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Efori Bu’ulolo
Universitas Budi Darma, Medan
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering

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Analisis Perbandingan Kinerja Boldi-Vigna Codes Dengan Algoritma Fixed Lenght Binary Encoding (FLBE) Dalam Kompresi File Text Ewit Purba; Efori Bu’ulolo; Bister Purba
KOMIK (Konferensi Nasional Teknologi Informasi dan Komputer) Vol. 6 No. 1 (2023): Challenge and Opportunity For Z Generation in Metaverse Era
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/komik.v6i1.5700

Abstract

The increasing use of larger data causes problems in data storage, the greater the data stored, the greater the storage space that will be needed. This can cause the data transformation process to be slow and take a long time. Currently, there are many algorithms developed for data compression, but none are so good for compressing various file types because of their different characteristics. One solution or alternative in solving the problem that will be done is to compress the file to reduce the size of the data and speed up the data transmission process so as to save storage space. The algorithms used in this research are Boldi-Vigna (ζ1) Code and Fixed Length Binary Encoding (FLBE) algorithms. To find out the comparison of compression performance, the parameters to be compared are Ratio of Compression (RC), Compression Ratio (CR), Space Saving (SS), Redundancy (Rd), Compression & Decompression Time. Based on the test results show that the Fixed Length Binary Encoding (FLBE) algorithm is better than the Boldi-Vigna (ζ1) Code algorithm where the average result of the comparison of the Boldi-Vigna (ζ1) Code Ratio is 1.69 bits while the Ratio of Compression algorithm Fixed Length Binary Encoding (FLBE) 1.86 bits. The average compression ratio of the Boldi-Vigna algorithm (ζ1) Code is 58.92%, while the Compression Ratio of the Fixed Length Binary Encoding (FLBE) algorithm is 53.57%.
Implementasi Algoritma Code-Excited Linear Prediction (Celp) Pada Kompresi File Audio Lucius Yupiter Telaumbanua; Efori Bu’ulolo; Kurnia Ulfa
KOMIK (Konferensi Nasional Teknologi Informasi dan Komputer) Vol. 6 No. 1 (2023): Challenge and Opportunity For Z Generation in Metaverse Era
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/komik.v6i1.5701

Abstract

The use of storage media to store various files, whether text, video or audio, has been implemented for a long time and continues to grow today. The size of a file will affect the data transmission process as well as the use of large enough storage space. This is what lies behind the application of the compression process to a file so that the file size can be reduced and simplify the process of data transmission and use less storage space. One file that is often compressed is an audio file. Audio files often use a large enough storage space when compared to other document files. This is why compression of audio files is needed. In compressing audio files, the author will use the CELP method. This method is part of lossy compression. The lossy compression method recognizes that human hearing is limited so that not all data in the audio file can be heard directly. The lossy algorithm will provide a fairly large change in file size compared to lossless compression. The CELP method is applied because it can perform coding efficiently and the quality is maintained. In this study, it is known that the application of the CELP method on audio files produces an audio file compression application, especially those with the Mp3 extension. The compressed Mp3 audio file has a very small size with a compression ratio of 7.38%.
Algoritma Clustering K-Nearest Neighbor Dalam Pengelompokan Masyarakat Kecamatan Medan Area Berdasarkan Tingkat Ekonomi Keluarga Rizky Meliani Astri Hasibuan; Efori Bu’ulolo; Sumiaty Adelina Hutabarat
KOMIK (Konferensi Nasional Teknologi Informasi dan Komputer) Vol. 6 No. 1 (2023): Challenge and Opportunity For Z Generation in Metaverse Era
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/komik.v6i1.5756

Abstract

An economy that is constantly changing and developing may result in increased economic difficulties. Current economic developments have decreased to 4.79, causing poverty in Indonesia to increase. To see the condition of the people's economy, it is necessary to collect detailed data so that people with a lower economy get assistance. The problems faced in this study are data collection and classification of the economic level of the population. The process is not very efficient because it is done manually. Classification procedures will only be carried out when necessary. Controlling the welfare of the population has not been carried out in detail. To overcome these problems, we need a system so that the grouping of people who have a classification from high to low economic level. This study uses the K-NN Clustring Algorithm which requires information training to classify objects that are very close. In the last process the Clustring K-NN algorithm is a method for calculating the ratio of old data to updated information. As well as looking for the highest data occurrence value that will be used as a reference as a result.
Analisis Perbandingan Algoritma Lz78 Dengan Algoritma Transformasi Walsh-Hadamard Untuk Kompresi Citra Alexander Pamdapotan Manullang; Efori Bu’ulolo; Meryance Viorentina Siagian
KOMIK (Konferensi Nasional Teknologi Informasi dan Komputer) Vol. 6 No. 1 (2023): Challenge and Opportunity For Z Generation in Metaverse Era
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/komik.v6i1.5792

Abstract

Data communication is an activity that is very often carried out in the field of information technology. Data with a large size will take a longer transfer time compared to data that has a smaller size, sometimes there is a risk that it cannot be accommodated on the storage media and is not delivered, so that it will reduce the empty capacity in the memory of the storage media. This can cause the data transformation process to be slow and take a long time. The solution that can solve the above problem is by doing compression. Compression is a technique that can reduce the size of the image to be smaller than its original size. This compression performs a compression technique with compression properties where it is allowed to lose some or most of the data in the image. The algorithm used in this study is the LZ78 algorithm with the Walsah-Hadamard Transform algorithm. To find out the comparison of the compression performance, the parameters to be compared are Ratio of Compression (RC), Space Saving (SS). Based on the results of the researcher's test in comparing the LZ78 algorithm with the Walsh-Hadamard Transformation, the compression value obtained by calculating the ratio that the Walsh-Hadamard Transform algorithm is better than LZ78 where the results of the comparison of the Ratio of Compression of the Walsh-Hadamard Transformation algorithm are 0.71 bits, while the Ratio of 2.66 bit LZ78 compression algorithm. LZ78 62.5% while the Compression Ratio algorithm Walsh-Hadamard Transformation 76%
Co-Authors Alexander Pamdapotan Manullang Bister Purba Ewit Purba Hutabarat, Sumiaty Adelina Kurnia Ulfa Lucius Yupiter Telaumbanua Meryance Viorentina Siagian Rizky Meliani Astri Hasibuan