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All Journal Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Informasi Interaktif Information System Journal (INFOS) Jurnal Informatika Teknologi dan Sains (Jinteks) Jurnal Pengabdian Mitra Masyarakat (JPMM) EXPLORE
Ibnu Hadi Purwanto
Universitas Amikom Yogyakarta
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Medicine & Pharmacology Social Sciences Other

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A Hybrid Round-Robin Scheduler for GPU Batch Rendering in Constrained Cloud Environments Ibnu Hadi Purwanto; Dhani Ariatmanto; M. Shahkhir Mozamir; Afifah Nur Aini
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 10 No 1 (2026): February 2026
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v10i1.7117

Abstract

Creating high-quality 2D and 3D assets is essential for digital content, but inefficient scheduling and inaccurate time estimates often hamper the rendering process. Traditional methods, which assume rendering time is directly proportional to frame count, fail to account for variations in scene complexity, resulting in severe estimation errors averaging 97.0% across all tasks. We propose a Hybrid Round-Robin Scheduler (HRRS) that intelligently manages batch rendering tasks through complexity-aware classification. Our method first categorizes tasks by complexity (Low, Medium, High) and routes them to appropriate queues with tiered quantum allocations. It then employs non-linear time estimation models and dynamically adjusts processing priorities based on real-time performance metrics. We evaluated our scheduler against standard algorithms—First-Come-First-Served (FCFS), Shortest Job First (SJF), and Round Robin (RR)—using 21 diverse rendering tasks with frame counts ranging from 10 to 420 frames. The results demonstrate that our approach reduces average waiting time by 45.9% (from 29.63s to 16.02s) and cuts bottleneck-induced delays by 78% (from 41s to 9s), while maintaining optimal CPU utilization at 85% and limiting context switches to only nine occurrences. A key finding reveals that complexity, rather than frame count, is the primary driver of processing time; high-complexity tasks required significantly longer processing (averaging 238.27 seconds) compared to medium-complexity tasks (averaging 34.52 seconds), representing a 6.9-fold performance differential. Our hybrid framework effectively overcomes the primary limitations of existing algorithms: it prevents bottlenecks from large tasks (FCFS), avoids the parallelism issues of SJF, and minimizes the performance overhead from frequent switching in Round Robin. This work provides a robust foundation for intelligent resource allocation in cloud rendering environments where task demands are variable and difficult to predict, establishing that effective scheduling requires complexity-aware algorithms rather than universal approaches.
A Hybrid Intersection Filtering and Recursive Feature Elimination Technique for Efficient Feature Reduction in High Dimensional Datasets Akhmad Dahlan; Yoga Pristyanto; Anggit Ferdita Nugraha; Rifda Faticha Alfa Aziza; Ibnu Hadi Purwanto
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 10 No 2 (2026): April 2026
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v10i2.7396

Abstract

High-dimensional datasets are commonly encountered in real-world machine learning applications and often degrade classification performance due to redundant and irrelevant features. In addition, the presence of excessive features increases computational complexity and processing time. Feature selection is therefore a crucial preprocessing step to improve model accuracy and efficiency. This study proposes a hybrid feature selection approach called Intersection Filtering based on Recursive Feature Elimination with Cross-Validation (IF-RFECV), which integrates wrapper-based and filter-based strategies to obtain a stable and optimal subset of features. The proposed method first applies Recursive Feature Elimination with Cross-Validation (RFECV) using multiple classification models to rank and select relevant features. Subsequently, an intersection filtering mechanism is employed to identify features that are consistently selected across different RFECV-based models, thereby reducing model-dependent bias and improving feature robustness. The effectiveness of IF-RFECV is evaluated using four benchmark datasets with varying dimensionality obtained from the KEEL and UCI repositories. Several classification algorithms, including Gradient Boosting, K-Nearest Neighbor, Naïve Bayes, Decision Tree, Random Forest, and Support Vector Machine, are used to assess model performance. Experimental results demonstrate that IF-RFECV produces a more compact feature subset compared to conventional RFECV while achieving superior performance in terms of accuracy, precision, recall, and F1-score on most datasets, particularly those with higher dimensionality. Although IF-RFECV requires slightly higher computational time due to its two-stage process, the performance gains and improved generalization justify this trade-off. These findings indicate that IF-RFECV is an effective and robust feature selection technique for high-dimensional classification problems.
PENDEKATAN DOUBLE DIAMOND DALAM PERANCANGAN ANTARMUKA PERSONAL ASSISTANT PADA WEBSITE EVENT ORGANIZER Rifda Faticha Alfa Aziza; Muhammad Agung Satriajaya Suryana; Afifah Nur Aini; Ibnu Hadi Purwanto
Information System Journal Vol. 8 No. 02 (2025): Information System Journal (INFOS)
Publisher : Universitas Amikom Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24076/infosjournal.2025v8i02.2575

Abstract

Perkembangan teknologi digital mendorong event organizer menyediakan layanan berbasis website yang informatif dan mudah digunakan. Namun, banyak website event organizer masih memiliki masalah antarmuka dan alur interaksi sehingga menyulitkan pengguna mencari informasi dan melakukan pemesanan. Penelitian ini bertujuan merancang antarmuka personal assistant pada website event organizer menggunakan metode Double Diamond. Tahapan penelitian meliputi discover, define, develop, dan deliver dengan pengumpulan data melalui observasi, wawancara, empathy map, dan user persona. Tahap pengembangan menghasilkan user flow, wireframe, dan prototipe high-fidelity. Evaluasi dilakukan melalui usability testing menggunakan Maze Design. Hasil pengujian menunjukkan usability score 80, success rate 100%, drop-off 0%, misclick rate 28,7%, dan durasi rata-rata 68,9 detik. Seluruh responden (100%) menyatakan fitur membantu.
Co-Authors Afifah Nur Aini Agus Purwanto Ahmad Fachry M Ahmad Nur Hanafi Ahmad Zaid Rahman Akhmad Dahlan Anugrah Adhi Pratama Pratama Dhani Ariatmanto Dhimas Dhimas Adi Satria Faizal Zulfi Ardiyanto Fatimah Hafizh Majid Ika Asti Astuti Jeki Kuswanto Khalida Arlinta Syamrahmarini Laila Qodarsih M. Shahkhir Mozamir Muhammad Agung Satriajaya Suryana Muhammad Arbi Hamam Nugraha, Anggit Ferdita Nur Asyifa Putu Kalpika Jayanti Rifda Faticha Alfa Aziza Saefudin Irsyad Safar Dwi Kurniawan Sukoco Sukoco Suyanto Suyanto Yoga Pristyanto Zainal Makhasin Zindy Abimanyu Risky