This research is focused on assessing the public opinion of netizens on Twitter regarding the Constitutional Court's decision on the results of the 2024 Presidential Election by utilizing the Naïve Bayes classification algorithm. Twitter is the main social media platform that many people use to voice their opinions, including on political issues. Through the Naïve Bayes classification method, public opinion is divided into two main sentiments: positive and negative. This research began with the collection of comment data through the scraping process, then continued with the pre-processing stages of data which included case folding, tokenizing, normalization, stopword removal, and stemming. The processed data is then manually labeled to form training data. The Naïve Bayes model was trained using the training data, then tested using test data to evaluate the performance of the classification model. The results of the evaluation showed that the model had an accuracy rate of 90%, with precision and recall values in the positive class of 83% each. These findings show that the Naïve Bayes algorithm can effectively classify netizens' sentiments against the Constitutional Court's ruling. In addition, the classification results also show that netizens' opinions tend to be divided, with a slightly higher proportion of negative sentiments compared to positive sentiments. This study also enriches the methods used in digital sentiment analysis, especially in understanding public responses to political issues that develop on social media. The results of this study are expected to be a reference in data-based decision-making on public opinion, especially in the realm of public policy, political communication, and digital information management. Going forward, similar research could be further developed with different algorithms or with a wider scope of data to get a more comprehensive picture.

With technological advancements, digitalization in business has become a crucial factor in enhancing sales effectiveness, especially for micro, small, and medium enterprises (MSMEs). Toko Sakinah, which has been operating for nearly 35 years in Bengkalis, still relies on conventional sales methods, such as WhatsApp transactions or direct store visits. This approach limits market reach and operational efficiency. Therefore, this study aims to design and develop a web-based digital sales system using the Design Sprint method to improve sales effectiveness at Toko Sakinah.The Design Sprint method is applied to develop the system quickly and systematically, focusing on understanding user needs, prototyping, and testing solutions before full implementation. The result of this research is a sales website that facilitates online product ordering, sales reporting, and interaction between buyers and store owners. This website is expected to expand market reach, enhance sales efficiency, and serve as a development model that can be replicated by other MSMEs.

The Communication, Informatics, and Statistics Agency (DISKOMINFOTIK) of Bengkalis is a government institution focused on information and statistics. One of the media used for information dissemination is the DISKOMINFOTIK Bengkalis website. The website of the Communication, Informatics, and Statistics Agency of Bengkalis District offers several information options such as the agency profile, work units, public information, agendas, announcements, contact details of DISKOMINFOTIK Bengkalis, as well as news published by DISKOMINFOTIK Bengkalis. Therefore, the DISKOMINFOTIK Bengkalis website is considered a significant medium for both the agency itself and the public. Hence, software quality becomes an important consideration as it directly affects the website's performance. For this reason, testing needs to be conducted on the DISKOMINFOTIK Bengkalis website. This research addresses 11 factors in McCall, namely Correctness, Reliability, Usability, Integrity, Efficiency, Reusability, Portability, Interoperability, Maintainability, Flexibility, and Testability. Testing is done in two ways: with testing tools and with questionnaires submitted to users and system developers. The results of testing conducted with questionnaires yield quality scores of Correctness 77,66%, Reliability 77,8%, Efficiency 79,01%, Integrity 77,31%, Usability 77,67%, Portability 77,31%, Maintainability 90%, Testability 90%, Flexibility 93,3%, Reusability 95%, Interoperability 90%, so the average quality is 84%.

Marketplace system is a means of meeting sellers and buyers online, in this case service providers and car renters. With this information system, renters no longer need to bother looking for the required car that matches the criteria, and service providers are also easier in marketing and expanding their marketing reach. This system was built using the RAD (Rapid Application Development) method of development which allows customers to place orders, select vehicles, eliminating complexity in the traditional process involving visits to rentals. In car rental services there are several problems that need to be resolved. First, there is no web-based information system service that makes it easy for customers to access car rental service company information. Second, conventional services are still fairly slow. Currently, reservations are made in two ways, namely coming to the place or contacting the telephone number on Whatsapp so that prospective tenants have to wait for a response from the rental provider to get information about prices and available cars. Therefore, a car rental information system is needed that specifically serves fleet rental with online booking to display transaction information in the form of car rental and return on the system dashboard to support strategic purposes. This research focuses on building a car rental information system using the PHP programming language with the CodeIgniter framework.