Pilar Nusa Mandiri : Journal of Computing and Information System https://ejournal.nusamandiri.ac.id/index.php/pilar <p>The PILAR Nusa Mandiri Journal is a formation of the Information Systems study program, which was originally a medium for accommodating scientific writings of STMIK 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 has become a Rank 3 Accredited Journal&nbsp;and is trying to become a higher accredited journal. PILAR Journal Nusa Mandiri 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 Accreditation Certificate (S3), 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 Number 85 / M / KPT / 2020, April 1, 2020</span></span></p> PPPM Nusa Mandiri en-US Pilar Nusa Mandiri : Journal of Computing and Information System 1978-1946 <div class="page"> <p>The Authors submitting a manuscript do so on the understanding that if accepted for publication, copyright of the article shall be assigned to the PILAR Nusa Mandiri journal as the publisher of the journal, and the author also holds the copyright without restriction.</p> <p>Copyright encompasses exclusive rights to reproduce and deliver the article in all form and media, including reprints, photographs, microfilms, and any other similar reproductions, as well as translations. The reproduction of any part of this journal, its storage in databases, and its transmission by any form or media, such as electronic, electrostatic and mechanical copies, photocopies, recordings, magnetic media, etc. , are allowed with written permission from the PILAR Nusa Mandiri journal.</p> <p>PILAR Nusa Mandiri journal, the Editors and the Advisory International Editorial Board make every effort to ensure that no wrong or misleading data, opinions, or statements be published in the journal. In any way, the contents of the articles and advertisements published in the PILAR Nusa Mandiri journal are the sole and exclusive responsibility of their respective authors and advertisers.</p> </div> COMPARISON OF LINEAR REGRESSIONS AND NEURAL NETWORKS FOR FORECASTING ELECTRICITY CONSUMPTION https://ejournal.nusamandiri.ac.id/index.php/pilar/article/view/1459 <p>Electricity has a major role in humans that is very necessary for daily life. Forecasting of electricity consumption can guide the government's strategy for the use and development of energy in the future. But the complex and non-linear electricity consumption dataset is a challenge. Traditional time series models in such as linear regression are unable to solve nonlinear and complex data relationship problems. While neural networks can overcome the problems of nonlinear and complex data relationships. This was proven in the experiments in this study. Experiments carried out with linear regressions and neural networks on the electricity consumption dataset A and the electricity consumption dataset B. Then the RMSE results are compared on the linear regressions and neural networks of the two datasets. On the electricity consumption dataset A obtained by RMSE of 0.032 used the linear regression, and RMSE of 0.015 used the neural network. On the electricity consumption dataset B obtained by RMSE of 0.488 used the linear regression, and RMSE of 0.466 used the neural network. The use of neural networks shows a smaller RMSE value compared to the use of linear regressions. This shows that neural networks can overcome nonlinear problems in the electricity consumption dataset A and the electricity consumption dataset B. So that the neural networks are afford to improve performance better than linear regressions.</p> Tyas Setiyorini Frieyadie Frieyadie ##submission.copyrightStatement## http://creativecommons.org/licenses/by-nc/4.0 2020-09-08 2020-09-08 16 2 135 140 10.33480/pilar.v16i2.1459 APPLICATION OF BACKPROPAGATION NEURAL NETWORK ALGORITHM FOR CIHERANG RICE IMAGE IDENTIFICATION https://ejournal.nusamandiri.ac.id/index.php/pilar/article/view/1500 <p>Rice is a food source for carbohydrates that are most consumed in Indonesia, because of this the production is higher compared to other food crops. There are several superior rice varieties planted by the farmers, one of them is Ciherang. This type is widely planted by farmers because has high selling as economic value and can be used as premium rice. The existence of several types of rice that had a high sales value makes some person was deceitfulness by mix the rice with premium quality with bad quality. Many people do not know the problem of distinguishing types of rice from one to another that has the same shape. Classification techniques using the backpropagation neural network algorithm and image processing are used to identify one of the most preferred types of rice, Ciherang. The network architecture model on the backpropagation algorithm is very influential on the value of accuracy. In determining the best network’s architectures, 4 times attempted where network architecture with 5 nodes in the input layer, 8 nodes in the hidden layer, and 1 node in output layer produce the highest accuracy of 82,66%.</p> Dita Aprilia Jajam Haerul Jaman Riza Ibnu Adam ##submission.copyrightStatement## http://creativecommons.org/licenses/by-nc/4.0 2020-09-08 2020-09-08 16 2 141 148 10.33480/pilar.v16i2.1500 STUDENT PERFORMANCE ANALYSIS USING C4.5 ALGORITHM TO OPTIMIZE SELECTION https://ejournal.nusamandiri.ac.id/index.php/pilar/article/view/1348 <p>Education is one of the fields that generate heaps of data. Pile of data that can utilized by higher education institutions to improve tertiary performance. One way to process data piles in the education is to use data mining or called education data mining. The quality assessment of educational institutions conducted by the community and the government is strongly influenced by student performance. Students who have poor performance will have a negative impact on educational institutions. Student data is processed to obtain valuable knowledge regarding the classification of student performance. One method of data mining is the C4.5 algorithm which is known to be able to produce good classifications. In this research and optimization method will be used namely optimize selection on the c4.5 algorithm. Based on the research, it is known that the optimization selection optimization method can improve the performance of algorithm c4.5 from 85% to 87%.</p> Hilda amalia Yunita Yunita Ari Puspita Ade Fitria Lestari ##submission.copyrightStatement## http://creativecommons.org/licenses/by-nc/4.0 2020-09-08 2020-09-08 16 2 149 154 10.33480/pilar.v16i2.1348 EDUCATIONAL DATA MINING FOR STUDENT ACADEMIC PREDICTION USING K-MEANS CLUSTERING AND NAÏVE BAYES CLASSIFIER https://ejournal.nusamandiri.ac.id/index.php/pilar/article/view/1432 <p>This study proposes the merging of the K-Means clustering data mining method and the Naïve Bayes classifier (K-Means Bayes) for better results in data processing for Student Academic Performance data. Data was taken from the Student Academic Performance dataset which is used as a test case. The amount of data used in this study were 131 data and 21 attributes. The accuracy of the results obtained from the combination of the proposed method is 97.44%. The results obtained when compared with calculations using the K-Means method and calculations using the Naïve Bayes method, the proposed method (K-Means Bayes) gives better results. Although the initial centroid determination on the K-Means method is done randomly, the impact can be reduced by adding the Naive Bayes classifier method which results in a better accuracy value, thereby increasing the accuracy of the method used. Compared to the K-Means and Naïve Bayes methods, the proposed method increases the accuracy of about 27% of the Naïve Bayes algorithm and about 23% of the K-Means algorithm. With the results obtained, it can be concluded that the proposed method can improve predictions of student academic performance data. The initial centroid determination for grouping in the K-Means method can affect the quality of the accuracy of the data produced</p> Dewi Ayu Nur Wulandari Riski Annisa Lestari Yusuf Titin Prihatin ##submission.copyrightStatement## http://creativecommons.org/licenses/by-nc/4.0 2020-09-08 2020-09-08 16 2 155 160 10.33480/pilar.v16i2.1432 ANALYSIS OF INTER-RELIGIOUS TOLERANCE SENTIMENTS IN INDONESIA ON CONVERSATIONS ON SOCIAL MEDIA TWITTER https://ejournal.nusamandiri.ac.id/index.php/pilar/article/view/1520 <p>Conversations on social media Twitter related to tolerance among religious communities in Indonesia are fascinating. However, it is a sensitive issue. In reality, there is often a war of comments about the implementation of tolerance between religious people in carrying out their own beliefs. The community is not careful in issuing opinions that can result in social insecurity, insecurity, and national instability. This condition will significantly affect the state of the country's economy. In some cases, political problems can be a trigger for intolerance between religious communities. The purpose of this study is to compare the performance of classification accuracy on positive or negative sentiments from conversations that intersect with the problem of tolerance among religious communities during the past year. In this study, we compared the performance of the accuracy of the modeling of sentiment analysis classification on public conversations on social media Twitter related to tolerance between religious communities in Indonesia. Because the text that will be carried out modeling comes from the Indonesian language, to facilitate labeling, translation is carried out into English, then a performance comparison of the sentiment analysis classification modeling with SVM algorithm, Naïve Bayes, Decision Tree, and k-NN. Based on the experiments, it was concluded that the SVM algorithm has the highest performance for the classification of sentiment analysis categories up to 65.03% compared to the Naïve Bayes algorithm, which reached 59.92%, Decision Tree, which reached 63.52% and k-NN which reached 57 66%.</p> Yogie Pribadi Noor Hafidz Yamin Nuryamin Windu Gata ##submission.copyrightStatement## http://creativecommons.org/licenses/by-nc/4.0 2020-09-08 2020-09-08 16 2 161 168 10.33480/pilar.v16i2.1520 TWITTER SENTIMENT ANALYSIS OF POST NATURAL DISASTERS USING COMPARATIVE CLASSIFICATION ALGORITHM SUPPORT VECTOR MACHINE AND NAÏVE BAYES https://ejournal.nusamandiri.ac.id/index.php/pilar/article/view/1423 <p>Natural disasters trigger people, especially Twitter users to provide information or opinions in the form of tweets. The Tweet can be an expression of sadness, concern, or complaint. Processing of data from these tweets will create trends that can be used for information needs such as education, economics, and others. Natural disasters are events that threaten human life caused by nature, including in the form of earthquakes. The method used is the Support Vector Machine and Naive Bayes from the tweet. The data collected is filtered from tweets by deleting duplicate data. In calculating the Natural Disaster sentiment analysis using a comparison of the Support Vector Machine and the Naive Bayes algorithm, the difference in accuracy is 3.07% where the results of the Support Vector Machine are greater than Naive Bayes. The purpose of this research is to analyze sentiment for the distribution of disaster aid that does not flow information due to information &amp; coordination in the field. so as to provide information on the location of natural disasters, natural disaster management, and its presentation to victims that can be shared evenly in an efficient time due to information and natural management so that the distribution of aid is hampered</p> Ainun Zumarniansyah Rangga Pebrianto Normah Normah Windu Gata ##submission.copyrightStatement## http://creativecommons.org/licenses/by-nc/4.0 2020-09-15 2020-09-15 16 2 169 174 10.33480/pilar.v16i2.1423 SURVEY PAPER: SOFTWARE AUTOMATED TESTING TOOL USING SYSTEMATIC LITERATURE REVIEW METHOD https://ejournal.nusamandiri.ac.id/index.php/pilar/article/view/1456 <p><em>Software testing is one of the most important roles in successful software development. The increasing complexity of software development requires the development team to use automated testing tools to test the quality and functionality of the application. In software testing, choosing a testing tool must be appropriate and in accordance with the software to be tested. Using the Systematic Literature Review method, this research collects and analyzes previous survey papers from 2 keywords, "Comparative of Automated Software Testing Tools" and "A Critical Analysis of Automated Software Testing Tools" on Google Scholar with a span of 2010 - 2020. Results from papers collected through 5 stages of the SLR, resulting in 11 papers that were reviewed. From these 11 journals, obtained 5 automated testing tools that are often discussed, Selenium, QTP, TestCompelete, Watir, and Ranorex. The results of this study indicate that there is no software automatic testing tool that is truly perfect because every automated software testing tool has its own strengths and weaknesses and it is expected that the results of this study can help software testers in choosing automatic testing tools according to their needs.</em></p> Dheanda Absharina Fahirah Fahirah Fenni Agustina ##submission.copyrightStatement## http://creativecommons.org/licenses/by-nc/4.0 2020-09-16 2020-09-16 16 2 175 182 10.33480/pilar.v16i2.1456 INFORMATION SYSTEM VALENT FOR PT ENSEVAL PUTERA MEGATRADING ON MOBILE USING EXPO https://ejournal.nusamandiri.ac.id/index.php/pilar/article/view/1499 <p>Every year, routinely PT. Enseval Putera Megatrading held several events which were held face-to-face and online and involved many parties, from the employees' board of directors and external parties as sources. The events held include: National Work Meeting (RAKERNAS), Mid Year Work Meeting, Product Launching, EMDP Graduation, Enseval Anniversary, Innovation Forum, and others. There are at least 3 stages that the Event Owner must go through in organizing and achieving the goals of an event, pre-event starting from the formation of the committee to determining the concept of the event that will take place, running events starting from registering participants who are present in the event to maintaining the event's run down, post events ranging from gathering feedback on events to publishing materials. An event management system is absolutely necessary to support the success of the event. Valent, which is a React Native based mobile application, is the right step to answer this need. React native is an application that is used using the JavaScript programming language to create applications in developing applications on smartphones based on Android and iOS. (Zammetti, 2018) This allows Valent to run on 2 OSes at once (Android &amp; IOS) so that all event participants can enjoy the benefits of the Valent application directly from their respective smartphones.</p> Sean Michael ##submission.copyrightStatement## http://creativecommons.org/licenses/by-nc/4.0 2020-09-15 2020-09-15 16 2 183 190 10.33480/pilar.v16i2.1499 CONSTRUCTING MOODLE-BASED ONLINE LEARNING FOR VOCATIONAL SCHOOL https://ejournal.nusamandiri.ac.id/index.php/pilar/article/view/1481 <p>During the Covid-19 pandemic, every level of educational institutions is demanded to employ online learning based on their condition and capability. Many efforts have been done to sustain the teaching and learning process without a face-to-face system. The characteristic of the learning model in vocational school is identical to education specified in the technical field which encompasses several fields of expertise and is passed down to expertise program and expertise competency which require an integrated system. One of the ways to meet the demand is constructing online learning with a learning management system based on Moodle (Modulator Object-Oriented Dynamic Learning Environment). The aim of this model is to create an effective and integrated learning environment in order to create ease in observation and evaluation. This research used Zachman Framework which was started by determining scope system encompassing data, process and computer network configuration; designing business model by using Use Case Diagram; designing the model of the information system by using Class Diagram, Activity Diagram and &nbsp;Sequences Diagram; designing technology model by creating users’ interface program; and proceed to the implementation by customizing the Moodle software to create Moodle-based online learning which can be used in vocational schools.</p> Dwinita Arwidiyarti Henni Comala Hikmi ##submission.copyrightStatement## http://creativecommons.org/licenses/by-nc/4.0 2020-09-15 2020-09-15 16 2 191 198 10.33480/pilar.v16i2.1481