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All Journal Journal of Applied Data Sciences Advance Sustainable Science, Engineering and Technology (ASSET)
Muhammad Bambang Firdaus
Institut Teknologi Sepuluh Nopember
Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Industrial & Manufacturing Engineering

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Benchmarking Graphics Rendering Capabilities: Java Processing vs. P5.js Muhammad Bambang Firdaus; Adi Surya Darma; Zainal Arifin; M. Khairul Anam; Muhammad Yusuf Halim; Arda Yunianta
Advance Sustainable Science Engineering and Technology Vol. 8 No. 1 (2026): November - January
Publisher : Science and Technology Research Centre Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/asset.v8i1.2036

Abstract

Rendering efficiency is a critical factor in cross-platform animation development. This study benchmarks the performance of Java Processing and P5.js by measuring frame rates and frame counts across six heterogeneous computing devices for 2D and 3D animation tasks. Each benchmark was executed under standardized conditions for 60 seconds, and performance data were collected at fixed intervals. Results indicate that Java Processing consistently achieves higher rendering efficiency, with up to 313% greater frame rates and 265% higher frame counts compared to P5.js, particularly in computationally intensive 3D scenarios. These differences are attributed to Java Processing’s compiled execution and direct OpenGL integration, while P5.js performance is constrained by browser-based execution and limited GPU utilization. The findings suggest Java Processing is preferable for high-performance simulations and complex visualizations, whereas P5.js remains effective for lightweight web-based 2D applications.
CLaGAtt: A Hybrid CNN-LSTM-GRU-Attention Model for Stunting Classification Based on Anthropometric Sequences Sofiansyah Fadli; Ahmad Tantoni; Novia Arista; M. Khairul Anam; Muhammad Bambang Firdaus
Journal of Applied Data Sciences Vol 7, No 3: September 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v7i3.1454

Abstract

Stunting is a chronic nutritional problem that requires accurate early identification because it affects child growth, cognitive development, and long-term human capital. This study adapts the CLaGAtt model, a hybrid CNN–LSTM–GRU–Attention architecture, for stunting classification using anthropometric sequence data. Rather than proposing a new deep learning architecture, the main contribution of this study lies in adapting the existing CLaGAtt framework through an integrated preprocessing pipeline, sequence construction strategy, class balancing using SMOTE, and an evaluation protocol specifically designed for stunting prediction. The preprocessing pipeline included irrelevant-column removal, data transformation, label encoding, standard scaling, class balancing using SMOTE, and sequence generation with a time step of five and a step of one. Three train–test split scenarios were evaluated, namely 90:10, 80:20, and 70:30. Experimental results showed that the 90:10 split produced the best performance, with 91.42% accuracy, 91.50% precision, 91.50% recall, and 91.43% F1-score. The 80:20 and 70:30 scenarios achieved 87.14% and 85.71% accuracy, respectively, indicating that larger training proportions improved model generalization in the available dataset. These findings suggest that the adapted CLaGAtt framework can effectively integrate convolutional feature extraction, sequential learning, and temporal attention for stunting classification from structured anthropometric data. Future work should validate the model on external datasets and integrate regional visualization to support priority intervention mapping.
Co-Authors Adi Surya Darma Ahmad Tantoni Anam, M Khairul Arda Yunianta Muhammad Yusuf Halim Novia Arista Sofiansyah Fadli Zainal Arifin