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RETRACTED: Klasifikasi Data Time Series Arus Lalu Lintas Jangka Pendek Menggunakan Algoritma Adaboost dengan Random Forest Ahmad Rofiqul Muslikh; Heru Agus Santoso; Aris Marjuni
BRILIANT: Jurnal Riset dan Konseptual Vol 4, No 1 (2019): Volume 4 Nomor 1, Februari 2019
Publisher : Universitas Nahdlatul Ulama Blitar

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (302.293 KB) | DOI: 10.28926/briliant.v4i1.272

Abstract

RETRACTEDFollowing a rigorous, carefully concerns and considered review of the article published in BRILIANT: Jurnal Riset dan Konseptual to article entitled Klasifikasi Data Time Series Arus Lalu Lintas Jangka Pendek Menggunakan Algoritma Adaboost Dengan Random Forest Vol 4, No 1, pp. 78-96, February 2019, DOI: http://dx.doi.org/10.28926/briliant.v3i3.272.This article has been found to be in violation of the BRILIANT: Jurnal Riset dan Konseptual Publication principles and has been retracted.The editor investigated and found that the article published in Jurnal Teknologi Informasi CyberKU Vol. 14 no 1 January 2018, pp. 24-38.The document and its content has been removed from BRILIANT: Jurnal Riset dan Konseptual, and reasonable effort should be made to remove all references to this article.
Analisis Pemilihan Media Promosi UMKM untuk Meningkatkan Volume Penjualan Menggunakan Metode Analytical Hierarchy Process (AHP) Edi Subiyantoro; Ahmad Rofiqul Muslikh; Mardiana Andarwati; Galandaru Swalaganata; Fandi Yulian Pamuji
Jurnal Teknologi dan Manajemen Informatika Vol 8, No 1 (2022): Juni 2022
Publisher : Universitas Merdeka Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26905/jtmi.v8i1.6760

Abstract

The increase in the number of creative industry entrepreneurs on the scale of UMKMs in Indonesia must be supported by several factors so that these businesses can develop. These factors range from business conditions, environment, facilities, and infrastructure, to technology. In terms of the use of technology, UMKM business actors can use it in various fields including the procurement of raw materials, the production process to the marketing and promotion stages of the products produced. This analysis aims to determine the weight the importance of the criteria to create an element of UMKM sales volume. In addition, it also helps UMKM actors in making decisions in choosing and using which alternative best suits their needs. Based on the results of the analysis of this study, it can be concluded that alternative social media is a priority criterion in increasing the sales volume of UMKM actors. Based on the overall average weight value, the alternative for social media is to expand the market by increasing the intensity of promotions with various social media. Such as WhatsApp Business, Instagram, Facebook, YouTube, and others to increase product sales for UMKM actors.
KLASIFIKASI DATA TIME SERIES ARUS LALU LINTAS JANGKA PENDEK MENGGUNAKAN ALGORITMA ADABOOST DENGAN RANDOM FOREST Ahmad Rofiqul Muslikh; Heru Agus Santoso; Aris Marjuni
Jurnal Teknologi Informasi - Cyberku (JTIC) Vol 14 No 1 (2018): Jurnal Teknologi Informasi CyberKU Vol.14 no 1 2018
Publisher : Program Pascasarjana Magister Teknik Informatika, Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (584.878 KB)

Abstract

Data traffic in Indonesia is used for management control traffic flow, while the data on get results from the survey will be undertaken directly localized, the survey will be undertaken are less effective, and the data obtained from the survey results were used as a reference in control traffic flow, and therefore to obtain the data traffic flow more effective in need of a new approach that can classified and predict the data in the can with higher accuracy, so that density and congestion can be predicted earlier. In this study used the approach of using Adaboost and Random Forest algorithms to classification and predict the survey data that are time series, the results of testing for prediction using Adaboost with Random Forest With Confusion Matrix as a measuring accuracy rate of 87,8%, and the rate of error in getting at 0 , 0629. On the results using Adaboost with a Random Forest approach proved to be more efficient in predicting the survey data rather than simply relying on the original data to predict traffic flow