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All Journal Scientific Journal of Informatics Proceeding SENDI_U
Wellia Shinta Sari
Sistem Informasi, Fakultas Ilmu Komputer, Universitas Dian Nuswantoro Jl. Nakula I No. 5-11, Semarang, 50131, (024) 3517261
Computer Science & IT Education

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A Combination of K-Means and Fuzzy C-Means for Brain Tumor Identification Sari, Christy Atika; Sari, Wellia Shinta; Rahmalan, Hidayah
Scientific Journal of Informatics Vol 8, No 1 (2021): May 2021
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v8i1.29357

Abstract

Keywords are the labels of your manuscript and critical to correct indexing and searching. MRI or Magnetic Resonance Imaging is one of the health technologies used to scan the human body in order to get an image of an orgasm in the body. MRI imagery has a lot of noise that blends with the tumor object, so the tumor is quite difficult to detect automatically. In addition, it will be difficult to distinguish tumors from brain texture. Various methods have been carried out in previous studies. The method often used in the previous method is segmentation, but the process is quite heavy and the results that are less accurate are still the main obstacles. This study combines the K-Means method and Fuzzy C-Means (FCM) to detect tumors on MRI. The purpose of the combination is to get the advantages of each algorithm and minimize weaknesses. The method used is Contrast Adjustment using Fast Local Laplacian, K-Means FCM, Canny edge detection, Median Filter, and Morphological Area Selection. The dataset is taken from www.radiopedia.org. Data taken were 73 MRI of the brain, of which 57 MRIs with brain tumors and 16 MRIs of normal brain Evaluation of research results will be calculated using Confusion Matrix. The accuracy obtained is 91.78%.
IMPERCEPTIBLE KRIPTOGRAFI CITRA BERWARNA MENGGUNAKAN RIVEST SHAMIR ADLEMAN Sari, Christy Atika; Sari, Wellia Shinta; Sugiarto, Bambang
Proceeding SENDI_U 2021: SEMINAR NASIONAL MULTI DISIPLIN ILMU DAN CALL FOR PAPERS
Publisher : Proceeding SENDI_U

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Abstract

Algoritma Rivest Shamir Adleman (RSA) adalah algoritma unggul yang memanfaatkan teknik asimetris dengan 2 buah kunci berbeda yaitu privat dan public. Proses dekripsi RSA dengan memfaktorkan nilai bilangan prima yang begitu besar dan perhitungan operasi matematika yang rumit. Penelitian ini bertujuan untuk melalukan enkripsi dekripsicitra digital berwarna. Pengujian kemiripan antara citra asli dengan citra yang telah terenkripsi menggunakan nilai Mean Square Error (MSE), Peak Signal to Noise Ratio (PSNR) serta pengujian lain seperti Bit Error Rate (BER) dan Entropy. Dari hasil MSE dan PSNR antara citra asli dengan citra hasil enkripsi hasil menunjukkan bahwa untuk nilai MSE lebih dari 0, dan nilai PSNR kurang dari 30dB. Hal ini menunjukkan bahwa kedua gambar antara gambar asli dan gambar setelah terenkripsi memiliki perbedaan yang signifikan.
A Combination of K-Means and Fuzzy C-Means for Brain Tumor Identification Sari, Christy Atika; Sari, Wellia Shinta; Rahmalan, Hidayah
Scientific Journal of Informatics Vol 8, No 1 (2021): May 2021
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v8i1.29357

Abstract

Purpose: Magnetic Resonance Imaging is one of the health technologies used to scan the human body in order to get an image of an orgasm in the body. MRI imagery has a lot of noise that blends with the tumor object, so the tumor is quite difficult to detect automatically. In addition, it will be difficult to distinguish tumors from brain texture. Various methods have been carried out in previous studies. Methods: This study combines the K-Means method and Fuzzy C-Means (FCM) to detect tumors on MRI. The purpose of the combination is to get the advantages of each algorithm and minimize weaknesses. The method used is Contrast Adjustment using Fast Local Laplacian, K-Means FCM, Canny edge detection, Median Filter, and Morphological Area Selection. The dataset is taken from www.radiopedia.org. Data taken were 73 MRI of the brain, of which 57 MRIs with brain tumors and 16 MRIs of normal brain Evaluation of research results will be calculated using Confusion Matrix. Result: The accuracy obtained is 91.78%. Novelty: K-Means method and Fuzzy C-Means (FCM) to detect tumors on MRI.
Co-Authors Bambang Sugiarto Christy Atika Sari Rahmalan, Hidayah