AR’RAFIKA, ATIKA NADAA WINDI (2025) IMPLEMENTASI ALGORITMA HUFFMAN CODING DAN LEMPEL-ZIV-WELCH (LZW) COMPRESSION DALAM OPTIMALISASI PENYIMPANAN CLOUD LINGKUNGAN BISNIS. S1 thesis, Universitas Mercu Buana Jakarta - Menteng.
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Abstract
Penelitian ini mengkaji implementasi algoritma kompresi Huffman Coding dan Lempel-Ziv-Welch (LZW) dalam optimalisasi penyimpanan cloud di lingkungan bisnis. Seiring meningkatnya volume data di era digital, efisiensi penyimpanan cloud menjadi kebutuhan mendesak, terutama bagi perusahaan yang berupaya mengurangi biaya operasional tanpa mengorbankan aksesibilitas dan kinerja sistem. Algoritma Huffman Coding dan LZW dipilih karena kemampuan mereka dalam mengurangi ukuran data secara signifikan melalui pendekatan kompresi lossless, yang menjaga integritas informasi. Penelitian ini menggunakan pendekatan kuantitatif dengan menganalisis dataset dari Kaggle yang mencakup berbagai jenis file seperti teks, gambar, dan dokumen bisnis. Dataset tersebut digunakan untuk menguji efektivitas kedua algoritma dalam mengompresi data, mengukur rasio kompresi, waktu eksekusi, dan efisiensi penyimpanan. Hasil penelitian ini diharapkan tidak hanya memperkaya literatur mengenai kompresi data dalam cloud, tetapi juga memberikan panduan praktis bagi perusahaan untuk mengoptimalkan biaya penyimpanan di tengah pesatnya pertumbuhan data. Urgensi penelitian ini terletak pada kebutuhan perusahaan untuk mempertahankan daya saing dengan efisiensi penyimpanan yang berkelanjutan di era big data. This research examines the implementation of the Huffman Coding and Lempel- Ziv-Welch (LZW) compression algorithms in optimizing cloud storage in a business environment. With the increasing volume of data in the digital era, efficient cloud storage has become an urgent necessity, particularly for companies seeking to reduce operational costs without compromising accessibility and system performance. Huffman Coding and LZW were chosen due to their ability to significantly reduce data size through a lossless compression approach, which preserves data integrity. The study employs a quantitative approach by analyzing a dataset from Kaggle, which includes various file types such as text, images, and business documents. This dataset is used to evaluate the effectiveness of both algorithms in compressing data, measuring compression ratios, execution times, and storage efficiency. The findings of this research are expected to not only enrich the literature on data compression in cloud environments but also provide practical guidance for companies to optimize storage costs amid the rapid growth of data. The urgency of this research lies in the need for businesses to maintain competitiveness through sustainable storage efficiency in the era of big data.
| Item Type: | Thesis (S1) |
|---|---|
| NIM/NIDN Creators: | 41521110015 |
| Uncontrolled Keywords: | Kompresi Data, Huffman Coding, LZW Compression, Penyimpanan Cloud, Python Data Compression, Huffman Coding, LZW Compression, Cloud Storage, Python |
| Subjects: | 000 Computer Science, Information and General Works/Ilmu Komputer, Informasi, dan Karya Umum > 000. Computer Science, Information and General Works/Ilmu Komputer, Informasi, dan Karya Umum > 004 Data Processing, Computer Science/Pemrosesan Data, Ilmu Komputer, Teknik Informatika |
| Divisions: | Fakultas Ilmu Komputer > Informatika |
| Depositing User: | Maulana Arif Hidayat |
| Date Deposited: | 09 Aug 2025 02:52 |
| Last Modified: | 09 Aug 2025 02:52 |
| URI: | http://repository.mercubuana.ac.id/id/eprint/96693 |
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