https://ejournal.nusamandiri.ac.id/index.php/techno/issue/feed Techno Nusa Mandiri: Journal of Computing and Information Technology 2022-01-07T02:20:03-05:00 Nurajijah jurnal.techno@nusamandiri.ac.id Open Journal Systems <p>The TECHNO Nusa Mandiri: Journal of Computing and Information Technology is a journal published by LPPM Universitas Nusa Mandiri. The TECHNO Nusa Mandiri:&nbsp;Journal of Computing and Information Technology was originally intended to accommodate scientific papers made by Informatics Engineering lecturers. TECHNO Nusa Mandiri Journal has ISSN: <a title="Print Media" href="http://issn.pdii.lipi.go.id/issn.cgi?daftar&amp;1180425415&amp;1&amp;&amp;" target="_blank" rel="noopener"><strong>1978-2136</strong></a> (Print Media) and <a title="Online Media" href="http://issn.pdii.lipi.go.id/issn.cgi?daftar&amp;1452590549&amp;1&amp;&amp;" target="_blank" rel="noopener"><strong>2527-676X</strong></a> (Online Media). The TECHNO Nusa Mandiri:&nbsp;Journal of Computing and Information Technology have the accredited National Journal status is accredited by the Indonesian Ministry of Research and Higher Education at the Sinta S4 level, in accordance with Decree on Strengthening SK Research and Development Number 21 / E / KPT / 2018 which has been in effect since July 9, 2018, for 5 years. Source: <a title="Salinan Surat Keputusan Peringkat Akreditasi Elektronik Periode I 2018" href="http://risbang.ristekdikti.go.id/wp-content/uploads/2018/07/Salinan-Surat-Keputusan-Peringkat-Akreditasi-Elektronik-Periode-I-2018.pdf" target="_blank" rel="noopener">Risbang Ristekdikti.go.id</a>. This journal is&nbsp;Rank 4 Accreditation Certificate (S4), Accreditation is valid for 5 years. Starting from Vol. 13, No. 1 the Year 2016 to Vol. 17, No. 1 the Year 2020.&nbsp;<span class="tlid-translation translation"><span title="">Journal of TECHNO Nusa Mandiri, re-accreditation remains at Rank 4 (SINTA 4), starting Vol. 16 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> https://ejournal.nusamandiri.ac.id/index.php/techno/article/view/2222 K-MEANS SEGMENTATION AND CLASSIFICATION OF SWIETENIA MAHAGONI WOOD DEFECTS 2021-10-04T13:08:19-04:00 Sri Rahayu srirahayu.rry@nusamandiri.ac.id Dwiza Riana dwiza@nusamandiri.ac.id Anton Anton anton@nusamandiri.ac.id <p>The potential and usefulness of wood to meet the needs of human life are not in doubt. Demands us to continue to maintain the quality. Wood quality is closely related to wood defects. Manual defect checks in the wood industry are unreliable because they are prone to human error, For example, due to acute symptoms of headaches and tired eyes, technology in the form of image processing can help identify wood defects Swietenia Mahagoni. In this case, the method used is Euclidean distance with a ratio of k-means segmentation and thresholding on 42 images of wood defects consisting of 3 types of defects, namely growing skin defects, rotting knots, and healthy knots, every 14 images with data sharing. training for 30 images and testing for 12 images. The results of the k-means segmentation are then extracted on 6 features including metric, eccentricity, contrast, correlation, energy, and homogeneity using the Gray Level Co-occurrence Matrix (GLCM) extractor and classified by calculating the closest distance using the euclidean distance between the results of data feature extraction. testing of the value of feature extraction in the training data which is used as a previous database. It is the smallest value that indicates the type of defect. The success calculation is presented in the confusion matrix calculation and gets a success or accuracy value of 91.67%.</p> 2021-09-15T00:00:00-04:00 ##submission.copyrightStatement## https://ejournal.nusamandiri.ac.id/index.php/techno/article/view/2399 ANALYSIS OF DEPRESSION IN COLLEGE STUDENT DURING COVID-19 PANDEMIC USING EXTREAM GRADIENT BOOST 2021-11-11T10:01:21-05:00 Agung Prabowo agung.prabowo18082@student.unsika.ac.id Dharma Ajie Nur Rois dharma.ajie18120@student.unsika.ac.id Amar Luthfi amar.luthfi18154@student.unsika.ac.id Ultach Enri ultach@staff.unsika.ac.id <p><em>The Covid-19 pandemic that spreads in Indonesia causes health, economic, and social problems in the community, including mental health. Of course, this mental health problem also hit students. Seeing these conditions, we conducted research on students of the Faculty of Computer Science, University of Singaperbangsa Karawang using the Patient Health Questionnaire-9 which measures a person's level of depression. In this study, we used Extreme Gradient Boost or XGBoost to classify students' depression tendencies. We break down the dataset into training data and testing data with 4 data sharing combinations, they are 80 : 20, 50 : 50, 90 : 10, 70 : 30. The combination of 90 : 10 data sharing has the best performance with accuracy, precision, recall, and F1-scores respectively 92.86%, 94.29%, 92.86% , and 92.06%. This method also has better performance than K-Nearest Neighbor, Random Forest, Multi Layer Perception, Support Vector Machine and Decision Tree</em><em> .</em></p> 2021-09-15T00:00:00-04:00 ##submission.copyrightStatement## https://ejournal.nusamandiri.ac.id/index.php/techno/article/view/2706 APPLICATION OF CALCULATION METHODS MULTI ATRIBUTTE UTILITY THEORY (MAUT) IN SELECTION OF YARN SUPPLIER 2021-12-25T06:08:53-05:00 Susliansyah Susliansyah susliansyah.slx@bsi.ac.id Yahdi Kusnadi yahdi.ydk@bsi.ac.id Heny Sumarno heny_nyno@yahoo.com Hendro Priyono hendrop250@gmail.com Linda Maulida linda.lma@bsi.ac.id <p>The main objective of the yarn supplier selection process is to determine suppliers who have efficiency in meeting the company's needs consistently and minimize risks related to the procurement of yarn and components needed. In solving problems in supplier selection using the Multi Attribute Utility Theory (MAUT) method which consists of calculating matrix normalization and attribute normalization. The results obtained in this study are to find out the best supplier from other suppliers, namely GSM suppliers with a value of 0.87.</p> 2021-09-15T00:00:00-04:00 ##submission.copyrightStatement## https://ejournal.nusamandiri.ac.id/index.php/techno/article/view/2715 COMPARATION OF CLASSIFICATION ALGORITHM ON SENTIMENT ANALYSIS OF ONLINE LEARNING REVIEWS AND DISTANCE EDUCATION 2022-01-07T02:20:03-05:00 Lila Dini Utami lila.ldu@bsi.ac.id Siti Masripah siti.stm@bsi.ac.id <p>As of January 27, 2021, confirmed cases of COVID-19 nationally stood at 1,024,298 people, this data is data that has been officially announced by the Indonesian Ministry of Health. Meanwhile, in Jakarta, there are 256,416 confirmed cases of COVID-19. In July 2021, there was a very significant increase, seeing the data caused the Central government to make a decision to continue the Large-Scale Social Restrictions (PSBB), followed by the Enforcement of Restrictions on Community Activities (PPKM), which affected all aspects, especially the education aspect. In the education aspect, the government applies distance and online learning. Of course, many people agree or disagree with this decision, because there must be sacrifices, both in terms of time and cost. Seeing these conditions makes the authors interested in discussing and processing public opinions on distance and online learning systems which certainly have positive and negative responses from learning implementers, to process the data the author uses Data Mining, namely using the Text Mining Classification method with several The classification algorithms are the Naïve Bayes Algorithm (NB), the k-Nearest Neighbor (k-NN) Algorithm and the Support Vector Machine (SVM) Algorithm to see which classification algorithm has the highest accuracy and diagnostic value in processing this opinion. After the calculations are done, the algorithm that is more suitable for analyzing reviews or opinions in this study is to use the Support Vector Machine (SVM) classification algorithm with the highest accuracy value of 87.67% and an AUC value of 0.939 with an Excellent Classification diagnostic level.</p> 2021-09-15T00:00:00-04:00 ##submission.copyrightStatement## https://ejournal.nusamandiri.ac.id/index.php/techno/article/view/2547 COMPARISON OF ACCURACY MEASUREMENTS IN MOTION SENSORS AND HEART RATE MEASUREMENTS USING ANALYTICAL HIERARCHY PROCESS METHODS 2022-01-07T02:19:33-05:00 Tomi Lifti Novier tomilifti99@gmail.com Nurmalasari Nurmalasari nurmalasari.nmr@nusamandiri.ac.id Widi Astuti widiastuti.wtu@nusamandiri.ac.id Siti Masturoh siti.uro@nusamandiri.ac.id M. Rangga Ramadhan Saelan rangga.mgg@nusamandiri.ac.id <p>The use of motion sensors in measuring heart rate using smartwatch applications is currently a trend. Everyone is very helpful for measuring their own heart rate. This research is about the comparison of accuracy in motion sensors and measuring heart rate using the Analytical Hierarchy Process (AHP) method. Every technology and application in motion sensor measurement in heart rate measurement has almost the same features and uses as Xiaomi, Samsung, and Apple Inc. From the calculations carried out by the researcher, it shows that the field/stadium that is the most chosen by the community (respondents) is by Random Sampling, with the acquisition of a value of 0.490 aka 49.00%. The second is Treadmill with a value of 0.294 aka 29.40%. the overall value is 0.216 aka 21.60% The alternative that is most chosen by the community (respondents) is the field/stadium. The Analytical Hierarchy Process method can make it easier for prospective technology users to be able to measure the accuracy of motion sensors and detect heart rates, the AHP method makes product decisions based on criteria and alternatives contained in the hierarchy, the results of the study are Apple Inc. as the respondent's choice for technology that is trusted to measure better accuracy on the motion sensor and measure heart rate.</p> 2021-09-15T00:00:00-04:00 ##submission.copyrightStatement##