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All Journal JUITA : Jurnal Informatika International Journal of Informatics and Computation Journal of Fuzzy Systems and Control (JFSC)
Hadi Santoso
Universitas Mercu Buana
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Energy Engineering

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Brain Tumor Detection and Classification Using Fine-Tuned CNN with ResNet50 and EfficientNet Muhammad Ali Sultan; Christopher Marco Angelo; Muhammad Alkam Alfariz; Dinda Fatimah Kautsarina; Dhani Amanda Putra; Muhammad Sharjil Ashfaq; Hadi Santoso; Genoveva Ferreira Sores
International Journal of Informatics and Computation Vol. 6 No. 1 (2024): International Journal of Informatics and Computation
Publisher : University of Respati Yogyakarta, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35842/ijicom.v6i1.80

Abstract

Brain tumors have become a leading cause of mortality worldwide. Detecting and classifying brain tumors accurately at the initial stages is challenging due to their complex and varying structure. In this study, an improved fine-tuned model based on Convolutional Neural Networks (CNN) with ResNet50 and U-Net is proposed. The model works on the publicly available TCGA-LGG and TCIA dataset, which consists of 120 patients. The fine- tuned ResNet50 model outperforms the CNN model in brain tumor classification and detection using MRI images. Accurate and timely diagnosis of brain tumors is critical for successful treatment of the disease. Early detection not only aids in the development of better medication, but it can also save a life in the long run. The domain of brain tumor analysis has efficiently applied medical image processing ideas, particularly on MR images. This paper presents segmentation using Convolutional Neural Networks (CNN) architecture with ResNet50 and EfficientNet as backbones.
Analisis Perbandingan Algoritma Machine Learning untuk Klasifikasi Potabilitas Air Tanah di Jakarta Diky Arianto Tarihoran; Hadi Santoso
JUITA: Jurnal Informatika JUITA Vol. 13 Issue 3, November 2025
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30595/juita.v13i3.27348

Abstract

Groundwater quality is a fundamental aspect of fulfilling clean water needs, particularly in urban areas such as Jakarta, which faces significant supply limitations due to severe contamination from domestic waste, chemical pollutants, industrial activities, and septic tank leakage. This study aims to compare the performance of nine machine learning algorithms in developing a classification model for groundwater feasibility based on physical parameters. Real-time data were collected from three administrative regions in Jakarta using Internet of Things (IoT) sensors, which monitored pH, temperature, total dissolved solids (TDS), and turbidity. Model evaluation involved hyperparameter tuning, cross-validation, feature importance analysis, LIME interpretation, and performance metrics including AUC, accuracy, precision, recall, and F1-score. The results indicate that CatBoost achieved the highest overall performance (AUC: 0.9448, accuracy: 0.9318, F1-score: 0.9209). LightGBM demonstrated competitive results with an F1-score of 0.9211 and AUC of 0.9431, while XGBoost recorded the highest recall at 0.9359. Random Forest and AdaBoost also exhibited consistent performance, with precision of 0.9094 and recall of 0.9327, respectively. In contrast, Support Vector Machine (SVM) yielded the lowest performance (AUC: 0.8860, accuracy: 0.8499). Based on a comprehensive evaluation, CatBoost model is recommended as the most suitable model for IoT-based groundwater quality classification systems.
Enhancing Geospatial House Price Prediction in Greater Jakarta Using XGBoost and ResNet18 Feature Fusion Hadi Santoso; Alif Biuti Anastasya
JUITA: Jurnal Informatika JUITA Vol. 14 Issue 1, March 2026
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30595/juita.v14i1.28582

Abstract

Precise house price predictions play a vital role in shaping housing policies and informing investment decisions in urban regions such as Greater Jakarta, encompassing Jakarta, Bogor, Depok, Tangerang, and Bekasi. Most models rely exclusively on structured data, ignoring spatial and environmental factors that influence property prices. This study proposes a multimodal machine learning framework that integrates structured property data with Sentinel-2 satellite imagery within a geospatial context. The baseline dataset consists of 17 tabular variables. The ResNet-18 algorithm extracts visual environmental information from satellite imagery. The integration of both modalities through a late fusion strategy results in improved predictive performance. The baseline XGBoost achieved R² scores of 0.81 (log scale) and 0.79 (Rupiah), with an error of about 184 million Rupiah. The image-only model achieved an R² of 0.43, indicating moderate explanatory capability. Late fusion further improved performance, achieving R² values of 0.94 (log scale) and 0.93 (Rupiah), while reducing prediction error by over 40%.
Dual-Stream MobileNetV2 and Light Mixer Fusion for Robust Weather Classification Muhammad Reza Alfatah; Hadi Santoso
Journal of Fuzzy Systems and Control Vol. 4 No. 2 (2026): Vol. 4 No. 2 (2026)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/jfsc.v4i2.406

Abstract

This study proposes an image-based weather classification model designed to accurately recognize various sky conditions, with the aim of providing accessible weather information for elderly individuals and users with visual impairments. While existing lightweight models often struggle to effectively capture both fine-grained local textures and global contextual patterns, this study bridges the gap by proposing a hybrid dual-branch architecture. Specifically, the proposed model utilizes an early spatial feature-level fusion dual-branch architecture. The first branch combines MobileNetV2 with a Feature Pyramid Network (FPN) and incorporates selective attention mechanisms to capture multi-scale features. The second branch, referred to as the Mixer branch, improves visual feature representation through patch embedding and feature mixing techniques. Outputs from both branches are integrated using a fusion layer before being processed by a softmax classifier. The dataset includes five weather categories: cloudy, foggy, rainy, sunny, and sunrise, and is preprocessed through normalization, data augmentation, and partitioning into training, validation, and testing sets. Model training is conducted using TensorFlow and Keras with the Adam optimizer over a two-phase training schedule of 60 epochs (20 epochs for head-only pre-training and 40 epochs for whole-model fine-tuning). The experimental evaluation achieves a test accuracy of 0.975 (97.50%), with precision, recall, and F1-score reaching 0.976, 0.975, and 0.975, respectively, reflecting consistent and reliable classification performance. These results indicate that the proposed model has strong potential for integration with text-to-speech systems to improve accessibility of weather information for users with special needs.
Co-Authors Alif Biuti Anastasya Christopher Marco Angelo Dhani Amanda Putra Diky Arianto Tarihoran Dinda Fatimah Kautsarina Genoveva Ferreira Sores Muhammad Ali Sultan Muhammad Alkam Alfariz Muhammad Reza Alfatah Muhammad Sharjil Ashfaq