Jurnal Pilar Nusa Mandiri http://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> INTEREST ANALYSIS OF USING FINTECH OVO WITH TAM MODEL ON MSMEs IN DENPASAR CITY http://ejournal.nusamandiri.ac.id/index.php/pilar/article/view/2757 <p>The development of technology has now been felt in almost various sectors, one of which is the financial sector. Financial Technology (Fintech) is an innovation in the financial sector that can accelerate the process of financial services, one of which is digital payments. OVO is a type of digital payment with the widest acceptance in Indonesia because it has several partnerships, one of which is with MSMEs. Even so, the use of digital payments is still not massive among MSMEs. The reasons are, among others, the unsupported infrastructure and the perceived usefulness of the digital payment. This study aims to determine the factors that can affect the use of technology using a modified Technology Acceptance Model (TAM) by adding 2 external variables (system quality and culture). The method used is a quantitative method. The research data used primary data obtained directly from the respondents by distributing questionnaires. The data in this study will be analyzed using the PLS-based SEM method using the SmartPLS statistical tool. The results of this study show that as many as seven hypotheses can have a positive and significant effect on interest in using Fintech OVO, while there is one hypothesis that has a positive but not significant effect on interest in using Fintech OVO, namely the effect of perceived easy of use on perceived usefulness.</p> Diah Rachmafalen Amor Waning Ni Made Estiyanti I Gst. Agung Pramesti Dwi Putri ##submission.copyrightStatement## http://creativecommons.org/licenses/by-nc/4.0 2022-03-09 2022-03-09 18 1 1 8 10.33480/pilar.v18i1.2757 CLASSIFICATION OF BLOOD DONOR DATA USING C4.5 AND K-NEAREST NEIGHBOR METHODS (CASE STUDY: UTD PMI BALI PROVINCE) http://ejournal.nusamandiri.ac.id/index.php/pilar/article/view/2790 <p>Classification of blood donor data at UTD PMI Bali Province by applying the C4.5 and K-Nearest Neighbor algorithms. The number of blood donor data donors is 34,948, of which 90% of the data, namely 31,454 is used as training data. Meanwhile, 10% of the data, which is 3,494 data, is used as the implementation of data testing using the Orange application. C4.5 obtained an accuracy score of 92.9%, F1 of 92.2%, Precision of 93.1%, Recall of 92.9%, specificity of 68.2%. While K-nearest neighbor obtained an accuracy score of 91%, F1 of 90.1%, Precision of 90.8%, Recall of 91%, specificity of 63%. With the AUC (Area Under Curve) value for the C4.5 algorithm is 0.875 and the K-nearest neighbor is 0.813 Good Classification. The results of the evaluation using the confusion matrix C4.5 obtained an accuracy score of 92.6%, F1 of 95.7%, Precision of 99.4%, Recall of 92.4%, specificity of 96%. While k-nearest neighbor obtained an accuracy score of 90.9%, F1 of 94.6%, Precision of 98.4%, Recall of 91.2%, specificity of 88.4%. Based on the evaluation of the confusion matrix and the ROC Analysis Graph, the C4.5 algorithm obtained higher results than the K-Nearest Neighbor algorithm. Based on the data on the characteristics of blood donors at UTD PMI Bali Province, it shows that the gender is male, Badung area, Age 20 to &lt; 30, the occupation of private employees dominates in blood donation.</p> Ni Ketut Melly Astuti Nengah Widya Utami I Gede Putu Krisna Juliharta ##submission.copyrightStatement## http://creativecommons.org/licenses/by-nc/4.0 2022-03-09 2022-03-09 18 1 9 16 10.33480/pilar.v18i1.2790 IDENTIFICATION OF BACTERIAL SPOT DISEASES ON PAPRIKA LEAVES USING CNN AND TRANSFER LEARNING http://ejournal.nusamandiri.ac.id/index.php/pilar/article/view/2755 <p>Paprika, often called bell peppers, is a plant with the Latin name Capsicum annuum var. gross. Paprika in Indonesia has a high selling value, so the opportunity for cultivating the paprika plant itself is enormous. However, the cultivation of this plant cannot be separated from the threat of disease that can affect the yield of paprika. Bacterial spot is one of them, and it is a disease that is very dangerous for paprika plants because the disease infects all parts of the plant. In this case, early detection is needed to carry out appropriate treatment to minimize the effects caused by bacterial spots. Detection of bacterial spots on paprika can be done by direct observation or conducting laboratory tests, but this requires people who have the appropriate knowledge and experience. Based on the above problems, the identification system can be an option in identifying bacterial spot disease in paprika. This research chose the Convolutional Neural Network (CNN) algorithm in the identification system. Because CNN is one of the algorithms that can receive output in the form of an image which is very suitable for the case of bacterial spots on peppers, this research dataset is divided into healthy leaves and leaves infected with bacterial spots. In this study, the implementation of CNN with transfer learning obtained results from a test accuracy of 90%, training accuracy 97% with a loss of 8.5%, validation accuracy of 97.5% with a loss of 6.9%.</p> M. Ilhamsyah Ultach Enri ##submission.copyrightStatement## http://creativecommons.org/licenses/by-nc/4.0 2022-03-09 2022-03-09 18 1 17 24 10.33480/pilar.v18i1.2755 IMPLEMENTATION OF INFERENCE ENGINE WITH CERTAINTY FACTOR ON POTENTIAL DIAGNOSIS OF BRAIN TUMOR DISEASE http://ejournal.nusamandiri.ac.id/index.php/pilar/article/view/2947 <p>An expert system is a system that has the ability of experts or experts who master a <strong>particular</strong> field<strong> to</strong> assist in solving problems. Certainty factor (CF) is one of the methods in an expert system that <strong>can</strong> define the level of certainty based on facts <strong>to</strong> show the level of confidence of the expert. This study aims to apply a certainty factor (CF) algorithm to solve the problem of diagnosing potential human brain tumors. Because the symptoms that are felt are not necessarily brain tumors, it is necessary to analyze whether the person has the potential to have a brain tumor or not, even if the potential level is. Brain tumor disease is one of several types of dangerous <strong>condition</strong>s. This disease is caused by <strong>the </strong>abnormal growth of cells around the brain. This research produces an application that can diagnose potential brain tumor diseases based on symptom input selected by the user. Then the expert system <strong>can</strong> display the <strong>diagnosis result</strong>s in percentages and solutions from the results of the diagnosis. The <strong>study results</strong> indicate that the CF method can solve the problem of uncertainty by giving a degree of confidence from an expert and system user. The <strong>accuracy test results</strong> resulted in an accuracy value reaching 95%. These results indicate that the system can function and <strong>can</strong> diagnose potential brain tumor diseases properly</p> Trinugi Wira Harjanti ##submission.copyrightStatement## http://creativecommons.org/licenses/by-nc/4.0 2022-03-09 2022-03-09 18 1 25 30 10.33480/pilar.v18i1.2947 IMPLEMENTATION OF MOORA METHOD FOR DECISION SUPPORT SYSTEM SCHOLARSHIP SELECTION IN SMK MUHAMMADIYAH PRAMBANAN http://ejournal.nusamandiri.ac.id/index.php/pilar/article/view/2261 <p>Decision Support System for Scholarship Selection at SMK Muhammadiyah Prambanan Using the MOORA Method aims to implement the Multi-Objective Optimization method on the basis of Ration Analysis. In determining scholarship recipients based on predetermined criteria and building a system in the form of a website to help provide alternative decisions in determining the acceptance of scholarships at SMK Muhammadiyah Prambanan.&nbsp;Based on the source of the data obtained, using primary data including interview and observation methods supported by secondary data obtained by literature studies that are relevant to the problem. Scholarship data is calculated and then ranked based on the final value generated from the MOORA calculation.&nbsp;The process of scholarships selection is based on criteria including report card grades, dependents of parents, the income of parents, percentage of attendance, and the number of siblings. The results of this study are the Scholarship Selection Decision Support System Using the MOORA Method, where the final value in the form of an alternative that has the greatest preference value will be placed at the top rank. The alternative will be a recommendation to receive a scholarship<strong>.</strong></p> Dinar Abdi Perdana Donni Prabowo Bety Wulan Sari ##submission.copyrightStatement## http://creativecommons.org/licenses/by-nc/4.0 2022-03-09 2022-03-09 18 1 31 36 10.33480/pilar.v18i1.2261 DATA MINING USING RANDOM FOREST, NAÏVE BAYES, AND ADABOOST MODELS FOR PREDICTION AND CLASSIFICATION OF BENIGN AND MALIGNANT BREAST CANCER http://ejournal.nusamandiri.ac.id/index.php/pilar/article/view/2912 <p>This study predicts and classifies benign and malignant breast cancer using 3 classification models. The method used in this research is Random Forest, Naïve Bayes and AdaBoost. The prediction results get Random Forest = 100%, Naïve Bayes = 80% and AdaBoost = 80%. Results using Test and Score with Number of Folds 2, 5 and 10. Number of Folds 2 Random Forest model Accuracy = 95%, Precision = 95% and Recall = 95%, Naïve Bayes Accuracy = 93%, Precision = 93% and Recall 93%, AdaBoost Accuracy = 90%, Precision = 90% and Recall = 90%. With Number of Folds 5 with Random Forest = 96%, Precision = 96% and Recall 96%. Naïve Bayes Accuracy value = 94%, Precision = 94% and Recall = 94%, AdaBoost Accuracy value = 93%, Precision = 93% and Recall = 93%. With Number of Folds 10 Random Forest model = 96%, Precision = 96% and Recall 96%. Naïve Bayes Accuracy value = 94%, Precision = 94% and Recall = 94%, AdaBoost Accuracy value = 92%, Precision = 92% and Recall = 92%. Of the 3 models used, Random Forest got the best classification results compared to the others.</p> Bahtiar Imran Hambali Hambali Ahmad Subki Zaeniah Zaeniah Ahmad Yani Muhammad Rijal Alfian ##submission.copyrightStatement## http://creativecommons.org/licenses/by-nc/4.0 2022-03-09 2022-03-09 18 1 37 46 10.33480/pilar.v18i1.2912 DECISION SUPPORT SYSTEM FOR PALM PLANTATION LAND SELECTION USING THE TOPSIS METHOD http://ejournal.nusamandiri.ac.id/index.php/pilar/article/view/2950 <p>Palm oil is an important export commodity in Indonesia. Quality palm oil is produced from quality oil palm plants as well. One of the factors to be able to produce quality oil palm plantations requires the right land. For this reason, land selection is an important factor. This study aims to develop a decision support system by implementing the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method for the selection of oil palm plantations. The TOPSIS method is a negative approach that is obtained by considering the shortest distance from the positive ideal solution and the farthest from the ideal solution. The system is built using a waterfall system development approach that starts from analysis, design, coding and testing. The developed system has the ability to manage alternatives, manage criteria, perform alternative calculations with TOPSIS, and display the best alternative results with TOPSIS. From the results of black-box testing, it proves that the developed system can work and run well. In addition, the results of manual calculations with the system show the same results.</p> Rini Nuraini Liesnaningsih Liesnaningsih Nurdiana Handayani Hengki Rusdianto ##submission.copyrightStatement## http://creativecommons.org/licenses/by-nc/4.0 2022-03-09 2022-03-09 18 1 47 52 10.33480/pilar.v18i1.2950