Publication Search

Twitter is a social media that provides information for its users. Interactions on Twitter may increase the amount of data from its users. Business development cannot be separated from product competition between business actors. One way for companies to win the competition is to know the purchasing power of customers. A product's selling power can be known one way or another through user interaction on Twitter. User-generated content (UGC) results from data on Twitter. UGC data can be used to determine the product supply scheme taken by laptop products in this study. The method used to analyze the interaction is Social Network Analysis (SNA) by comparing the properties of the network. Social network modeling on UGC data was carried out on three laptops: Lenovo v14, Asus ZenBook S, and Acer Swift 3. From the graph visualization, it was found that Acer excels in two properties, namely "Size" and "Average Degree." Meanwhile, the Asus ZenBook S dominates the "Modularity" property.

The Halal Product Assurance Agency (BPJPH) is an agency under the auspices of the Ministry of Religion with the task of ensuring the halalness of products in Indonesia. BPJPH has become a public concern after establishing the new halal logo. On February 10, 2022 the new halal logo was ratified by the Head of BPJPH, Muhammad Aqil Irham. This has become a topic of public discussion either directly or through social media, one of which is social media twitter. The number of opinion tweets about the change of the halal logo can be used as a data source to obtain information about public opinion on the change of the halal logo through sentiment analysis. Sentiment analysis can be done by machine learning approach, one of these is the SVM algorithm . In this research, oversampling and undersampling are applied to handle data that has an unbalanced sentiment class. The results showed that the Support Vector Machine (SVM) model using oversampling training data got the highest accuracy, recall, precision, and f1-score, namely 71% accuracy, 67% precision, 61% recall, and 61% f1-score while training using undersampling training data has the lowest performance, namely getting 56% accuracy, 51% precision, 57% recall, and 52% f1-score.

The government that is running at this time is also not spared from public comments on Twitter, especially regarding the increase in subsidized fuel. There are at least 4 impacts felt by the community when subsidized fuel prices increase, namely a decrease in people's purchasing power, an increase in basic prices, an increase in the unemployment rate and an increase in the poverty rate. This study aims to implement the Naïve Bayes Classifier and KNN algorithms in classifying a tweet of an increase in subsidized fuel so that it can be identified as belonging to a class with positive or negative sentiments. The data used in this research are 560 tweets. The data is divided into 2, namely 500 training data from tweet data and 60 test data from tweet data stored in xlsx format. The results of the accuracy with the Naïve Bayes Classifier algorithm is 85% while the KN algorithm is 86.8% so it can be concluded that the KNN method is better than the Naïve Bayes Classifier method in classifying tweets of increases in subsidized fuel. Keywords: Subsidized BBM, Naive Bayes, KNN

The widespread use of social media among the public has created a new need, namely the urgency to create space for conducting business activities, causing the platform to turn into a place for communication, interaction, as well as a trading space. These changes bring various impacts, one of which is the formation of new types of crime in cyberspace. Fraud in electronic transactions via social media (Twitter) is a crime that targets the internet, computers and related technology as its target. Based on the position of the case, the fraud that occurs can be classified as a crime of computer-related fraud or a crime to gain personal gain and/or harm others. The handling of fraud cases can be carried out using the legal basis contained in Law Number 19 of 2016 concerning Information and Electronic Transactions, namely Article 28 paragraph (1) in conjunction with Article 45 paragraph (2). The use of these two articles is based on the principle of lex specialist derogat legi generali. In addition, if examined using a victimological point of view, victims of fraud cases that occur are included in the category of participating victims where the tendency of victims to be unaware of their attitudes/behaviors in certain circumstances is a reason for someone to act. commit crimes against them. The research method used to answer these problems is normative legal research with a case study approach in the form of legal behavior products.    

In today's digital era, we often hear or see the use of slang words, both among teenagers and adults. We often find slang words on social media users, one of which is Twitter. The rapid development of communication technology in this digital era, so that social media is very easy to access, in general social media such as twitter most of its users are teenagers. In the end, it has a major influence on how to interact in social life both in cyberspace and the real world. Some experts state that the characteristics of the generation in the current era are different from the previous generation, one of the reasons is because the current generation was born and grew up in a modern environment and surrounded by digital technology. This study aims to identify the various forms of slang found in captions and comment columns on social media twitter.  The data in this study is in the form of slang words obtained by applying observation methods with screenshot and record techniques. The data sources used were @collegemenfess Twitter social media accounts and @kdrama_menfess. The results in this study are various forms of slang words in the form of (1) mager, (2) bucin, (3) baper, (4) gabut, (5) bm, (6) gaje, (7) gercep, (8) mode. The analysis showed mixed results, but most of the participants stated that they got slang words to interact with in social circles and obtained those slang words from memes and other social media. 

Penelitian ini membahas kajian sosilinguistik, tepatnya analisis penggunaan campur kode pada laman Twitter Collegemenfess. Laman Twitter ini merupakan salah satu auto base dengan pengikut terbanyak di Indonesia. Penelitian dilakukan dengan metode deskriptif kualitatif. Setelah dilakukan penelitian, ditemukan penggunaan campur kode pada cuitan di Collegemenfess. Data yang ditemukan berupa sepuluh campur kode ke luar dan lima campur kode ke dalam. Penggunaan campur kode yang ditemukan berupa kata dasar, frasa, istilah dan kalimat. Penggunaan campur kode ke luar yang ditemukan yakni pada bahasa Inggris, lalu penggunaan campur kode ke dalam ditemukan pada bahasa Jawa, Sunda, dan Medan. Dapat disimpulkan, pengguna auto base Collegemenfess dalam berinteraksi di Twitter sering kali menggunakan campur kode untuk menyampaikan maksudnya.