In a situation that is still being hit by the COVID19 Pandemic until now, it has an impact on the way people meet the needs of life, both primary and secondary needs. In one case, the pandemic also affected the shopping patterns of people who required them to shop online in order to reduce activities outside the home. So in this study the authors conducted research on the topic of Decision Support Systems in the selection of E-Marketplace. In this case, the author applies the VIKOR and Rank Order Centroid methods in assessing several sites or applications that are generally used by the public in online shopping activities. The alternatives used are Shopee, Lazada, Tokopedia, Bukalapak and Facebook Marketplace. For the criteria used, namely by using an assessment based on the User Interface, Product Completeness, Service Response, Transaction Process, and Delivery Service. The test results obtained are based on the calculation of the VIKOR method and the Rank Order Centroid against the alternatives and criteria used, Shoopee as the best alternative by obtaining a VIKOR index value of 1. Thus, it can be seen that the VIKOR method and Rank Order Centroid has been successfully applied to the recommendation system for marketplace selection.
Warts are a skin health problem, usually characterized by small, rough bumps on the surface of the skin caused by a virus, known as the human papillomavirus (HPV). The common way of treating warts is with immunotherapy, which is the treatment of warts by strengthening the body's immune system. In the process of predicting and diagnosing warts, it can be done by applying Machine Learning. This study focuses on the comparison of the K-Nearest Neighbor classification method with Random Forest to see the level of accuracy in predicting the success of the treatment of warts. Data for Immunotheraphy was obtained from the UCI Machine Learning Repository with a total of 90 data records, 7 attributes and 1 attribute class. Based on the results of testing the K-Nearest Neighbor and Random Forest methods to see the accuracy of the prediction of the success of the data being tested, the results obtained are the accuracy of the KNN method of 90.00% and the Random Forest method with an accuracy of 85.50%. From the results obtained from the tests that have been carried out, it is known that the Random Forest method is a better method than K-Nearest Neighbor in predicting accuracy in the Immunotheraphy Dataset.
The problem of infertility between husband and wife is an important issue that destroys family harmony, and many people still consider infertility or infertility a female problem. However, about 7% of men of childbearing age suffer from infertility. The biggest factor causing male infertility is sperm quality problems. Sperm analysis can be the best predictor of male fertility potential. Machine learning and data mining techniques can be used to automate disease diagnosis. This study aims to obtain a regular form classification model from sperm sample data of 100 volunteers. This classification model can be used to predict male fertility levels into 2 classes, namely normal and alter (decreased fertility). This study uses a fertility dataset obtained from the UCI Machine Learning Repository. Before the data mining process, data preprocessing is required. The classification process is carried out using Naive Bayes and attribute reduction techniques using forward selection to see the increase in the accuracy of Naive Bayes performance. The Naive Bayes test without attribute reduction has an accuracy of 85%, while attribute reduction with forward selection has an accuracy of 88% in predicting sperm fertility. Therefore, by using forward selection with Naive Bayes to reduce attributes in this study, this study was able to increase accuracy by 3% and can be used to help predict sperm fertility
Riset ini bertujuan untuk menguji dan menerapkan metode MOORA dalam pengambilan keputusan untuk penentuan perangkingan dari data konsentrasi tingkat kesuburan sperma dan kemudian menggunakan metode pembobotan kriteria berdasarkan iperhitungan metode Rank Order Centroid agar bobot kriteria diperoleh secara sistematis dan obyektif sehingga tidak lagi ditentukan secara subjektif dari asumsi pengambil keputusan. Data pengujian yang digunakan bersumber dari UCI iMachine iLearning iRepository yaitu Fertility Dataset yang merupakan data tingkat konsentrasi kesuburan sperma yang memiliki 100 record data, 9 kriteria, dan 1 variable kelas serta data set tersebut berjenis multivariate. Hasil dari pengujian metode MOORA pada penelitian ini menunjukkan bahwa dengan menerapkan metode MOORA dan Rank Order Centroid mampu dalam melakukan perangkingan terhadap data konsentrasi tingkat kesuburan sperma yang menghasilkan A19 sebagai ialternatif iterbaik dengan nilai preferensi tertinggi, sedangkaniA44 isebagai alternatif peringkat terakhiridengan nilai preferensi paling terendah. Kemudian dari isegi iwaktu eksekusi program, metode MOORA membutuhkan waktu eksekusi selama 0.019 detik.
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