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All Journal Pendas : Jurnah Ilmiah Pendidikan Dasar Jatilima : Jurnal Multimedia Dan Teknologi Informasi Jurnal Teknologi Sistem Informasi dan Sistem Komputer TGD Proceedings of The ICECRS Ulead : Jurnal E-pengabdian Jurnal Masyarakat Indonesia
Pristiwanto
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Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Education Languange, Linguistic, Communication & Media Mathematics Other

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Journal : Pendas : Jurnah Ilmiah Pendidikan Dasar

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ANALISIS PERBANDINGAN ALGORITMA LEMPEL ZIV WELCH DAN TABOO CODES DALAM KOMPRESI FILE MKV Ridho, Faisal; Pristiwanto; Siregar, Saidi Ramadan
Pendas : Jurnal Ilmiah Pendidikan Dasar Vol. 9 No. 04 (2024): Volume 09, Nomor 04, Desember 2024
Publisher : Program Studi Pendidikan Guru Sekolah Dasar FKIP Universitas Pasundan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23969/jp.v9i04.18046

Abstract

When selecting the most efficient compression method, it is crucial to compare the available algorithms. The methods used for file compression vary, including algorithms such as LZW (Lempel Ziv Welch) and Taboo Codes. Large file sizes can cause various issues, such as increased storage requirements and longer transmission times, leading to higher costs. Compression is essential as it reduces file size, and different compression algorithms, like LZW and Taboo Codes, offer various benefits. Both algorithms are lossless compression techniques. The criteria used to determine which algorithm is more efficient for compressing MKV files include Compression Ratio, Ratio Compression, Redundancy, and Space Saving. The Exponential Method is used to analyze and compare these algorithms to understand their effectiveness, highlighting their respective strengths and weaknesses. After weighting each criterion using the exponential method, Lempel Ziv Welch achieved a score of 8.7926, while Taboo Codes achieved a score of 9.48313. Based on these results, it can be concluded that Lempel Ziv Welch requires less effort compared to Taboo Codes.
ANALISIS PERBANDINGAN ALGORITMA ARITHMETIC CODING DAN LEMPEL ZIV WELCH (LZW) DALAM MENGKOMPRESI FILE AUDIO MP3 Arief, Mhd. Abrar; Pristiwanto; Siregar, Saidi Ramadan
Pendas : Jurnal Ilmiah Pendidikan Dasar Vol. 9 No. 04 (2024): Volume 09, Nomor 04, Desember 2024
Publisher : Program Studi Pendidikan Guru Sekolah Dasar FKIP Universitas Pasundan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23969/jp.v9i04.18048

Abstract

MP3 audio files are a common format for storing and transmitting audio data, but file size can impact delivery time and memory capacity requirements. Compressing MP3 files is crucial for efficient transfer and storage space savings. Two popular lossless compression algorithms, namely arithmetic coding and Lempel-Ziv-Welch (LZW), play a significant role in determining how well MP3 audio files can be compressed without significant loss of quality. A comparative analysis between these two algorithms using criteria such as Compression Ratio, Ratio Compression, Redundancy, and Space Saving with the help of exponential methods indicates that arithmetic coding and LZW have total values of 10.1127 and 9.6047, respectively. This suggests that while arithmetic coding may be more efficient in terms of compression, LZW requires less effort. This comparison provides insights into the advantages and disadvantages of each algorithm, aiding in the selection of the most effective compression method for MP3 audio files.
OPTIMALISASI PENILAIAN KELAYAKAN KREDIT DENGAN ALGORITMA BACKPROPAGATION Lubis, Mai Sarah Nur; Ramadhani, Putri; Pristiwanto
Pendas : Jurnal Ilmiah Pendidikan Dasar Vol. 9 No. 04 (2024): Volume 09, Nomor 04, Desember 2024
Publisher : Program Studi Pendidikan Guru Sekolah Dasar FKIP Universitas Pasundan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23969/jp.v9i04.18159

Abstract

The implementation of the backpropagation algorithm in customer loan eligibility using Artificial Neural Networks (ANN) with the backpropagation method represents an effective approach for evaluating and predicting loan eligibility. This algorithm allows the use of historical data to train a model capable of identifying complex patterns in customer data, including payment behavior, credit history, and other relevant factors. By leveraging this technique, financial institutions can enhance accuracy in assessing credit risk, optimize loan decisions, and reduce the risk of non-performing loans. Loan eligibility assessment is a crucial aspect in the banking industry for minimizing credit risk and increasing profitability. This study proposes a loan eligibility assessment method using the Backpropagation algorithm within Artificial Neural Networks (ANN). The method aims to identify customer eligibility based on historical data and related features such as income, credit history, and age. The research findings indicate that the ANN Backpropagation model can provide accurate predictions of customer loan eligibility with lower error rates compared to traditional methods. These findings suggest that applying the ANN Backpropagation algorithm can enhance the effectiveness of the credit assessment process, minimize credit default risk, and potentially optimize loan decisions in the banking industry.
Co-Authors Arief, Mhd. Abrar Azlan Barus, Apriwati Emy Rosnawati Faisal Ridho, Faisal Furqan Hery Sunandar Lubis, Mai Sarah Nur Matias Julyus Fika Sirait Maulidina Muhammad Amin Muhammad Amin Putri Ramadhani, Putri Saidi Ramadan Siregar Saidi Ramadan Siregar Siagian, Novriadi Antonius Simangunsong, Pandi Barita Nauli Sitorus, Syahrial Sri Budi Purwaningsih, Sri Budi Taufany, Firdaus Enov At Tuti Andriani Zain, Nur Khofifah