https://journal.educollabs.org/index.php/JMTT <p><img src="https://journal.educollabs.org/public/site/images/isantiko/banner.png" alt="" width="901" height="128" /></p> <table> <tbody> <tr valign="top"> <td width="30%"> <p><img src="https://journal.educollabs.org/public/site/images/isantiko/cov-med.jpg" alt="" width="500" height="715" /></p> <p align="right">Regards,</p> <p align="right"><img src="https://journal.educollabs.org/public/site/images/isantiko/signeditor.png" alt="sign" width="100" height="30" /></p> </td> <td rowspan="9" width="2%"> </td> <td width="50%"> <p align="justify"><strong>Journal of Multimedia Trend and Technology </strong>is an online <strong>journal</strong> that is organized and managed independently by a consortium of multimedia and visual communication design lecturers. JMTT is an open-access journal that is provided for researchers, lecturers, and students who will publish research results in the field of all of the things about Digitalized Multimedia and its process.</p> <p align="justify">Currently, JMTT is under the auspices of the Amikom University Purwokerto higher education organization, with the management of the Multimedia, Game, and Mobile Apps Study Center together with Educollabs.</p> <p align="justify">Scientific Journal of Multimedia Trend and Technology as JMTT. JMTT is a forum for writers of scientific articles from Indonesia. JMTT is a scientific publication media under the auspices of software development company Edu-Collabs, and managed by the Multimedia and Information Technology community in the Banyumas area of Indonesia. JMTT publishes at least <strong>3 times</strong> a year with issues including multimedia, information technology, digital media, multimedia information systems, and digital media platforms.</p> <p align="justify">Together with the multimedia and information technology family, JMTT provides the latest issues related to what technologies are currently becoming popular in the multimedia world. This journal also provides the latest reference facilities for later scientific article writers. </p> </td> </tr> </tbody> </table> <p align="justify"> </p> Universitas Amikom Purwokerto en-US Journal of Multimedia Trend and Technology 2964-1330 https://journal.educollabs.org/index.php/JMTT/article/view/145 <p>Selecting the right management multimedia application is a crucial strategic decision for organizations to optimize operations and minimize implementation risks. However, the vast array of application alternatives with diverse evaluation criteria often creates complexity in the decision-making process. This study aims to map the trends in Decision Support System (DSS) methods and identify the key criteria influencing management application selection through a Systematic Literature Review (SLR) approach. Based on the analysis of primary literature published between 2021 and 2026, it was found that the Analytical Hierarchy Process (AHP) and Simple Additive Weighting (SAW) methods are the most dominant approaches used. The results indicate that usability and cost efficiency are the primary determining factors consistently appearing across all reviewed literature, outweighing mere technical sophistication. These findings provide a framework for management practitioners to develop more effective vendor evaluation instruments by prioritizing the balance between functional capabilities and human resource operational readiness.</p> Zahran Muhyiddin Noval Khalifah Mohamad Zidan Copyright (c) 2026 Muhyiddin , Noval Khalifah, Mohamad Zidan https://creativecommons.org/licenses/by-nc-sa/4.0 2026-04-29 2026-04-29 5 1 1 8 10.35671/jmtt.v5i1.145 https://journal.educollabs.org/index.php/JMTT/article/view/82 <p>This study aims to analyze user review sentiments for six tourist attractions in East Nusa Tenggara Province by utilizing a large amount of review data obtained from Google Maps. Data was collected through a scraping process using Serp API, followed by cleaning and text pre-processing to improve data quality. Sentiment labeling was performed automatically using the Indo-BERT model to obtain three sentiment classes: positive, negative, and neutral. Text feature representation was performed using the Term Frequency–Inverse Document Frequency (TF-IDF) method, then classified using the baseline Support Vector Machine (SVM) model and the optimized SVM model with Grid-Search CV. The evaluation results showed that the baseline SVM model produced an accuracy of 83.87%, but showed an imbalance in performance between classes with a Macro F1-score of 0.4287. After parameter optimization using Grid-Search CV, the optimized SVM model produced an accuracy of 78.27% with an increase in the Macro F1-score value to 0.4818. This increase indicates an improvement in the model's ability to recognize minority sentiment classes despite a decrease in overall accuracy. Overall, the optimized SVM model provides more balanced and representative classification results in describing tourists' perceptions based on online reviews, so it can be used as a basis for sentiment analysis in the tourism sector.</p> Aulia Dian Agustina Primandani Arsi Pungkas Subarkah Irfan Santiko Copyright (c) 2026 Aulia Dian Agustina, Primandani Arsi, Pungkas Subarkah, Irfan Santiko https://creativecommons.org/licenses/by-nc-sa/4.0 2026-04-29 2026-04-29 5 1 9 22 10.35671/jmtt.v5i1.82