Paradigma Epistemologis Kompresi Data Teks: Huffman, Arithmetic, dan Neural Language Model
DOI:
https://doi.org/10.70609/jusifor.v4i2.8384Kata Kunci:
Kompresi teks, Epistemologi, Huffman coding, Arithmetic coding, Neural language modelAbstrak
Penelitian ini mengeksplorasi kompresi data teks sebagai paradigma epistemologis melalui analisis komparatif tiga pendekatan fundamental, yaitu metode tradisional (Huffman Coding + LZW), berbasis bit (Arithmetic Coding), dan pembelajaran mesin (Neural Language Model). Menggunakan dataset Project Gutenberg yang terdiri dari 15.000 literatur klasik dengan total ukuran 8,5 GB dan 2,1 miliar word-tokens, evaluasi dilakukan berdasarkan compression ratio, execution time, dan memory usage. Hasil menunjukkan trade-off fundamental antarparadigma. Metode tradisional memiliki eksekusi tercepat (8,3 detik/GB, 482 MB/s, 52 MB) dengan rasio 3,2:1. Arithmetic coding mencapai performa mendekati optimal (99,5% Shannon bound) dengan rasio 3,8:1. Neural language model menghasilkan rasio tertinggi 4,6:1, namun membutuhkan waktu dan memori jauh lebih besar. Analisis epistemologis menegaskan perbedaan konsepsi informasi mekanis, matematis-optimal, dan semantic-aware, serta memberikan kerangka konseptual bagi pengembangan sistem kompresi adaptif.
Referensi
[1] Abdulmonim, D. A., & Muhamad, Z. H. (2024). Improvement of Lossless Text Compression Methods using a Hybrid Method by the Integration of RLE, LZW and Huffman Coding Algorithms. International Journal of Software Engineering and Applications, 15(5), 2.
[2] Ahuja, N. A., Datta, P., Kanzariya, B., Somayazulu, V., & Tickoo, O. (2023). Neural rate estimator and unsupervised learning for efficient distributed image analytics in Split-DNN models. In 2023 IEEE CVPR (pp. 00201).
[3] Astsatryan, H., Lalayan, A., Kocharyan, A., & Hagimont, D. (2021). Performance-efficient recommendation and prediction service for Big Data frameworks focusing on data compression and in-memory data storage indicators. Scientific Programming, 22(4), 1945.
[4] Baidoo, P. K. (2023). Comparative analysis of the compression of text data using Huffman, arithmetic, run-length, and Lempel Ziv Welch coding algorithms. Journal of Advances in Mathematics and Computer Science, 38(9), 1812.
[5] Eldstål-Ahrens, A., Arelakis, A., & Sourdis, I. (2022). L2C: Combining lossy and lossless compression on memory and I/O. ACM Transactions on Architecture and Code Optimization, 19(1), 3481641.
[6] Leiderman, T., & Ben-Ezra, Y. (2024). Information Bottleneck driven deep video compression. Entropy, 26(10), 836.
[7] Liu, J., & Gulisano, V. (2025). On-demand memory compression of stream aggregates through reinforcement learning. In 2025 ACM SIGMOD (pp. 3676151).
[8] Mafmudin, M., & Harnaningrum, L. N. (2025). Improving memory efficiency on Android: Leveraging data structures for optimal performance. Jurnal Ilmiah Komputer, 9(3), 2131.
[9] Nguyen-Tang, T., & Choi, J. (2017). Markov information bottleneck to improve information flow in stochastic neural networks. Entropy, 21(10), 976.
[10] Rahman, M., & Hamada, M. (2020). Burrows-Wheeler Transform Based Lossless Text Compression Using Keys and Huffman Coding. Symmetry, 12(10), 1654.
[11] Saidutta, Y. M., Abdi, A., & Fekri, F. (2021). Analog joint source-channel coding for distributed functional compression using deep neural networks. In 2021 IEEE ISIT (pp. 9517797).
[12] Senthil, S., & Robert, L. (2011). Text compression algorithms - a comparative study. International Journal of Computer Theory and Engineering, 3(1), 1-7.
[13] Sharma, K., & Gupta, K. (2017). Lossless data compression techniques and their performance. In 2017 IEEE CCAA (pp. 8229810).
[14] Sharmiladevi, S., More, S., & Bose, H. (2025). Enhancing data compression techniques for optimization: A novel integration of Burrows-Wheeler Transform, Lempel-Ziv-Welch, run-length encoding and Huffman coding. In 2025 IEEE INCIP (pp. 11020095).
[15] Soflaei, M., Zhang, R., Guo, H., Al-Bashabsheh, A., & Mao, Y. (2023). Information bottleneck and aggregated learning. IEEE Transactions on Pattern Analysis and Machine Intelligence, 45(9), 3302150.
[16] Tunnicliffe, M., & Hunter, G. (2025). The classical model of type-token systems compared with items from the Standardized Project Gutenberg Corpus. Analytics, 4(2), 16.
[17] Venkatesh, V., Poorna, N., Vinay, C. S., Reddy, K. A., Raj, S., & Anushiadevi. (2024). Modified arithmetic coding to increase the compression rate of text data. In 2024 IEEE InC460750 (pp. 10649229).
[18] Wan, L., Alpcan, T., Kuijper, M., & Viterbo, E. (2024). Lightweight conceptual dictionary learning for text classification using information compression. IEEE Transactions on Knowledge and Data Engineering, 36(7), 3421255.
[19] Wang, Z., Lin, J., Aly, M., Young, S. I., Chandrasekhar, V., & Girod, B. (2021). Rate-distortion optimized coding for efficient CNN compression. In 2021 DCC (pp. 00033).
[20] Yang, Y., Mandt, S., & Theis, L. (2022). An introduction to neural data compression. Foundations and Trends® in Machine Learning, 15(2), 1-123. doi:10.1561/0600000107
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