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Siagian, Edward Robinson
Universitas Budi Darma, Medan
Agriculture, Biological Sciences & Forestry Humanities Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Nursing Social Sciences Other

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Journal : KOMIK (Konferensi Nasional Teknologi Informasi dan Komputer)

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IMPLEMENTASI ALGORITMA REGRESI LINEAR SEDERHANA DALAM MEMPREDIKSI BESARAN PENDAPATAN DAERAH (STUDI KASUS: DINAS PENDAPATAN KAB. DELI SERDANG) Ginting, Fransiskus; Buulolo, Efori; Siagian, Edward Robinson
KOMIK (Konferensi Nasional Teknologi Informasi dan Komputer) Vol 3, No 1 (2019): Smart Device, Mobile Computing, and Big Data Analysis
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/komik.v3i1.1602

Abstract

Data Mining is an information discovery by extracting information patterns that contain trend searches in a very large amount of data and assist the process of storing data in making a decision in the future. In determining the pattern classification techniques do to collect records (Training set). Regional income is generally derived from local taxes and levies, local taxes are one source of funding for the region on the national average has not been able to make a large contribution to the formation of local revenue. By utilizing Regional Revenue data, it can produce forecasting and predictions of Regional Revenue income in the future to match the reality / reality so that the planned RAPBD can run smoothly. Simple Linear Regression or often abbreviated as SLR (Simple Linear Regression) is one of the statistical methods used in production to make predictions or predictions about the characteristics of quality and quantity to describe the processes associated with data processing for the acquisition of regional income. So that in the testing phase with visual basic net can help in processing valid Regional Revenue Amount data. Keywords: Data Mining, Local Revenue, Simple Linear Regression Algorithm, Visual Basic net 2008
IMPLEMENTASI DATA MINING DENGAN METODE REGRESI LINEAR BERGANDA UNTUK MEMPREDIKSI DATA PERSEDIAAN BUKU PADA PT. YUDHISTIRA GHALIA INDONESIA AREA SUMATERA UTARA Indah Lestari Lumban Gaol; Sinar Sinurat; Edward Robinson Siagian
KOMIK (Konferensi Nasional Teknologi Informasi dan Komputer) Vol 3, No 1 (2019): Smart Device, Mobile Computing, and Big Data Analysis
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/komik.v3i1.1579

Abstract

PT. Yudhistira Ghalia North Sumatra. Where Inventory (stock) of goods is an important thing in a company for data collection or checking activities in order to find out the amount of goods that are used up and goods that will be needed in a company. Inventory of goods is always needed in company activities. So that in the supply of books has been delayed for making stock and excess stock making of books. In this study, multiple linear regression method will be used to predict book inventory data. So for that we need to predict book inventory data for the future how many books should be in stock. Multiple linear regression algorithm has advantages such as generalizing and extracting from certain data patterns, being able to acquire knowledge even though there is no certainty, and being able to do calculations in parallel so that the process is shorter. After being predicted, it will be possible to produce results that can be used in the future so that it can help PT. Yudhistira especially the part of the inventory of goods which must be provided in the following month.Keywords: Data Mining, Inventory, Multiple Linear Regression Algorithms
Analisis Perbandingan Algoritma Elias Delta Code Dengan Algoritma Prefix Code Dalam Mengkompresi Data Teks Kevin Yanto Sarumaha; Muhammad Syahrizal; Edward Robinson Siagian
KOMIK (Konferensi Nasional Teknologi Informasi dan Komputer) Vol 6, No 1 (2022): Challenge and Opportunity For Z Generation in Metaverse Era
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/komik.v6i1.5698

Abstract

The use of storage media to store various data, be it text, video or audio, has been used for a long time and continues to grow today. The amount of data will affect the process of data transmission and the use of large enough storage space. This is the background for applying the compression process to data, especially text data, so that data can be reduced and facilitate the data transmission process and use less storage space. However, many compression techniques or methods have been found from various experts, which makes it a difficult choice to determine which method is efficient enough for compressing data, especially text data. Data compression, of course, gives results from different compression ratios, including the Elias Delta Code and Prefix Code compression yahoo. In compressing text data the author uses the yahoo Elias Delta Code and yahoo Prefix Code compression methods and analyzes and compares the performance results of the data compression ratio in the two algorithms. In this study, comparisons were made in comparing the Elias Delta Code algorithm and the Prefix Code algorithm in text data compression, the Elias Delta Code algorithm topped first with a comparison result of 73.3% compared to the Prefix Code algorithm which had a comparison value of 66.6%. . Based on this analysis, the Elias Delta Code algorithm is the best algorithm for compressing.
Co-Authors , Sony Bahagia Sinaga Armansyah Ritonga Chandra Frenki Sianturi Dahwila Syapnika Doansi Tarihoran Efori Buulolo Fince Tinus Waruwu Ginting, Fransiskus Ikwan Ikwan Indah Lestari Lumban Gaol Ira Sarifah Rangkuti Kevin Yanto Sarumaha Kristian Siregar Manalu, Mamed Rofendi Maringan Sianturi Monang Juanda Tua Sihombing Muhammad Syahrizal Nelly Astuti Hasibuan Panjaitan, Muhammad Iqbal Purba, Elvitrianim Rajagukguk, Denni M Rajagukguk Riswan Limbong Sembiring, Abdul Sani Siburian, Henry Kristian Siddik Karo-Karo Sinaga, Sony Bahagia Sinurat, Sinar Tamba, RL. Harmadi Tamba, RL. Harmady