. 1 (2024): Applied Information System and Management (AISM)
Publisher : Depart. of Information Systems, FST, UIN Syarif Hidayatullah Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15408/aism.v7i1.34871

Abstract

Moodle is a widely used Learning Management System in various educational institutions worldwide. However, frequent reports on internet forums indicate performance degradation when massive simultaneous users access Moodle. One of the most resource-intensive components supporting Moodle is the database, as all user-accessed data is stored in it. This study aims to optimize Moodle’s performance through distributed databases. Distributing the database into multiple database servers allows the database load to be distributed across all the database servers, resulting in an overall improvement in Moodle performance. This study compares the performance of Moodle installed on a single server with that installed on multiple database servers. Various testing parameters are employed to get valid results. Namely, course read, course write, and database performance, utilizing the server performance plugin available in Moodle. This research reveals a performance improvement of 384% in course read, 193% in course write, and 260% in the Moodle database in the multi-server scenario compared to the single-server scenario. This result validates that the database is the most crucial part of Moodle.
Co-Authors Adi, Puput Dani Prasetyo Akbar, Galuh Muhammad Iman Aqza Tri Ananda HAT Cahyo Crysdian Fachrul Kurniawan Fajar Rohman Hariri Ganis Chandra Puspitadewi Harry Soekotjo Dachlan Harry Soepandi Johan Ericka Wahyu Prakasa Karisma, Alfiana Intan Kitagawa, Akio M. Imamudin Mohammad Faisal Fajar Fadilah Mokhamad Amin Hariyadi Muhammad Faisal Muhammad Faqih Muhammad Yassir Okta Qomaruddin Aziz Purnomo Budi Santoso Rito Putriwana Pratama Sastya Hendri Wibowo Setiawan, Dwi Risky Shoffin Nahwa Utama Shoffin Nahwa Utama Totok Chamidy Yunifa Miftachul Arif