Penerapan Model CRISP-DM Untuk Klasifikasi Tumor Otak Menggunakan Algoritma CNN
DOI:
https://doi.org/10.29408/jit.v8i2.31014Keywords:
CNN Algorithm, Classification, Brain Tumor, CRISP-DMAbstract
In general, a tumor is an abnormal tissue growth in the body that occurs due to uncontrolled cell division. Tumors can be benign (non-cancerous) or malignant (cancerous). Benign tumors tend to grow locally without spreading to other tissues, while malignant tumors can invade surrounding tissues and spread to other parts of the body through the bloodstream. A brain tumor is a mass or growth of abnormal tissue in or around the brain. Specifically, brain tumors can be primary (originating from the brain cells themselves or surrounding tissues, such as the meninges, pituitary gland, or cranial nerves) or secondary (metastasis from cancer originating from other parts of the body). The main problem today is that brain tumors have many types, therefore research on the classification of brain tumor types is very valuable today. In order to improve accuracy and also speed up the diagnosis process. The results of this study, brain tumor image data classification has been carried out using the CNN Algorithm method with the CRISP-DM data mining technique. The dataset consists of 3,554 datasets, but from the results of testing this algorithm has good performance with an accuracy of 98% with Evaluation and validation using the Confusion Matrix test parameters. and successfully classified human brains affected by tumors and those not affected by tumors, in addition to being able to classify 3 different types of tumors, namely glioma, meningioma, and pituitary.
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