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: <a href="https://issn.brin.go.id/terbit/detail/1180425463" target="_blank" rel="noopener">1978-1946</a> and E-ISSN: <a href="https://issn.brin.go.id/terbit/detail/1452590194" target="_blank" rel="noopener">2527-6514</a>. 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 : Journal of Computing and Information System, 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> en-US <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> jurnal.pilar@nusamandiri.ac.id (Siti Nurhasanah Nugraha) jurnal.pilar@nusamandiri.ac.id (BTI) Fri, 01 Sep 2023 00:00:00 +0700 OJS 3.1.1.4 http://blogs.law.harvard.edu/tech/rss 60 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 https://ejournal.nusamandiri.ac.id/index.php/pilar/article/view/4176 Fri, 01 Sep 2023 00:00:00 +0700 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 https://ejournal.nusamandiri.ac.id/index.php/pilar/article/view/4420 Fri, 01 Sep 2023 00:00:00 +0700 Disease Detection of Rice and Chili Based on Image Classification Using Convolutional Neural Network Android-Based https://ejournal.nusamandiri.ac.id/index.php/pilar/article/view/4669 <p>The current development of machine learning makes it easier for humans to obtain information, especially from images. The presence of processing assistance from machines can increase the accuracy of the information provided to further convince the recipient of the information. Rice and chili farmers in Indonesia have experienced many disease attacks from several types of plant diseases. Not many farmers understand and are good at guessing the diseases that attack their rice and chili plants. So many rice and chili farmers experienced crop failure. This research aims to build a disease-detection system for rice and chili plants based on Android-based image classification. The machine learning method used is Convolutional Neural Network (CNN) with the Mobile Net version one model combined with the Sequential CNN and Tensor Flow Lite models. The results of the transfer learning evaluation on the Mobile Net version 1 model and the sequential CNN model obtained training accuracy of 0.88% with a loss of 0.34%, validation accuracy of 0.84% with a loss of 0.40%, and testing accuracy of 86% with a loss of 43%. Each uses batch 69 of the total training data stopping at epoch 30 from epoch 100. The results of field testing on the application of rice and chili disease detection on 20 images of rice and chili plants can detect Rice Neck Blast disease with a probability of 75% to 100% and Rice Hispa with a probability of 97% to 100%. It can also detect chili plant diseases such as Chili Yellowish with a probability of 83%, Chili Leaf Spot with a probability of 99%, Chili Whitefly with a probability of 91% to 95, Chili Healthy with a probability of 78% to 99%, and Chili Leaf Curl with a probability 75 to 76%. The probability obtained varies according to how likely damage is to rice and chili plants. CNN with the Mobile Net version one model and the Sequential model can extract and classify images so that it has maximum information processing capabilities. This research can make it easier to help farmers identify diseases that attack their rice and chili plants.</p> <p>&nbsp;</p> Rudi Muslim, Zaeniah Zaeniah, Ardiyallah Akbar, Bahtiar Imran, Zaenudin Zaenudin ##submission.copyrightStatement## http://creativecommons.org/licenses/by-nc/4.0 https://ejournal.nusamandiri.ac.id/index.php/pilar/article/view/4669 Fri, 01 Sep 2023 00:00:00 +0700 Implementation of Smarter Method for Prospective Student Council Selection System SMK Negeri 1 Rembang https://ejournal.nusamandiri.ac.id/index.php/pilar/article/view/4591 <p>One of the schools that has attempted to make the student council active and the primary platform for student development to encourage student activities at school is SMK Negeri 1 Rembang. OSIS administrators can execute numerous labor programs in both academic and non-academic domains. Participants must pass several selection processes to join the SMK Negeri 1 Rembang OSIS board. This student council board's election procedure still employs manual methods. The selection procedure may take longer and allow for subjective evaluations depending on the number of candidates and the criteria used. As a result, it is essential to develop a decision support system (SPK) that uses Rank Order Centroid (ROC) weighting and the Simple Multi-Attribute Rating Technique Exploiting Rank (SMARTER) method to help choose student council administrators. The SMARTER technique addressed disproportionality because the weights assigned do not provide a hierarchy or order of importance between the current criteria and their sub-criteria. Based on the computation of the final value of the standards and sub-criteria on each alternative, the system produces results in the form of the biggest to most minor order. Blackbox testing of this program demonstrates that it can operate and be used at SMK N 1 Rembang both in terms of functionality and outcomes from the system.</p> <p>&nbsp;</p> Bety Wulan Sari, Donni Prabowo, Wahyu Puji Lestari ##submission.copyrightStatement## http://creativecommons.org/licenses/by-nc/4.0 https://ejournal.nusamandiri.ac.id/index.php/pilar/article/view/4591 Fri, 01 Sep 2023 00:00:00 +0700 Physical Violence Detection System to Prevent Student Mental Health Disorders Based on Deep Learning https://ejournal.nusamandiri.ac.id/index.php/pilar/article/view/4600 <p>Physical violence in the educational environment by students often occurs and leads to criminal acts. Apart from that, repeated acts of physical violence can be considered non-verbal bullying. This bullying can hurt the victim, causing physical disorders, mental health, impaired social relationships and decreased academic performance. However, monitoring activities against acts of violence currently being carried out have weaknesses, namely weak supervision by the school. A deep Learning-based physical violence detection system, namely LSTM Network, is the solution to this problem. In this research, we develop a Convolutional Neural Network to detect acts of violence. Convolutional Neural Network extracts features at the frame level from videos. At the frame level, the feature uses long short-term memory in the convolutional gate. Convolutional Neural Networks and convolutional short-term memory can capture local spatio-temporal features, enabling local video motion analysis. The performance of the proposed feature extraction pipeline is evaluated on standard benchmark datasets in terms of recognition accuracy. A comparison of the results obtained with state-of-the-art techniques reveals the promising capabilities of the proposed method for recognising violent videos. The model that has been trained and tested will be integrated into a violence detection system, which can provide ease and speed in detecting acts of violence that occur in the school environment.</p> Sukmawati Anggraeni Putri, Achmad Rifai, Imam Nawawi ##submission.copyrightStatement## http://creativecommons.org/licenses/by-nc/4.0 https://ejournal.nusamandiri.ac.id/index.php/pilar/article/view/4600 Fri, 01 Sep 2023 00:00:00 +0700 Design and Implementation of IoT Based Smart Lecture Attendance System at Mataram University of Technology https://ejournal.nusamandiri.ac.id/index.php/pilar/article/view/4608 <p>Student attendance is one of the reporting activities that exist in educational institutions. The problem that occurs in educational institutions is that when entering the lecture, many students are late and often absent, which can cause discipline where students often do absenteeism, so lecturers cannot know the number of students who attend accurately. From these problems, a solution is needed to help lecturers recapitulate attendance data. This system uses ESP32 as a data manager, RFID for data reading, and ESP32 to validate student attendance by taking pictures of faces. The data is stored on the web server using ESP32CAM to cover the shortcomings of RFID, which is still card-based<strong>,</strong> so that it can emphasize the flaws. To simplify the attendance in this study, utilizing the website as an interface to facilitate lecturers in knowing the number of students who are present, late, or absent more efficiently and accurately</p> Ardiyallah Akbar, Zaenudin Zaenudin, Ahmad Yani, Rudi Muslim ##submission.copyrightStatement## http://creativecommons.org/licenses/by-nc/4.0 https://ejournal.nusamandiri.ac.id/index.php/pilar/article/view/4608 Fri, 01 Sep 2023 00:00:00 +0700 BALI TOURIST VISITS CLUSTERED VIA TRIPADVISOR REVIEWS USING K-MEANS ALGORITHM https://ejournal.nusamandiri.ac.id/index.php/pilar/article/view/4571 <p><em>Bali is one of the provinces with the most popular destinations for tourists. However, there are obstacles in developing tourist destinations in the province of Bali in terms of absorbing more tourist visits. Tripadvisor, the world's largest tourism information platform. In order to improve its service to users, Tripadvisor conducts online reviews to obtain ratings based on travel experience. The purpose of this study is to determine clustering and accuracy in tourist visits to tourist destinations in the province of Bali. Clustering is done using 3 clusters using the KDD method. The first process is data selection, then data processing which consists of several stages, first deleting rows of empty data, then cleaning duplicate data and the final result is 5261 clean data then data transformation, so that data can be read by python, The next process is data mining, this process uses the K-Means clustering algorithm which produces 3 clusters with cluster 1 being medium with 1495 data, high cluster 2 with 2315 data, and low cluster 3 with 1451 data. The Davies Boldin Index is used to evaluate the K-Algorithm means clustering, the result is 0.3 where the value is very good because it is not minus and the value is close to zero.</em></p> Ufik Alngatiq Hidayat Wamulkan A.S, Nengah Widya Utami, I Nyoman Yudi Anggara ##submission.copyrightStatement## http://creativecommons.org/licenses/by-nc/4.0 https://ejournal.nusamandiri.ac.id/index.php/pilar/article/view/4571 Sat, 30 Sep 2023 00:00:00 +0700