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Jurnal Komputer Teknologi Informasi Sistem Komputer (JUKTISI)
Published by LKP Karya Prima Kursus
ISSN : 29623022     EISSN : 29637104     DOI : -
Core Subject : Science, Education, Engineering,
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Engineering
Focus dan scope dari JUKTISI (Jurnal Komputer Teknologi Informasi Sistem Komputer) terbit pertama kali pada tahun 2022 yang dimaksudkan sebagai media kajian ilmiah dari hasil pemikirian yang dituangkan kedalam Jurnal. Jurnal JUKTISI Lembaga Kursus dan Pelatihan Karya Prima terbit 3 (tiga) kali setahun pada bulan Februari, Juni dan September. Topik utama yang diterbitkan mencakup: 1. Teknologi Informasi 2. Sistem Komputer 3. Teknik Informatika 4. Sistem Informasi 5. Sistem Pendukung Keputusan 6. Sistem Pakar 7. Kecerdasan Buatan 8. Manajemen Informasi 9. Data Mining 10. Big Data 11. Jaringan Komputer 12. Dan lain-lain (topik lainnya yang berhubungan dengan Teknologi Informasi dan Komputer)
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 401 Documents
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Design and Implementation of Information Systems for Efficient Big Data Processing Apriadi, Deni; Anjani, Dewi; Risal, Andi Alviadi Nur; Triana, Yaya Sudarya; Mubarak, Husni
Jurnal Komputer Teknologi Informasi Sistem Komputer (JUKTISI) Vol. 5 No. 1 (2026): Juni 2026
Publisher : LKP KARYA PRIMA KURSUS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62712/juktisi.v5i1.1014

Abstract

The rapid growth of data volume, velocity, and variety has created significant challenges for traditional information systems, which are often unable to process large-scale data efficiently. This study aims to design and implement an efficient information system for big data processing using a distributed computing approach. The research adopts a systematic and experimental method consisting of system design, implementation, and performance evaluation. The proposed system is developed using a distributed architecture with parallel processing mechanisms to improve scalability and resource utilization. Performance evaluation is conducted using key metrics, including processing time, throughput, and efficiency improvement percentage, based on experimental testing with datasets ranging from 1 GB to 10 GB. The results show that the proposed system consistently reduces processing time and increases throughput compared to the baseline system. The system achieves efficiency improvements ranging from 33.3% to 36.9%, exceeding the predefined success indicator of 30%. These findings demonstrate that the integration of distributed computing and optimized system architecture significantly enhances big data processing performance. Therefore, the proposed system provides a scalable and practical solution for handling large-scale data processing in modern information systems.

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