Analisis Kinerja Algoritma Penjadwalan Disk SSTF Menggunakan Pendekatan Perhitungan Manual dan Simulasi Berbasis Web

Abstract

Disk scheduling algorithms play a crucial role in determining the performance of operating systems, particularly in managing disk input/output requests efficiently. One of the widely studied algorithms is Shortest Seek Time First (SSTF), which prioritizes disk requests based on the minimum seek distance from the current head position. This study aims to analyze the performance of the SSTF disk scheduling algorithm using two different analytical approaches, namely manual calculation and web-based simulation, in order to evaluate their effectiveness, consistency, and applicability under varying levels of problem complexity. The research employed an experimental method involving undergraduate students who implemented the SSTF algorithm using Microsoft Excel for manual calculations and a web-based Disk Scheduling Algorithms Visualizer for simulation. Several datasets with increasing levels of complexity were used to examine total head movement, seek time, and request servicing order. The results indicate that both approaches produce consistent and accurate outcomes for datasets with low to medium complexity. However, as the complexity increases, significant differences emerge in terms of time efficiency, consistency of results, and technical reliability. Manual calculation remains applicable across all datasets but requires more time and is prone to human error, while the web-based simulation offers faster and more consistent results but is limited by technical constraints when handling complex datasets. This study contributes to the field of operating systems by providing a methodological perspective on disk scheduling analysis, emphasizing that the choice of analytical approach significantly affects both the accuracy of performance evaluation and the learning process. The findings highlight the importance of integrating manual and simulation-based approaches to achieve more reliable analysis and effective learning outcomes.