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Accuracy Analysis of K-Nearest Neighbor and Naïve Bayes Algorithm in the Diagnosis of Breast Cancer Irma Handayani; Ikrimach Ikrimach
JURNAL INFOTEL Vol 12 No 4 (2020): November 2020

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/infotel.v12i4.547


In the medical field, there are many records of disease sufferers, one of which is data on breast cancer. An extraction process to fine information in previously unknown data is known as data mining. Data mining uses pattern recognition techniques such as statistics and mathematics to find patterns from old data or cases. One of the main roles of data mining is classification. In the classification dataset, there is one objective attribute or it can be called the label attribute. This attribute will be searched from new data on the basis of other attributes in the past. The number of attributes can affect the performance of an algorithm. This results in if the classification process is inaccurate, the researcher needs to double-check at each previous stage to look for errors. The best algorithm for one data type is not necessarily good for another data type. For this reason, the K-Nearest Neighbor and Naïve Bayes algorithms will be used as a solution to this problem. The research method used was to prepare data from the breast cancer dataset, conduct training and test the data, then perform a comparative analysis. The research target is to produce the best algorithm in classifying breast cancer, so that patients with existing parameters can be predicted which ones are malignant and benign breast cancer. This pattern can be used as a diagnostic measure so that it can be detected earlier and is expected to reduce the mortality rate from breast cancer. By making comparisons, this method produces 95.79% for K-Nearest Neighbor and 93.39% for Naïve Bayes
TEKNIMEDIA: Teknologi Informasi dan Multimedia Vol. 4 No. 2 (2023): Desember 2023
Publisher : Badan Penelitian dan Pengabdian Masyarakat (BP2M) STMIK Syaikh Zainuddin NW Anjani

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46764/teknimedia.v4i2.132


The development of monitoring applications in the field of floriculture is necessary to improve the quality of ornamental plants. Keeping up with the advancements in Internet of Things technology for detecting growing media conditions, an efficient monitoring solution is needed to optimize the growth, care, and productivity of plants. This research aims to create a mobile-based floriculture plant monitoring application that is expected to assist plant cultivators in observing the microenvironmental conditions of plants, measuring soil moisture, air humidity, and light received by the plants to detect potential issues such as pest infestations or diseases. Thus, the research and development of this application are expected to support the floriculture industry in enhancing plant quality, production efficiency, and environmental sustainability, making it an essential part of modern sustainable cultivation. This research will result in a mobile application built using the Flutter framework, designed to be more flexible for real-time monitoring, especially for Aglaonema plants, using the Waterfall development method. This application can detect not only Aglaonema plants but also other floriculture plants like Chrysanthemums and other ornamental plants. The application has been tested using the Blackbox method, and the test results indicate that the application performs well in monitoring the temperature, soil moisture, and air humidity of Aglaonema plants, sending data to the server, and displaying it on the application quickly and efficiently.