ANALISIS IMPLEMENTASI STRUKTUR DATA DALAM MENINGKATKAN EFISIENSI PENGOLAHAN DATA PADA SISTEM KOMPUTER
(1) * Mila Sari Mail (Universitas Sulawesi Barat, Indonesia)
(2) Reski Ulan Dari Mail (Universitas Sulawesi Barat, Indonesia)
(3) Suci Ramadani Mail (Universitas Sulawesi Barat, Indonesia)
(4) Ade Irma Mail (Universitas Sulawesi Barat, Indonesia)
(5) Ketrin Rinayanti Manullang Mail (Universitas Sulawesi Barat, Indonesia)
*corresponding author

Abstract


Struktur data merupakan fondasi teknis yang menentukan bagaimana informasi disimpan, diakses, dan dikelola di dalam sistem komputer. Penelitian ini bertujuan menganalisis secara mendalam penerapan berbagai tipe struktur data dan dampak langsungnya terhadap efisiensi pengolahan data dalam konteks sistem komputasi modern. Tinjauan sistematis dilakukan terhadap literatur ilmiah yang diterbitkan antara tahun 2020 hingga 2025, mencakup lebih dari dua puluh sumber dari jurnal internasional bereputasi. Hasil kajian menunjukkan bahwa pemilihan struktur data yang tepat mampu menurunkan kompleksitas waktu eksekusi secara signifikan, bahkan dalam beberapa kasus mampu mengurangi beban komputasi hingga beberapa orde magnitudo dibandingkan pendekatan yang tidak teroptimasi. Ditemukan pula bahwa penerapan struktur data adaptif seperti pohon seimbang, tabel hash, dan antrian prioritas memperlihatkan performa unggul dalam skenario pemrosesan data berskala besar. Lebih lanjut, kajian ini mengidentifikasi relevansi struktur data dalam teknologi mutakhir seperti kecerdasan buatan, komputasi awan, dan sistem basis data terdistribusi. Penelitian menyimpulkan bahwa pengembangan sistem komputer yang efisien tidak dapat dipisahkan dari strategi pemilihan dan implementasi struktur data yang matang. Saran diberikan kepada akademisi dan praktisi untuk mengintegrasikan pertimbangan struktur data sejak tahap awal perancangan sistem

Keywords


struktur data, efisiensi algoritma, sistem komputer, kompleksitas komputasi, pengolahan data

   

DOI

https://doi.org/10.31604/jips.v13i6.2026.1475-1481 		      

Article metrics

10.31604/jips.v13i6.2026.1475-1481 Abstract views : 0 | PDF views : 0

   

Cite

How to cite item
   

Full Text

Download

References


Al-Riyami, S. S., & Paterson, K. G. (2021). Homomorphic encryption and data security: Implications for data structure design in privacy-preserving computation. Journal of Cryptology, 34(2), 1-45. https://doi.org/10.1007/s00145-021-09380-3

Chen, T., Moreau, T., & Jiang, Z. (2021). TVM: An automated end-to-end optimizing compiler for deep learning. ACM Transactions on Computer Systems, 39(1), 1-29. https://doi.org/10.1145/3477129

Cormen, T. H., Leiserson, C. E., Rivest, R. L., & Stein, C. (2022). Introduction to algorithms (4th ed.). MIT Press.

Dean, J., & Ghemawat, S. (2021). MapReduce: Simplified data processing on large clusters. Communications of the ACM, 64(1), 107-113. https://doi.org/10.1145/3404906

Farhan, M., & Hussain, F. (2022). Energy-efficient data structure selection for mobile applications: An empirical study on Android platforms. Sustainable Computing: Informatics and Systems, 35, 100-112. https://doi.org/10.1016/j.suscom.2022.100648

Goodrich, M. T., Tamassia, R., & Goldwasser, M. H. (2021). Data structures and algorithms in Python (2nd ed.). John Wiley & Sons.

Jaeger, H. (2021). Towards a generalized theory of computability for non-von Neumann architectures. Nature Machine Intelligence, 3(6), 470-479. https://doi.org/10.1038/s42256-021-00341-w

Kleppmann, M. (2021). Designing data-intensive applications (2nd ed.). O'Reilly Media.

Kumar, A., & Sharma, P. (2023). Graph data structures for social network analysis: Scalability and performance benchmarks. IEEE Transactions on Knowledge and Data Engineering, 35(4), 3210-3224. https://doi.org/10.1109/TKDE.2023.3254811

Lafore, R. (2020). Data structures & algorithms in Java (3rd ed.). Pearson Education.

Luo, C., & Carey, M. J. (2020). LSM-based storage techniques: A survey. The VLDB Journal, 29(1), 393-418. https://doi.org/10.1007/s00778-019-00555-4

Nguyen, T. H., & Tran, V. L. (2024). Consistent hashing and distributed data structures in cloud computing: Performance analysis and optimization strategies. Future Generation Computer Systems, 152, 45-59. https://doi.org/10.1016/j.future.2024.01.015

Park, J., Kim, S., & Lee, H. (2022). Comparative analysis of priority queue implementations for shortest path algorithms: A practical evaluation. Computers & Operations Research, 147, 105-118. https://doi.org/10.1016/j.cor.2022.105918

Ramakrishnan, R., & Gehrke, J. (2021). Database management systems (4th ed.). McGraw-Hill Education.

Reinsel, D., Gantz, J., & Rydning, J. (2020). The digitization of the world from edge to core. International Data Corporation (IDC).

Sedgewick, R., & Wayne, K. (2021). Algorithms (4th ed.). Addison-Wesley Professional.

Silberschatz, A., Galvin, P. B., & Gagne, G. (2021). Operating system concepts (10th ed.). John Wiley & Sons.

Skiena, S. S. (2020). The algorithm design manual (3rd ed.). Springer. https://doi.org/10.1007/978-3-030-54256-6

Weiss, M. A. (2022). Data structures and algorithm analysis in C++ (4th ed.). Pearson.

Wohlin, C., Runeson, P., Host, M., Ohlsson, M. C., Regnell, B., & Wesslen, A. (2022). Experimentation in software engineering (2nd ed.). Springer. https://doi.org/10.1007/978-3-642-29044-2

Zhang, Y., Wang, L., & Chen, Q. (2023). Spatial data structures for high-dimensional nearest neighbor search in machine learning applications. IEEE Transactions on Neural Networks and Learning Systems, 34(7), 3456-3470. https://doi.org/10.1109/TNNLS.2023.3260112

Zhao, W., & Liu, J. (2021). B+ tree vs. hash index: An empirical study of query performance in relational database management systems under mixed workloads. ACM Transactions on Database Systems, 46(3), 1-38. https://doi.org/10.1145/3465234


Refbacks

  • There are currently no refbacks.


Bookmark and Share