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All Journal Jurnal Ilmiah Kursor Journal of Applied Data Sciences
Denisa Septalian Alhamda
university of Pembangunan Nasional Veteran Jawa Timur
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management

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Optimization EfficientNetV2 model variant using Grad-CAM for multiple MRI brain tumor classification Denisa Septalian Alhamda; Wahyu Syaifullah J; Prasetyaning Estu Pratiwi; Surjo Hadi; Wan Suryani Wan Awang; I Gede Susrama Mas Diyasa
Jurnal Ilmiah Kursor Vol. 13 No. 3 (2026)
Publisher : Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21107/kursor.v13i3.428

Abstract

Fast and accurate diagnosis plays a critical role in effectively treating brain tumors. This study optimized and evaluated the EfficientNetV2 architecture through transfer learning, fine-tuning, and data augmentation, using three variants Small, Medium, and Large to classify MRI images into four categories: glioma, meningioma, pituitary tumors, and no tumor. Grad-CAM visualization was employed to enhance interpretability, providing a clear view of the critical regions in the MRI images that influenced the model’s decisions. Grad-CAM was tested across all model variants, and the best results were observed with EfficientNetV2-Large, where the model successfully highlighted the key areas associated with brain tumors. Among the variants, EfficientNetV2-Large achieved the best performance, with 99.85% accuracy, 99.60% precision, 99.65% recall, and 99.50% F1-score. However, this model required the longest computation time of 288 seconds per step, which may not be feasible in resource- constrained environments. Overall, this study underscores the potential of EfficientNetV2 models in revolutionizing brain tumor diagnosis by balancing accuracy, efficiency, and interpretability through advanced optimization techniques.Key words: Brain tumors, MRI classification, EfficientNetV2, Grad-CAM, Deep learning.
Optimizing The XGBoost Model with Grid Search Hyperparameter Tuning for Maximum Temperature Forecasting Sugiarto Sugiarto; I Gede Susrama Mas Diyasa; Denisa Septalian Alhamda; Rangga Laksana Aryananda; Allan Ruhui Fatmah Sari; Hanifudin Sukri; Deshinta Arrowa Dewi
Journal of Applied Data Sciences Vol 6, No 4: December 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i4.885

Abstract

This study presents a novel comparative approach to maximum temperature forecasting in Surabaya, Indonesia, by integrating Extreme Gradient Boosting (XGBoost) with Grid Search Hyperparameter Tuning and benchmarking it against Autoregressive Integrated Moving Average (ARIMA) and Neural Prophet models. The main idea is to evaluate the capability of XGBoost in capturing nonlinear patterns in environmental time series data, which traditional models often fail to address. Using 15,388 historical daily maximum temperature records from the BMKG Juanda weather station spanning 1981–2022, the objective is to identify the most accurate predictive model for short- and medium-term forecasts. The modeling process involved four stages: data acquisition, preprocessing, training, and evaluation, with performance assessed using Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE). The findings show that, after hyperparameter tuning, XGBoost achieved the best performance with MAE = 0.32 and RMSE = 0.65, outperforming ARIMA (MAE = 0.85, RMSE = 1.20) and Neural Prophet (MAE = 0.70, RMSE = 0.98). Prediction results for 2025 indicate peak maximum temperatures in January, October, and November, aligning with recent climate patterns. The contribution of this research lies in demonstrating the superiority of a tuned XGBoost model for complex environmental datasets, offering a practical tool for urban climate planning, agricultural scheduling, and heatwave risk mitigation. The novelty of this work is the systematic integration of Grid Search-based optimization with XGBoost for meteorological forecasting in a tropical urban context, producing higher accuracy than both classical statistical and modern hybrid time series methods. These results highlight the model’s adaptability and potential for broader climate-related applications, with future research recommended to incorporate additional meteorological variables such as humidity and wind speed for even greater predictive capability.
Co-Authors Allan Ruhui Fatmah Sari Deshinta Arrowa Dewi Hanifudin Sukri I Gede Susrama Mas Diyasa I Gede Susrama Mas Diyasa Prasetyaning Estu Pratiwi Rangga Laksana Aryananda Sugiarto Sugiarto Surjo Hadi Wahyu Syaifullah J Wan Suryani Wan Awang