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All Journal International Journal of Health, Engineering and Technology Nuansa Informatika
Dita Madonna Simanjuntak
Universitas Kristen Indonesia
Agriculture, Biological Sciences & Forestry Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Dentistry Engineering Health Professions Immunology & microbiology Industrial & Manufacturing Engineering Mechanical Engineering Medicine & Pharmacology Nursing Public Health Veterinary

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Web-Based IT Helpdesk System with Naive Bayes Automatic Ticket Classification Using Laravel: Sistem Helpdesk TI Berbasis Web dengan Klasifikasi Tiket Otomatis Naive Bayes Menggunakan Laravel Gallen Cakra Adhi wibowo; Dita Madonna Simanjuntak; Henoch Juli christanto
NUANSA INFORMATIKA Vol. 20 No. 2 (2026): Nuansa Informatika 20.2 July 2026
Publisher : FKOM UNIKU

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25134/ilkom.v20i2.626

Abstract

Managing IT support requests in a university environment remains largely manual, leading to untracked ticket backlogs, slow response times, and misrouted requests caused by incorrect manual categorization. This study presents the design and implementation of a web-based IT Helpdesk Ticketing System integrated with an automatic ticket category classification module using Multinomial Naive Bayes with TF-IDF feature extraction, developed for Universitas Kristen Indonesia (UKI) using the Waterfall SDLC on the Laravel 10 framework. The novelty of this work lies in the real-time integration of a Python-based Naive Bayes microservice directly into the Laravel ticket submission workflow. The classifier was trained on 450 domain-labeled IT support tickets across five categories and evaluated using 5-fold cross-validation (mean accuracy 88.7% +/- 0.9%) and a held-out test set (accuracy 88.9%, Macro-F1 87.6%), outperforming SVM (85.6%) and KNN (81.5%). Black-Box testing yielded 100% pass rate. ISO 9241-11 usability evaluation (n=25) produced effectiveness 95%, efficiency 92%, SUS 76.25 (Grade B)
Comparison Of CNN, Resnet 50, And Vgg 16 For Pneumonia Classification Using Transfer Learning Gallen Cakra Adhi Wibowo; Dita Madonna Simanjuntak; Henoch Juli Christanto
International Journal of Health Engineering and Technology Vol. 5 No. 2 (2026): Vol 5. No. 2 JULY 2026
Publisher : CV. AFDIFAL MAJU BERKAH

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55227/ijhet.v5i2.978

Abstract

Pneumonia is one of the leading causes of death from infectious diseases worldwide, making rapid and accurate radiological diagnosis crucial for successful medical treatment. This study implemented and compared three deep learning architectures—a custom Convolutional Neural Network (CNN), ResNet50, and VGG16—for binary classification of chest X-ray images into Normal and Pneumonia categories. The Chest X-ray Pneumonia dataset from Kaggle (5,863 images) was used with an 80/10/10 (train/validation/test) data split and data augmentation to address class imbalance. ResNet50 with transfer learning from ImageNet weights achieved the best performance: 95.1% accuracy, 92.3% precision, 96.7% recall, 94.4% F1-score, and 97.5% AUC-ROC, outperforming the custom CNN (89.4% accuracy, 95.2% AUC) and VGG16 (93.7% accuracy, 96.1% AUC). Statistical analysis confirmed that the performance difference between ResNet50 and the custom CNN was statistically significant (p < 0.05). The results showed that residual learning on ResNet50 effectively addressed the vanishing gradient problem in deep networks and achieved clinically relevant classification accuracy, supporting its potential integration into computer-aided diagnosis (CAD) systems.
Co-Authors Gallen Cakra Adhi Wibowo Gallen Cakra Adhi wibowo Henoch Juli Christanto Henoch Juli Christanto