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All Journal Sistemasi: Jurnal Sistem Informasi Jurnal Computer Science and Information Technology (CoSciTech) International Journal Engineering and Applied Technology (IJEAT)
Dwi Sartika simatupang
Nusa Putra University
Civil Engineering, Building, Construction & Architecture Computer Science & IT Electrical & Electronics Engineering Engineering Mechanical Engineering

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MODELLING OF DATA WAREHOUSE WITH MAKING THE TREND TO MAKE DECISION IN COMPANY XYZ Dwi Sartika simatupang; Anggun Fergina; Bahadir Ozsut
INTERNATIONAL JOURNAL ENGINEERING AND APPLIED TECHNOLOGY (IJEAT) Vol. 5 No. 2 (2022): November 2022
Publisher : Nusa Putra University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52005/ijeat.v5i2.72

Abstract

The goals of the thesis were to modellig a data warehouse which was loaded from the operational database PT. XYZ, to analyze data warehouse with Tableau Software to know the trends of activity Trouble ticket, and to create reports and dashboards to facilitate PT. XYZ in viewing the trends. Data were compiled by observation to PT. XYZ from reporting team. Data were analyzed using software tableau. The results obtained were the trend of activity trouble ticket more than two hundred thousand data in sixteen weeks or four month in 2017. It can be concluded that developed data warehouse can be used to analyze in gaining information about the trends of activity trouble ticket and help to make a decision.
Analisis Sentimen Twitter Terhadap Cyberbullying Menggunakan Metode Support Vector Machine (SVM) Rismi Nurlaely; Dwi Sartika Simatupang; Kamdan Kamdan; Ivana Lucia Kharisma
Computer Science and Information Technology Vol 4 No 2 (2023): Jurnal Computer Science and Information Technology (CoSciTech)
Publisher : Universitas Muhammadiyah Riau

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Abstract

The development of technology helps humans to communicate with each other. The rapid development of technology has led to the birth of many new media platforms. As technology advances in this digital era. The existence of information technology has a great impact on human civilization. One form is the use of social media. However, the ease of sharing information through social media has not escaped abuse by its users. One form of abuse is cyberbullying carried out on social media, especially Twitter. The 2018 Internet Penetration and Internet User Behavior Survey in Indonesia published by the Indonesian Internet Service Providers Association (APJII) shows that 49 percent of internet users have experienced bullying in the form of ridicule or harassment on social media. In this regard, sentiment analysis was carried out through social media twitter to measure the accuracy value using the SVM (support vector machine) algorithm. Sentiment analysis by crawling twitter data as many as 1000 tweet data in Indonesian using the RapidMiner tool. And this classification with SVM (Support Vector Machine) algorithm gets an accuracy of 92% by testing at 80:20 proportion, which is 80% training data and 20% testing data. Of the 998 data that have been preprocessed, 625 data on the positive class and 374 data on the negative class have been obtained.
Implementation of Deep Neural Network in the Design of Ethereum Blockchain Scam Token Detection Applications Dimas Arya Pamungkas; Ivana Lucia Kharisma; Dwi Sartika Simatupang; Kamdan Kamdan
Sistemasi: Jurnal Sistem Informasi Vol 12, No 3 (2023): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v12i3.3162

Abstract

The popularity of blockchain continues to increase as technology develops, especially in the context of Ethereum as one of the leading blockchain platforms. However, this increase was also followed by many cases of fraud, especially in the form of tokens. In blockchain technology, tokens often refer to cryptocurrencies or digital currencies used as a means of exchange related to a particular project or platform. This research designs and builds an application system that can detect scam crypto tokens on the Ethereum blockchain, specifically for the ERC-20 (Ethereum Request for Comments 20) token type, which was proposed by Fabian Vogelsteller in November 2015, is a token standard that implements APIs for tokens. in Smart Contracts. Making a scam detection application implements the deep learning method with the Deep Neural Network (DNN) algorithm and evaluates performance using two test scenarios by dividing the dataset into three ratios of training data and test data. The output of the application is JSON-RPC which is integrated with the website. In testing the DNN model, using 80% training data and 20% test data, the DNN algorithm provides an accuracy of 0.997558%. Furthermore, system testing was carried out involving various scenarios to verify its functionality, including input validation, data extraction, DNN prediction, and display of prediction results, which gave good results from the system created. The application has succeeded in identifying scam tokens with high accuracy. , increasing user security in crypto transactions.
Co-Authors Anggun Fergina Bahadir Ozsut Dimas Arya Pamungkas IVANA LUCIA KHARISMA Kamdan Rismi Nurlaely