DETEKSI TUMOR OTAK DENGAN MENGGUNAKAN METODE CONVOLUTIONAL NEURAL NETWORK (CNN)

Andre Wijaya Universitas Singaperbangsa Karawang
Athena Sahadatunnisa Universitas Singaperbangsa Karawang
Safrian Andromeda Universitas Singaperbangsa Karawang

DOI: https://doi.org/10.36546/jte.v14i2.1439

Keywords: Tumor Otak, Convolutional Neural Network, Logistic Regression

Abstract

Tumor otak merupakan salah satu pertumbuhan jaringan abnormal yang ditandai dengan pertumbuhan sel yang berlebihan di bagian otak tertentu. Pendeteksian tumor dapat dilakukan dengan menggunakan Convolutional Neural Network (CNN) dengan menggunakan library logistic regression sebagai pengkasifikasian. Pada pengujian dengan menggunakan data set sebanyak 1.222 image dengan positive tumor dengan dataset sebanyak 827 image dan no tumor dengan dataset sebanyak 395 image. Pada pengujian ini didapatkan hasil akurasi saat training sebesar 100% dan pengujian saat testing sebesar 95%. Pada pengklasifikasian ini dibagi menjadi dua yaitu positive tumor dan no tumor.

References

S. Y. Prasetyo and G. Z. Nabiilah, "Perbandingan Model Machine Learning pada Klasifikasi Tumor Otak Menggunakan Fitur Discrete Cosine Transform," Jurnal Nurul Fikri, vol. 9, no. 1, pp. 29-34, 2023.

T. Hidayat and R. A. Saputra, "Deteksi Tumor Otak menggunakan CNN," Jurnal Fikom, vol. 9, no. 2, pp. 94-98, 2023

T. A. Mutiara and Q. N. Azizah, "Klasifikasi Tumor Otak Menggunakan Ekstraksi Fitur HOG dan Support Vector Machine," Jurnal Infortech, vol. 4, no. 1, pp. 45-50, 2022.

A. S. Febrianti, T. A. Sardjono and A. F. Babgei, "Klasifikasi Tumor Otak Pada Citra Magnetic Resonance Image Dengan Menggunakan Metode Support Vector Machine," Jurnal Teknik ITS, vol. 9, no. 1, pp. 118-123, 2020.

W. Hasmoto, S. and S. , "Convulation Neural Network Arsitektur Mobilnet V2 Untuk Mendeteksi Tumor Otak," senTIK, vol. 5, no. 1, pp. 17-21, 2021.

H. Fonda, Y. Irawan and A. Febriani, "Klasifikasi Batik Riau dengan Menggunakan Convolutional Neural Network (CNN)," Jurnal Ilmu Komputer, vol. 9, no. 1, pp. 7-10, 2020.

D. Y. Utami, E. Nurlelah and F. N. Hasan, "Comparison of Neural Network Algorithms, Naive Bayes and Logistic Regression To Find The Highest Accuracy In Diabetes," JITE (Journal of Informatics and Telecommunication Engineering), vol. 5, no. 1, pp. 53-64,2021.

E. Listiana and M. A. Muslim, "Penerapan Adaboost Untuk Klasifikasi Support Vector Machine Guna Meningkatkan Akurasi Pada Diagnosa Chronic Kidney Disease.," Prosinding SNATIF, pp. 875-881, 2017.

J. Bohaju, "Brain Tumor," Kaggle.com, 2020. [Online]. Available: https://www.kaggle.com/datasets/jakeshbohaju/brain-tumor. [Accessed 11 December 2023].

M. Sandler, A. Howard, M. Zhu and A. Zhmoginov, "MobileNetV2: Inverted Residuals and Linear Bottlenecks," Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 4510-4520, 2018.

Published

2024-11-15

Issue

Vol 14 No 2 (2024): Jurnal Teknik Elektro

Section

Articles

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