1Department of Electronics, Universitas Muhammadiyah Malang, Indonesia
2Department of Electrical Engineering, Universitas Muhammadiyah Malang, Indonesia
BibTex Citation Data :
@article{JTSISKOM13619, author = {Merinda Lestandy and Lailis Syafa'ah and Amrul Faruq}, title = {Klasifikasi pendonor darah potensial menggunakan pendekatan algoritme pembelajaran mesin}, journal = {Jurnal Teknologi dan Sistem Komputer}, volume = {8}, number = {3}, year = {2020}, keywords = {potential blood donor; KNN; Naïve Bayes; donors classification}, abstract = {Blood donation is the process of taking blood from someone used for blood transfusions. Blood type, sex, age, blood pressure, and hemoglobin are blood donor criteria that must be met and processed manually to classify blood donor eligibility. The manual process resulted in an irregular blood supply because blood donor candidates did not meet the criteria. This study implements machine learning algorithms includes kNN, naïve Bayes, and neural network methods to determine the eligibility of blood donors. This study used 600 training data divided into two classes, namely potential and non-potential donors. The test results show that the accuracy of the neural network is 84.3 %, higher than kNN and naïve Bayes, respectively of 75 % and 84.17 %. It indicates that the neural network method outperforms comparing with kNN and naïve Bayes.}, issn = {2338-0403}, pages = {217--221} doi = {10.14710/jtsiskom.2020.13619}, url = {https://jtsiskom.undip.ac.id/article/view/13619} }
Refworks Citation Data :
Note: This article has supplementary file(s).
Article Metrics:
Last update:
Prediksi Siswa Putus Sekolah Swasta Menggunakan Algoritma Bayesian Network (Studi Pada : SMA Islam Al Wahid Kepung)
Classification of beneficiaries for the rehabilitation of uninhabitable houses using the K-Nearest Neighbor algorithm
Last update: 2024-11-19 23:28:17
Starting from 2021, the author(s) whose article is published in the JTSiskom journal attain the copyright for their article and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. By submitting the manuscript to JTSiskom, the author(s) agree with this policy. No special document approval is required.
The author(s) guarantee that:
The author(s) retain all rights to the published work, such as (but not limited to) the following rights:
Suppose the article was prepared jointly by more than one author. Each author submitting the manuscript warrants that all co-authors have given their permission to agree to copyright and license notices (agreements) on their behalf and notify co-authors of the terms of this policy. JTSiskom will not be held responsible for anything arising because of the writer's internal dispute. JTSiskom will only communicate with correspondence authors.
Authors should also understand that their articles (and any additional files, including data sets and analysis/computation data) will become publicly available once published. The license of published articles (and additional data) will be governed by a Creative Commons Attribution-ShareAlike 4.0 International License. JTSiskom allows users to copy, distribute, display and perform work under license. Users need to attribute the author(s) and JTSiskom to distribute works in journals and other publication media. Unless otherwise stated, the author(s) is a public entity as soon as the article is published.