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Journal : Pendas : Jurnah Ilmiah Pendidikan Dasar

ANALISA PERBANDINGAN ALGORITMA INTERPOLATIVE CODING DAN RICE CODE UNTUK KOMPRESI FILE AUDIO (MP3) Simanungkalit, Lidya Indah Pratama; Saputra, Imam; Ramadhani, Putri
Pendas : Jurnal Ilmiah Pendidikan Dasar Vol. 9 No. 04 (2024): Volume 09, No. 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.v9i4.17847

Abstract

This study aims to apply the interpolative coding and rice code algorithms for compressing MP3 audio files and to compare the effectiveness of these two algorithms in the compression process. Additionally, the study focuses on measuring the compression ratios of each algorithm to evaluate the efficiency of each method in reducing MP3 file sizes. As the use of applications for information management becomes increasingly common, a frequent issue arises: the need for significant storage space. Therefore, compression is required to reduce the size of these files. File compression is the process of transforming a set of data into a coded format to minimize storage requirements. There are several data compression algorithms, including Interpolative Coding and Rice Code. The multitude of compression algorithms creates uncertainty about which algorithms are most accurate for data compression. Thus, a comparison between algorithms, such as Interpolative Coding and Rice Code, is necessary. This comparison will help determine which algorithm is more accurate for compressing MP3 audio files, using parameters such as compression ratio and space saving of the Interpolative Coding and Rice Code algorithms.
PENERAPAN ALGORITMA STOUT CODE UNTUK MENGKOMPRESI FILE VIDEO BERFORMAT MP4 Hondro, Piter Saputra; Saputra, Imam; Ramadhani, Putri
Pendas : Jurnal Ilmiah Pendidikan Dasar Vol. 9 No. 04 (2024): Volume 09, No. 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.v9i4.17863

Abstract

Currently, numerous MP4 video files are widely distributed and often have relatively large sizes. To address storage issues, compression is needed to reduce file sizes. The aim of this research is to implement the Stout Codes algorithm for compressing MP4 video files, analyze the compression level achieved by this algorithm, and evaluate its performance in terms of compression ratio and video quality after compression. This study seeks to explore the effectiveness of Stout Codes in reducing MP4 file sizes while maintaining optimal video quality, providing insights into how this algorithm can be applied in video management and storage. Data compression is the process of transforming data into a more efficient code to save storage space. Various algorithms are used in the compression process, one of which is the Stout Codes algorithm, which is relatively uncommon. Stout Codes work by encoding video blocks into smaller binary forms without losing original information. Applying this algorithm to MP4 video files results in a Compression Ratio of 5.62%, meaning the file size before compression, which is 128 bits, can be reduced to 72 bits after compression. Thus, the Stout Codes algorithm offers an effective solution for reducing video file sizes while preserving data integrity, despite its less frequent use compared to other compression algorithms.
ANALISA PERBANDINGAN ALGORITMA PREFIX CODE DENGAN ALGORITMA BURROWS-WHEELER TRANSFORM DALAM KOMPRESI FILE VIDEO Hardianti, Putri Delfi; Saputra, Imam; Aripin, Soeb
Pendas : Jurnal Ilmiah Pendidikan Dasar Vol. 9 No. 04 (2024): Volume 09, No. 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.v9i4.17912

Abstract

A video file is a type of file recorded or stored in a digital format that contains visual and audio data, including moving images, sound, and text. The large size of video files often causes issues in storage and data transmission, especially for long-duration videos. Storage media such as Google Drive and cloud storage are used to address storage space needs, but these solutions are often not efficient enough. Therefore, data compression techniques are required to reduce file size without losing important information. To address this issue, this research implements two data compression algorithms: Prefix Code and Burrows-Wheeler Transform. The Prefix Code algorithm uses a unique binary encoding method for each symbol in the data, while the Burrows-Wheeler Transform performs a text data transformation to produce repetitive patterns that are easier to compress. The aim of this research is to compare the effectiveness of these two algorithms in compressing video files, focusing on the parameters of Compression Ratio (CR), Ratio of Compression (RC), and Space Saving (SS). The results indicate that both algorithms are effective in compressing video files. However, a comparison between the algorithms shows significant differences in compression performance. The Prefix Code algorithm proves to be more efficient in reducing file size without compromising data quality, while the Burrows-Wheeler Transform algorithm shows advantages in maintaining data integrity during the transformation process. This analysis provides deeper insights into the effectiveness of both algorithms and can assist in choosing the most appropriate compression technique for video files.
PENGENALAN POLA BUNGA BERBASIS CITRA MENGGUNAKAN JARINGAN SARAF TIRUAN DENGAN ALGORITMA PERCEPTRON Fahrezi, Azrial; Saputra, Imam; Siregar, Annisa Fadillah
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.v9i04.18128

Abstract

Flowers are transformations of buds, including stems and leaves, with shapes and colors adapted to the plant's functions. They also serve as sites for fertilization and pollination. Flowers come in various shapes and colors, with over 250,000 flowering plant species known and classified into 350 families. Therefore, employing technology for flower pattern recognition is crucial for enhancing accuracy and efficiency. One effective method involves using Artificial Neural Networks (ANN) in conjunction with the perceptron algorithm. This algorithm has proven effective in image-based pattern recognition due to its ability to learn complex and linear patterns from image data. This study explores the use of neural networks, specifically the perceptron method, in recognizing flower patterns. The test utilizes sunflower image samples, with the perceptron algorithm applied to produce accurate and effective data in flower pattern recognition.
IMPLEMENTASI JARINGAN SARAF TIRUAN UNTUK MEMPREDIKSI TINGKAT PRODUKSI JAGUNG GILING MENGGUNAKAN METODE BACKPROPAGATION (STUDI KASUS: MIKRA MAKMUR BERSAMA) Aritonang, Reza Sri Rezeki; Saputra, Imam; Siregar, Annisa Fadillah
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.v9i04.18160

Abstract

This study aims to implement Artificial Neural Networks (ANN) to predict corn flour production levels at the agricultural company Mikra Makmur Bersama using the backpropagation learning method. As a machine learning technique, ANN has the potential to enhance prediction accuracy by effectively analyzing historical data. Data on corn flour production from 2021 to 2023 was collected from the company and used to train the ANN model with a backpropagation architecture. This process involves feedforward and backward propagation to optimize neuron weights, aiming to produce accurate and reliable predictions. The backpropagation algorithm updates weights based on prediction errors and can adapt to complex patterns in the data. The results show that the implemented ANN model successfully predicted corn flour production levels with significant accuracy, as tested with data from 2021 to 2023. This study is expected to serve as a reference for applying ANN technology in other agricultural sectors and encourage the use of advanced methods to enhance efficiency and productivity.
Co-Authors A.A. Ketut Agung Cahyawan W A.N. Afandi Abdul Karim Adiguna, Satria Afriansyah, Muhammad Agung Dwi Pradana Aguswinaya, Agung Rake Ahmad Tamrin Sikumbang Al-Adawiyah, Robiah Alfarisi Pasaribu, Ahmad Amalia, Dira Amelia Ramadhani, Amelia Annisa Fadillah Siregar Anugrah, Elfira Aripriharta - Ariska, Melinda Aritonang, Reza Sri Rezeki Aryadito, Rehan Ayulia Sari Azlan Azlan, Azlan Bagaskoro, Muhammad Cahyo Bilal Abdul Aziz Darma Taksiah Sihombing, Darma Taksiah Dina Octavia Erna Verawati Fadlina Fahrezi, Azrial Fince Tinus Waruwu Ginting, Suranta Bill Fatric Gokma Lumbantoruan Guidio Leonarde Ginting Gultom, Istanto Hardianti, Putri Delfi Harianja, Chindy Lorenza Hery Sunandar Hetty Rohayani Hondro, Piter Saputra Indra Williamsyah Sinaga Jariah, Nur Ainun Kolopaking, Sania Lemcia Hutajulu Lole, Marsya Reskiani Lubis, Adi Mora Maimunah, M Mariansari, Mariansari Marliana Marliana, Marliana Marselino Clifer Tuju Matondang, Firman MAURITZ PANDAPOTAN MARPAUNG Mesran, Mesran Muhammad Afnan Habibi Muhammad Resa Arif Yudianto Muhammad Rizkan Abdul Aziz Muhammad Syahrizal Mutiara, Berkah Nasib Marbun Nastiti, Sindy Ndruru, Eferoni Nelly Astuti Hasibuan Nur Afifah Siregar, Rizka Nur Syifa’ul Alyah Oktarina, Dian Panggabean, Riski Melisa Permata, Shely Junian Pohan, Ferdi Putri Ramadhani, Putri Rahmawaty Rahmawaty Rahmawaty Raimah Handayani Harahap Rasyid, Irfan Ritonga, Fitri Aisyah Rivalri Kristianto Hondro Robinson Siagian, Edward Rohmat Indra Borman Sagita, Ira Sahrul Saputra Saidi Ramadan Siregar Saputra, Dendi Bianda Saputra, Febrian Eko Sari, Juwita Indah Sari, Sri Indah Setiawan, Aditya Wahyu Sheva, Putri Picaso Azury Simanullang, Putri M Simanungkalit, Lidya Indah Pratama Sinurat, Sinar Suginam Sultan Rexy Adji Suparwatini, Suparwatini Surya Darma Nasution Suryanegara, Raden Kartika Satya Sussolaikah, Kelik Syafruddin Syafruddin Tamaulina Br.Sembiring Tanjung, Dewi Maulida Sari Taronisokhi Zebua Tatang Permana, Tatang Tua, Rahmat Utami, Nur Indah Yanti, Martina Vevi Yusufa, Ilham Zainun, Zainun