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All Journal INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi Advance Sustainable Science, Engineering and Technology (ASSET)
Arda Yunianta
Faculty of Computing, University Teknologi Malaysia
Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Industrial & Manufacturing Engineering

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Enhancing the Decision Tree Algorithm to Improve Performance Across Various Datasets Putra, Pandu Pratama; Anam, M Khairul; Defit, Sarjon; Yunianta, Arda
INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi Vol 8 No 2 (2024): August 2024
Publisher : Universitas Nusantara PGRI Kediri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29407/intensif.v8i2.22280

Abstract

Background: The Village Fund is an initiative by the central government to promote equitable regional development. However, it has also led to corruption. Many Indonesians share their opinions on the Village Fund on social media platforms like X, and news coverage is extensive on portals like detik.com. Objective: This study aims to classify data from social media and news coverage to enhance understanding. Methods: The research improves the decision tree algorithm by integrating other algorithms and techniques such as XGBoost and SMOTE. Ensuring high accuracy is vital for the credibility of machine learning classifications among the public. The study uses two different datasets, necessitating varied testing approaches. For the news portal dataset, a single test with seven labels is conducted, followed by enhancement with XGBoost. The X dataset undergoes two tests with datasets of 1200 and 3078 entries, using three labels. Conclusion: The evaluation results indicate that the highest accuracy achieved with the news portal data was 82%, thanks to a combination of decision tree algorithms with various parameters and the balancing effect of SMOTE. For the Twitter dataset with 3078 entries, the highest accuracy reached 95%, attributed to the application of ensemble techniques, particularly boosting.
Benchmarking Graphics Rendering Capabilities: Java Processing vs. P5.js Firdaus, Muhammad Bambang; Darma, Adi Surya; Arifin, Zainal; Anam, M. Khairul; Halim, Muhammad Yusuf; Yunianta, Arda
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.
Co-Authors Anam, M Khairul Darma, Adi Surya Firdaus, Muhammad Bambang Halim, Muhammad Yusuf Pandu Pratama Putra, Pandu Pratama Sarjon Defit Zainal Arifin