Digital Zone: Jurnal Teknologi Informasi dan Komunikasi https://journal.unilak.ac.id/index.php/dz <p>Digital Zone journal publish by Fakultas Ilmu Komputer Universitas Lancang Kuning (Online <a href="http://issn.pdii.lipi.go.id/issn.cgi?daftar&amp;1426561719&amp;1&amp;&amp;" target="_blank" rel="noopener">ISSN 2477-3255&nbsp;and Print ISSN 2086-4884</a>) This journal publish two periode in a year on May and November</p> <p><strong>Journal Digital Zone: Jurnal teknologi informasi dan Komunikasi has been accredited by National Journal Accreditation (ARJUNA) Managed by Ministry of Research, Technology, and Higher Education, Republic Indonesia since year 2021 to 2025 according to the decree No. 164/E/KPT/2021</strong></p> <p>&nbsp;</p> en-US <div class="page"> <div class="pkp_footer_content"> <p><img src="/public/site/images/llisnawita/CC._6.jpg" width="63" height="23">&nbsp;Jurnal Digital Zone is licensed under a<a href="Creative%20Commons Attribution-ShareAlike 4.0 International License." target="_blank" rel="noopener">Creative Commons Attribution-ShareAlike 4.0 International License.</a></p> <p>&nbsp;</p> </div> </div> digitalzone@unilak.ac.id (Admin Jurnal) digitalzone@unilak.ac.id (Digital Zone) Sat, 29 Oct 2022 00:00:00 +0000 OJS 3.1.2.4 http://blogs.law.harvard.edu/tech/rss 60 Sentiment Analysis on the Construction of the Jakarta-Bandung High-Speed Train on Twitter Social Media Using Recurrent Neural Networks Method https://journal.unilak.ac.id/index.php/dz/article/view/10777 <p><em>During the construction of the Jakarta-Bandung high-speed train, many Indonesian people gave their responses to the public. The answers were also varied, with some giving positive and negative reactions. The purpose of this study is to analyze the sentiments of the responses given by the public to the construction of the Jakarta-Bandung high-speed train on Indonesian-language Twitter. To perform sentiment analysis, tweet data was collected utilizing data crawling based on keywords related to the construction of the Jakarta-Bandung high-speed train and given positive, negative, and neutral labels and then represented into numbers using the Keras tokenizer. The method used for sentiment classification of tweet data is the Recurrent Neural Networks method. The highest accuracy results were obtained using the GRU architecture with an accuracy of 69.62%.</em></p> Titan Kinan Salaatsa, Yuliant Sibaroni Copyright (c) 2022 Digital Zone: Jurnal Teknologi Informasi dan Komunikasi http://creativecommons.org/licenses/by-sa/4.0 https://journal.unilak.ac.id/index.php/dz/article/view/10777 Sat, 29 Oct 2022 00:00:00 +0000 Live Forensics Analysis Of Malware Identified Email Crimes To Increase Evidence Of Cyber Crime https://journal.unilak.ac.id/index.php/dz/article/view/11570 <p><em>Now days Email is the most important aplplication&nbsp; on the internet, this make email one of the industry’s most targeted sector for commiting cyber crimes. Email phishing and spam not only harm many parties but also consumes a lot of network bandwidth. Most spam are emotet malware. Trojan malware that targets internet users financial system to steal financial information and personal data by sending phishing. In this research, digital forensics analysis email crimes identified malware using live forensics and tools analyze digital evidence of email content, as wall as offVise, Wireshark, and Procmon to analyze malware activities. The results of the investigation of the email content carried out using software found digital evidence that could be used as a reference that attachment downloaded by the victim was Emoted type malware, when the victim opened it, this malware will be installed automatically on the victim’s computer.</em></p> <p><em>.</em>&nbsp;<br><br><br></p> Yudhi Prawira Prawira, Samsudin Samsudin Copyright (c) 2022 Digital Zone: Jurnal Teknologi Informasi dan Komunikasi http://creativecommons.org/licenses/by-sa/4.0 https://journal.unilak.ac.id/index.php/dz/article/view/11570 Wed, 09 Nov 2022 00:00:00 +0000 Sentiment Analysis of Public Opinion Regarding Papuan Local Languages Condition Using Data Science Approach https://journal.unilak.ac.id/index.php/dz/article/view/11545 <p><em>&nbsp;Regional languages ​​can support economic empowerment and improvement through the tourism sector. Opinions from people's expressions in social media and online news collections in reporting the condition of regional languages ​​often become headlines in cyberspace that number in the thousands, which can be used as new knowledge as a basis for making decisions through the mining method. This study aims to explore public opinion sentiment related to the condition of the Papuan language, sourced from text data in cyberspace using a data science approach, namely the classification method with text mining techniques using the naïve bayes algorithm. Public opinion sentiments are processed and the results are presented using word cloud visualization through 4 stages of data science, namely data collection, data preprocessing, modeling exploration and visualization analysis. The result of 778 opinions, 92% tend to have a positive sentiment. The analysis of public opinion sentiment is carried out by the naïve bayes algorithm which has an algorithm model accuracy of 78% and a precision of 88%. The machine learning model that was built and the word cloud visualization analysis succeeded in providing new insights regarding the condition of the Papuan language.</em><br><br></p> Nur Fitrianingsih Hasan, Aisyah Aisyah, Rahman Rahman, Herlin Wonda Copyright (c) 2022 Digital Zone: Jurnal Teknologi Informasi dan Komunikasi http://creativecommons.org/licenses/by-sa/4.0 https://journal.unilak.ac.id/index.php/dz/article/view/11545 Sat, 26 Nov 2022 00:00:00 +0000 SVM Method with FastText Representation Feature for Classification of Twitter Sentiments Regarding the Covid-19 Vaccination Program https://journal.unilak.ac.id/index.php/dz/article/view/11531 <p><em>Covid-19 is a virus that has a high level of spread, making the government implement a mass vaccination program throughout Indonesia. This program received a lot of responses from the public, with positive and negative opinions or comments. Currently, the public's response through social media is also an input and consideration for the government to implement a program. Therefore, this study was conducted to produce a method approach to assessing the Covid-19 vaccination program by calculating the percentage of each sentiment class. The method used is the Support Vector Machine (SVM) and the fasttext language model feature as a representation of words in the Covid-19 vaccination sentiment dataset collected from Twitter. The data used has been dataset balancing, feature selection and parameter tuning, the optimal SVM model is obtained with a composition of 2536 training data, 778 development data and testing of 400 testing data, resulting in the best value of fi-1 score of 59% with an accuracy rate of 68%. The system is quite successful in detecting sentiment in tweets compared to before.</em>&nbsp;<br><strong><em>Keywords:</em></strong><em> sentiment classification, FastText, SVM, Covid-19 vaccine</em><em>.</em></p> Mukti M Kusairi, Surya Agustian Copyright (c) 2022 Digital Zone: Jurnal Teknologi Informasi dan Komunikasi http://creativecommons.org/licenses/by-sa/4.0 https://journal.unilak.ac.id/index.php/dz/article/view/11531 Sat, 26 Nov 2022 00:00:00 +0000 C4.5 Algorithm Implementation For Public Sentyment Analysis Covid-19 Vaccine https://journal.unilak.ac.id/index.php/dz/article/view/11658 <p><em>Corona virus disease is one of the dangerous diseases and has been prevented by giving vaccinations. In an effort to prevent, there must be a positive or negative public response. One of the media facilities used to convey public responses is Twitter. The public's reaction can be analyzed using public sentiment analysis using C4.5 algorithm. The purpose of paper for determine public's response to the administration of moderna and pfizer vaccinations. The implemented methodology starts from collecting data taken from tweets, pre-processing, classification using the C4.5 algorithm and validation using k-fold cross validation. Based on the results of the moderna keyword analysis, the positive sentiment response was 6% and negative sentiment was 94%, while the pfizer keyword positive sentiment was 12.4% and negative sentiment was 87.6%. The results of test iteration that have been carried out 3 times, the average error value is 38%.</em></p> Devi Astri Nawangnugraeni, M. Zakki Abdillah, Akrim Teguh Suseno Copyright (c) 2022 Digital Zone: Jurnal Teknologi Informasi dan Komunikasi http://creativecommons.org/licenses/by-sa/4.0 https://journal.unilak.ac.id/index.php/dz/article/view/11658 Wed, 30 Nov 2022 00:00:00 +0000