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All Journal Building of Informatics, Technology and Science TIN: TERAPAN INFORMATIKA NUSANTARA
Teti Desyani
Universitas Pamulang, Tangerang Selatan
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Analisis Banker’s Algorithm untuk Penghindaran Deadlock Berbasis Simulasi Kuantitatif Multiskenario Christian Bastanta Sembiring Meliala; Teti Desyani; Moch Ibba Ali Yassin; Aldiansyah Sastrawinata; Mikael Surya Saputra; Brian Aidil Rizkita; Rizki Arohman Maulana
TIN: Terapan Informatika Nusantara Vol 7 No 1 (2026): June 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v7i1.10112

Abstract

Deadlock represents a critical threat in operating system resource management, as it has the potential to bring all computational processes to a complete halt. This study examines the effectiveness, efficiency, and constraints of the Banker's Algorithm as a deadlock avoidance mechanism through a multi-scenario quantitative simulation. The data were derived from simulations involving three core components: the resource allocation matrix (Allocation), the maximum process requirement declaration (Max), and the resource availability vector (Available), within a system configuration consisting of five processes and three resource types. The findings demonstrate that the Banker’s Algorithm accurately distinguishes between safe and unsafe states through its two primary mechanisms: the Safety Algorithm and the Resource-Request Algorithm. With Available set to [3, 3, 2], the algorithm successfully identified the safe execution sequence ⟨P1, P3, P4, P0, P2⟩, ensuring all processes could complete without deadlock risk. When Available was reduced to [2, 1, 0], the system entered an unsafe state in which no process could initiate execution. Through multi-scenario simulations, the critical transition threshold from a safe to an unsafe state was identified at approximately 83% resource utilization. In terms of efficiency, the O(n²×m) time complexity makes the algorithm well-suited for small to medium-scale systems, though it may become a performance bottleneck in large-scale cloud computing environments. This study produces a quantitative evaluation framework that can serve as a reference for implementing the Banker’s Algorithm in modern operating systems.
Analisis Pengaruh Chi-Square Feature Selection terhadap Kinerja Random Forest dan XGBoost dalam Prediksi Konversi Pengunjung Website Muhamad Rosdiana; Teti Desyani; Perani Rosyani
Building of Informatics, Technology and Science (BITS) Vol 8 No 1 (2026): June 2026
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v8i1.9662

Abstract

The increasing number of visitors to e-commerce websites is not always accompanied by a corresponding increase in purchase transactions, making it difficult for companies to identify visitors with high conversion potential. In addition, using all available attributes may increase model complexity without necessarily improving predictive performance. This study analyzes the impact of Chi-Square Feature Selection on the performance of Random Forest and Extreme Gradient Boosting (XGBoost) in predicting website visitor conversion. The study uses the Online Shoppers Purchasing Intention dataset consisting of 12,330 instances with 17 predictor attributes and one target attribute. The research process includes exploratory data analysis, preprocessing, Chi-Square-based feature selection, classification model development using Random Forest and XGBoost, and evaluation using Accuracy, Precision, Recall, F1-Score, Matthews Correlation Coefficient (MCC), and Area Under the Curve (AUC-ROC). Four experimental scenarios were evaluated: all features (baseline), Top-15, Top-10, and Top-5 selected features. The results show that the baseline model using all features achieved the best overall performance. The Random Forest baseline model obtained an Accuracy of 90.05%, Precision of 73.94%, F1-Score of 63.13%, and MCC of 0.5835, while the XGBoost baseline model achieved the highest AUC-ROC of 0.9271. Furthermore, PageValues, BounceRates, ExitRates, ProductRelated_Duration, and ProductRelated were identified as the most influential features affecting visitor conversion. The main contribution of this study is providing empirical evidence that Chi-Square Feature Selection is more effective in reducing feature complexity and identifying relevant attributes than improving classification performance on the Online Shoppers Purchasing Intention dataset, offering practical guidance for feature selection strategies in machine learning-based website conversion prediction
Co-Authors Aldiansyah Sastrawinata Brian Aidil Rizkita Christian Bastanta Sembiring Meliala Mikael Surya Saputra Moch Ibba Ali Yassin Muhamad Rosdiana Perani Rosyani Rizki Arohman Maulana