Jurnal Pilar Nusa Mandiri https://ejournal.nusamandiri.ac.id/index.php/pilar <p>The Pilar Nusa Mandiri: Journal of Computing and Information System Journal is a formation of the Information Systems study program, which was originally a medium for accommodating scientific writings of Universitas Nusa Mandiri Jakarta Information Systems lecturers. Along with the times, this journal has become a National journal that has P-ISSN: 1978-1946 and E-ISSN: 2527-6514. Pilar Nusa Mandiri: Journal of Computing and Information System has become a <strong>Rank 3 Accredited Journal&nbsp;</strong>and is trying to become a higher accredited journal. Pilar Nusa Mandiri: Journal of Computing and Information System is published 2 times in 1 year, namely in March and September. This journal is&nbsp;<span class="tlid-translation translation"><span title="">Rank 3 <strong>Accreditation Certificate (S3)</strong>, Accreditation is valid for 5 years. Starting from Vol. 12, No. 1 the Year 2016 to Vol. 16, No. 2 the Year 2020.&nbsp;Journal of PILAR Nusa Mandiri, re-accreditation remains at Rank 3 (SINTA 3), starting Vol. 15 No. 2 of 2019 based on the Decree of the Minister of Research and Technology / National Research and Innovation Agency <strong>Number 85 / M / KPT / 2020, April 1, 2020</strong>.</span></span></p> LPPM Universitas Nusa Mandiri en-US Jurnal Pilar Nusa Mandiri 1978-1946 <div class="page"> <p>An author who publishes in the Pilar Nusa Mandiri: Journal of Computing and Information System agrees to the following terms:</p> <ol> <li>Author retains the copyright and grants the journal the right of first publication of the work simultaneously licensed under the Creative Commons Attribution-NonCommercial 4.0 License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal</li> <li>Author is able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book) with the acknowledgement of its initial publication in this journal.</li> <li>Author is permitted and encouraged to post his/her work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of the published work (See The Effect of Open Access).<br>Read more about the Creative Commons Attribution-NonCommercial 4.0 Licence here: https://creativecommons.org/licenses/by-nc/4.0/.</li> </ol> </div> Lexicon-Based and Naive Bayes Sentiment Analysis for Recommending the Best Marketplace Selection as a Marketing Strategy for MSMEs https://ejournal.nusamandiri.ac.id/index.php/pilar/article/view/4176 <p>MSMEs (micro, small, and medium enterprises) play an essential role in the Indonesian economy, contributing to 60% of the country's GDP (gross domestic product), creating jobs, and increasing non-oil and gas exports. However, MSMEs in Indonesia face various challenges, including access to technology, digital marketing tools, financial resources, limited market distribution, and low technological literacy. Marketplaces provide an essential marketing channel for MSMEs to increase their competitiveness and sales. Sentiment analysis can assist businesses in making informed decisions about which marketplace to use to increase customer satisfaction. Apart from the importance of the marketplace for MSMEs in Indonesia, research on sentiment analysis for marketplace recommendations is still minimal. Therefore, this study aims to analyze six popular marketplaces in Indonesia using Lexicon-based and naïve Bayes research methods to provide the best marketplace recommendations for MSME marketing. The results showed that Blibli.com had the highest accuracy, followed by Tokopedia, Tiktokshop, Lazada, Shopee, and Bukalapak. Blibli.com received positive reviews with 96.33%, followed by Tokopedia with 95.25%, Tiktokshop with 94.61%, and Lazada with the highest accuracy. 94.22%, Shopee 92.18%, and Bukalapak 89.57%. This research has two significant contributions. First, making a scientific contribution by applying a combination model of lexicon-based and naïve Bayes to analyze market sentiment in Indonesia Second, offering a practical contribution by providing recommendations to MSME actors and policymakers in choosing the best marketplace for MSMEs marketing purposes in Indonesia. By utilizing the recommended marketplace, MSMEs can optimize their marketing strategy and increase their competitiveness in the digital marketplace.</p> Hoiriyah Hoiriyah Helva Mardiana Miftahul Walid Aang Kisnu Darmawan ##submission.copyrightStatement## http://creativecommons.org/licenses/by-nc/4.0 2023-09-01 2023-09-01 19 2 65 76 10.33480/pilar.v19i1.4176 The Prediction Of Product Sales Level Using K-Nearest Neighbor and Naive Bayes Algorithms (Case Study : PT Kotamas Bali) https://ejournal.nusamandiri.ac.id/index.php/pilar/article/view/4420 <p>PT Kotamas Bali is a company that operates in tableware and kitchenware, where every sale is sold at various counters. Product sales are <strong>permanently</strong> printed and entered in sales reports, and there are problems such as the fact that the product is sold very much and it is difficult to see the rate of sale of most products, lots, and not lots. Then, to see the level of product sales, it is necessary to use data mining techniques with the method of Knowledge Discovery in Databases to predict the <strong>purchase rat</strong>e of products using the two algorithms K-Nearest Neighbor and Naïve Bayes. The purpose of this research is so that PT Kotamas Bali can see the sales rate of each product sold so that there is no accumulation of goods and more focus on the <strong>most marketed products</strong>. These two algorithms result in different accuracy on the 90:10 data split, where the K-Nearest Neighbor algorithm successfully predicted the sales rate of the product with a 99% accuracy rate and was categorized as an excellent classification<strong>. T</strong>he Naïve Bayes algorithm failed to make predictions with an accuracy of only 54% and was classified as a failure classification. ROC performance results on the K-Nearest Neighbor algorithm with an AUC value of 99% and the Naïve Bayes algorithm with an AUC of 74%. K-Nearest Neighbor managed to obtain the highest accuracy, while the Naïve Bayes algorithm failed to conduct classification.</p> Aris Setiawan Miha Djami Nengah Widya Utami A. A. Istri Ita Paramitha ##submission.copyrightStatement## http://creativecommons.org/licenses/by-nc/4.0 2023-09-01 2023-09-01 19 2 77 84 10.33480/pilar.v19i2.4420