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All Journal JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Journal of System and Computer Engineering
Kunaefi, Aang
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Computer Science & IT Decision Sciences, Operations Research & Management Education

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Journal : Journal of System and Computer Engineering

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Parameter Optimization Supports Vector Machine Using Genetic Algorithms to Improve the Efficiency of Data Transfer Prediction on Google Cloud Namruddin, Respaty; Mahendra, Ricky; Kunaefi, Aang; Bakar, Ramlah Abu
Journal of System and Computer Engineering Vol 6 No 1 (2025): JSCE: January 2025
Publisher : Universitas Pancasakti

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61628/jsce.v6i1.1636

Abstract

Efisiensi transfer data merupakan elemen kunci dalam infrastruktur cloud seperti Google Cloud. Penelitian ini bertujuan untuk meningkatkan akurasi prediksi efisiensi transfer data menggunakan Support Vector Machine (SVM) yang dioptimasi dengan Algoritma Genetika (GA). Dataset berisi informasi tentang ukuran file, latensi jaringan, utilisasi server, dan waktu transfer data. Algoritma Genetika diterapkan untuk mencari parameter optimal, yaitu nilai C dan gamma. Hasil penelitian menunjukkan bahwa optimasi parameter menggunakan GA mampu meningkatkan akurasi prediksi hingga 90%, dibandingkan metode tradisional Grid Search yang mencapai akurasi maksimal sebesar 88%.
Performance Analysis of API in Google Cloud Storage Service Integration Namruddin, Respaty; Sam, Rafiqa Mulia Indah Sari; Syamsuddin, Rajul Waahid; A, Amiruddin; Kunaefi, Aang
Journal of System and Computer Engineering Vol 6 No 1 (2025): JSCE: January 2025
Publisher : Universitas Pancasakti

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61628/jsce.v6i1.1637

Abstract

Google Cloud Storage (GCS) is one of the leading cloud storage services that supports large-scale data management through API integration. APIs allow applications to upload, download, and manage data in real-time. This study aims to analyze the performance of APIs in integration with GCS using response time, throughput, and latency parameters. Tests were conducted on various scenarios, including massive data transfer, distributed data management, and caching usage. The results showed that the average API response time reached 120 ms under normal conditions and increased to 180 ms under high load. Throughput reached an average of 400 MB/s, but decreased when the number of simultaneous requests increased. The average server latency was recorded at 60 ms and can be optimized with caching technology. Implementation of strategies such as Content Delivery Network (CDN) and request header optimization can improve performance by up to 30%. This study provides practical guidance for developers to optimally utilize GCS APIs in large-scale data management.
Co-Authors Amiruddin Amiruddin Bakar, Ramlah Abu Mahendra, Ricky Namruddin, Respaty Ririen Kusumawati Sam, Rafiqa Mulia Indah Sari Syamsuddin, Rajul Waahid Zainal Abidin