Diagnosis suatu penyakit akan menjadi tepat jika didukung dengan berbagai proses mulai pengecekan awal (amannesa) sampai pengecekan laboratorium. Hasil dari proses laboratorium mempunyai informasi berbagai penyakit, akan tetapi beberapa jenis penyakit memiliki prevalensi rendah. Penyakit bervalensi rendah memiliki pengaruh dalam penanganan pasien lebih lanjut. Dengan rasio yang tidak seimbang data laboratorium akan menyebabkan nilai akurasi menjadi rendah dalam pengklasifikasian dan penanganan penyakit. Majority Weighted Minority Oversampling Technique (MWMOTE) adalah saalah satu cara untuk menyelesaikan imbalanced. Penelitian ini bertujuan menangani permasalahan ketidakseimbangan data laboratorium kesehatan sehingga diperoleh hasil pengklasifikasian penyakit dengan tingkat akurasi lebih tinggi. Hasil pada penelitian ini menunjukkan bahwa MWMOTE dapat meningkatkan akurasi untuk permasalahan ketidakseimbangan data sebesar 3,13%. Diagnosis of a disease will be appropriate if supported by various processes ranging from initial checks (amannesa) to laboratory checks. Results from the laboratory process have information on various diseases, but some types of diseases have a low prevalence. Low-valvature disease has an effect in the treatment of the patient further. With an unbalanced ratio the laboratory data will cause the accuracy value to be low in the classification and handling of the disease. Majority Weighted Minority Oversampling Technique (MWMOTE) is one way to complete imbalanced. This study aims to address the problem of imbalance of health laboratory data to obtain the results of the classification of disease with a higher degree of accuracy. The results of this study indicate that MWMOTE can improve accuracy for data imbalance problems by 3.13%.
The disease epidemic that attacked the respiratory area and was detected in Indonesia starting in early 2020 is the Corona Virus (COVID-19). This virus's spread is relatively easy, namely through droplets from infected patients, so that the spread is very rapid. This research was conducted to cluster the data on Covid-19 cases in Jakarta Province considering that Jakarta is the starting point for the first case of Corona in Indonesia and until now has become one of the most significant contributors to COVID-19 issues in Indonesia, namely as of December 2020 positive cases of Covid-19 reached 154,000. Souls with the healing of 139.0000 souls. The grouping was carried out based on positive and dead patients from each urban village in Jakarta Province. This study uses the k-means Method to cluster in the handling of COVID-19 cases with 2 clusters. Data distribution in cluster 1 consists of 173 data and 18 data in cluster 2. The use of k-means in this study provides information on areas with the highest and lowest number of positive cases and the highest and lowest cure rates that can be used as an evaluation in handling the Covid-virus 19.
Imbalanced data causes misclassification because the majority of the dominant data is in the minority data, which results in a decrease in the value of accuracy. UCI dataset is a public dataset that can be used as a dataset in machine learning. This study aims to evaluate the Decision Tree, K-NN, Naive Bayes, and Support Vector Machine classification methods on data imbalances in MWMOTE. MWMOTE is used in resolving Imbalanced cases through weighting and grouping. This goal is achieved by evaluating the Decision Tree, K-NN, Naive Bayes, and Support Vector Machine classification methods in MWMOTE to produce more representative synthetic data and increase the accuracy value. The results obtained from this study indicate that the Decision Tree has higher evaluations of recall, precision, F-measure, and accuracy compared to K-NN, Naive Bayes, and Support Vector Machine for data that are balanced with MWMOTE.
hospitals, information technology is used in managing medical records. Most of the medical records in record information system that is able to produce disease index, operation index, doctor index and patient index, doctor index and patient index. Besides indexes for medical records, this application can also generate reports 10 major diseases and procedures both inpatient and outpatient.
Hydroponics is a method of cultivating plants by utilizing a small amount of land without using soil media. Hydroponic cultivation is still done conventionally in monitoring and controlling nutrients and pH of the air. Hydroponics is already with Internet of Things (IoT) technology in the cultivation process. The research aims to use IoT technology by developing control devices and monitoring hydroponic plants remotely, to make it easier for cultivators to control and monitor plant color, temperature, nutrients and the pH value of hydroponic plant water. Control and monitoring can be done through a smartphone application. The data from testing the condition of hydroponic plants obtained an average error of 1.8% for air temperature, 4.8% for water pH, 6.6% for plant color and 7% for water nutrients. Hydroponic plants with the TCS3200 sensor get a monitoring opportunity of 53.3%. Testing of tool control related to nutritional improvement has been carried out using the fuzzy Mamdani method with an increase in the probability of 88.75% for adding nutritional value and 0% for decreasing nutritional value. Tool control for improving the pH value of hydroponic plant water has been successfully carried out.
Malaria is a tropical disease that infects human red blood cells caused by infection with the plasmodium parasite. Plasmodium parasites spread to humans through female Anopheles mosquitoes and can reproduce in human blood cells. Malaria is a health problem that is at risk of causing other health problems such as anemia and even death. The current gold standard for malaria diagnosis is laboratory diagnosis by microscopic examination to find the malaria parasite through the blood cells of the patient. However, the diagnosis of malaria through microscopic observation of blood cells has the potential to take a long time, because the plasmodium parasite has a very small size. The malaria detection system using the Convolutional Neural Network (CNN) method is designed to detect malaria in human blood cells. CNN is a machine learning method designed to classify objects in an image. The system was built in three stages of development, namely the development of a CNN model for malaria detection, software development and hardware development. The hardware components used in the system include Raspberry pi, Raspberry Pi camera module, and LCD. The results of the malaria detection test using the CNN model gave an accuracy of 98.76% which was tested on blood cell images from a microscope
Kelompok pembudidaya ikan sadewa muda mandiri saat ini masih mengalami permasalahan dalam pembudidayaan ikan, hal ini dikarenakan control dan monitor mash dilakukan secara manual. Beberapa parameter yang terus dipantau oleh pembudidaya adalah PH, Kadar nutrisi air, dan suhu. Hal ini tentu saja tidak efektif dan memakan waktu. Oleh karena itu perlu system yang dapat melakukan monitoring terhadap parameter PH, kadar nutrisi air, dan suhu air, dan juga dapat melakukan control terhadap kualitas air. Hal ini dikarenakan kualitas air merupakan hal yang penting untuk budidaya ikan dengan teknologi bioflok. Dengan adanya system ini maka monitoring dan control dapat dilakukan dengan mudah lewat aplikasi mobile yang dapat terintegrasi dengan alat di luar, sehingga pembudidaya ikan tidak perlu datang dan melihat kondisi kolam secara berkala. Kegiatan pengabdian dilakukan dengan survei, persiapan pembuatan alat, pembuatan alat, integrasi alat, pengujain system, dan pelaksanaan kegiatan. Sistem ini sudah melakukan berbagai pengujian, seperti pengujian akurasi dan pengujian fungsional. Berdasarkan hasil pengujian akurasi, sensor suhu DS18B20 dan sensor DF Robot PH Meter V 1.1 memilik akurasi yang baik yaitu masing-masing 95,87% dan 98,28%. Sedangkan pada sensor Gravity TDS Meter V 1.0 masih belum cukup baik dimana persentase akurasi yang diperoleh adalah 93,44%.
Online seminars have many benefits in various fields of science. Information about workshops online, registration, and other activities so that it can help users get information. During the pandemic, COVID-19 had a strategic role in providing information related to the development of science and insight. The right application will provide optimal data and information to all users. The online seminar application is designed with a User-centered design to obtain user-friendly information and usability. The results of the study showed that the online seminar application that was designed had good usability and was acceptable to users. usability testing concluded that an acceptable application with a value of 82.40% and block box testing received the designed menu.
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