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All Journal Register: Jurnal Ilmiah Teknologi Sistem Informasi JURNAL KESEHATAN LINGKUNGAN: Jurnal dan Aplikasi Teknik Kesehatan Lingkungan IJIE (Indonesian Journal of Informatics Education) The Spirit of Society Journal : International Journal of Society Development and Engagement SINTECH (Science and Information Technology) Journal Jurnal Informatika Universitas Pamulang Journal of Information Technology and Computer Engineering Journal of Informatics and Vocational Education Jurnal PkM (Pengabdian kepada Masyarakat) JUSTIN (Jurnal Sistem dan Teknologi Informasi)
Febri Liantoni
Universitas Sebelas Maret
Arts Humanities Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Public Health Social Sciences Other

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Journal : SINTECH (Science and Information Technology) Journal

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PERBAIKAN KONTRAS CITRA MAMMOGRAM PADA KLASIFIKASI KANKER PAYUDARA BERDASARKAN FITUR GRAY-LEVEL CO-OCCURRENCE MATRIX Febri Liantoni; Agus Santoso
SINTECH (Science and Information Technology) Journal Vol. 3 No. 1 (2020): SINTECH Journal Edition April 2020
Publisher : LPPM STMIK STIKOM Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31598/sintechjournal.v3i1.528

Abstract

In this era to recognize breast tumors can be based on mammogram images. This method will expedite the process of recognition and classification of breast cancer. This research was conducted classification techniques of breast cancer using mammogram images. The proposed model targets classification studies for cases of malignant, and benign cancer. The research consisted of five main stages, preprocessing, histogram equalization, convolution, feature extraction, and classification. For preprocessing cropping the image using region of interest (ROI), for convolution, median filter and histogram equalization are used to improve image quality. Feature extraction using Gray-Level Co-Occurrence Matrix (GLCM) with 5 features, entropy, correlation, contrast, homogeneity, and variance. The final step is the classification using Radial Basis Function Neural Network (RBFNN) and Support Vector Machine (SVM). Based on the hypotheses that have been tested and discussed, the accuracy for RBFNN is 86.27%, while the accuracy for SVM is 84.31%. This shows that the RBFNN method is better than SVM in distinguishing types of breast cancer. These results prove the process of improving image construction using histogram equalization and the median filter is useful in the classification process.
Co-Authors Agus Santoso Andayani Yuwana Sari Bambang Yuwono Basori Basori Calvin Mikhailouzna Gibran Coana Sukmagautama Cucuk Wawan Budiyanto Dini Erlinawati Dwi Maryono Endar Suprih Wihidayat Erica Devi Ayuningrum Fadil Indra Sanjaya Halim Perdana Kesuma Hidayatulah Himawan Jose Feliciano Lim Belo Ximenes Meiyanto Eko Sulistyo Mohammad Iskandar Nur Fahmi Muhammad Bagus Panuntun Muhammad Hamka Ibrahim Muhammad Jaelani Mulia Sulistiyono Nurcahya Pradana Taufik Prakisya Pratiwi Ajeng Safitri Puspanda Hatta Rifa'i Abdul Karim Rifki Indra Perwira Risalina Myrtha Risya Ines Putri Siswoyo Subuh Pramono Sulis Setiyawati Sutrisno Sutrisno Yuliana Rizki Ikhsanty Yusfia Hafid Aristyagama