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All Journal JURNAL SISTEM INFORMASI BISNIS TELKOMNIKA (Telecommunication Computing Electronics and Control) Bulletin of Electrical Engineering and Informatics Abdimas PROSIDING CSGTEIS 2013 Jurnal Simantec Prosiding Semnastek Jurnal Al-Tadzkiyyah ELINVO (Electronics, Informatics, and Vocational Education) Jurnal Ilmiah Pangabdhi Network Engineering Research Operation [NERO] Muaddib : Studi Kependidikan dan Keislaman Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control DoubleClick : Journal of Computer and Information Technology JPP IPTEK (Jurnal Pengabdian dan Penerapan IPTEK) METHODIKA: Jurnal Teknik Informatika dan Sistem Informasi Jurnal Nasional Komputasi dan Teknologi Informasi Jurnal Pro Hukum : Jurnal Penelitian Bidang Hukum Universitas Gresik Istawa: Jurnal Pendidikan Islam MULTINETICS Jurnal Ekonomi Manajemen Sistem Informasi Proceedings of Annual Conference for Muslim Scholars JATI (Jurnal Mahasiswa Teknik Informatika) NERO (Networking Engineering Research Operation) Jurnal Pengabdian dan Pemberdayaan Banyuwangi IPSSJ J-CEKI
Wahyudi Setiawan
Sistem Informasi, Universitas Trunojoyo
Religion Aerospace Engineering Arts Humanities Automotive Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Neuroscience Public Health Social Sciences Transportation Other

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Journal : JURNAL SISTEM INFORMASI BISNIS

 1 
Sistem Deteksi Retinopati Diabetik Menggunakan Support Vector Machine Setiawan, Wahyudi; Adi, Kusworo; Sugiharto, Aris
JSINBIS (Jurnal Sistem Informasi Bisnis) Vol 2, No 3 (2012): Volume 2 Nomor 3 Tahun 2012
Publisher : Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (928.963 KB) | DOI: 10.21456/vol2iss3pp109-116

Abstract

Diabetic Retinopathy is a complication of Diabetes Melitus. It can be a blindness if untreated settled as early as possible. System created in this thesis is the detection of diabetic retinopathy level of the image obtained from fundus photographs. There are three main steps to resolve the problems, preprocessing, feature extraction and classification. Preprocessing methods that used in this system are Grayscale Green Channel, Gaussian Filter, Contrast Limited Adaptive Histogram Equalization and Masking. Two Dimensional Linear Discriminant Analysis (2DLDA) is used for feature extraction. Support Vector Machine (SVM) is used for classification. The test result performed by taking a dataset of MESSIDOR with number of images that vary for the training phase, otherwise is used for the testing phase. Test result show the optimal accuracy are 84% .   Keywords : Diabetic Retinopathy, Support Vector Machine, Two Dimensional Linear Discriminant Analysis, MESSIDOR
Klasifikasi Citra Histopatologi Kanker Payudara menggunakan Data Resampling Random dan Residual Network Setiawan, Wahyudi
JSINBIS (Jurnal Sistem Informasi Bisnis) Vol 11, No 1 (2021): Volume 11 Nomor 1 Tahun 2021
Publisher : Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21456/vol11iss1pp70-79

Abstract

Data imbalance between classes is one of the problems on image classification. The data in each class is not equal and has a relatively large difference generated in less than optimal classification results. Ideally, the data in each class is equal or have a slight difference. This article discusses the classification of the histopathology breast cancer image. The data consist of  8 classes with unbalanced data. The method for balancing the data in each class uses random resampling which is applied to training data only. The data used from BreakHist with magnifications of 40x, 100x, 200x, and 400x . The classification uses Residual Network (ResNet) 18 and 50. The best results are obtained on images with a magnification of 400x. Classification results using ResNet18 has an average accuracy of 79.82%, an average precision of 71.39%, and an average recall of 82.37%. Meanwhile using ResNet50 showed an average accuracy of 81.67%, average precision of 78.41%, and an average recall of 82.91%.
Co-Authors Abdul Ghofur Abunawas, Abunawas Aeri Rachmad Afiful Ikhwan Ainun Subair Ainun Subair Al-Afghoni, J.M. Havidz Yasaf Alinta, Cici Ami Yani Nur Amirul Mukminin Andika Wahyudi Aris Sugiharto Budi Setiyono Dewi, Meidah Dianti, Dhea Rahma Doni Abdul Fatah Fahmi, Muhammad Farid Fitri Damayanti Fitri Damayanti Fitri Damayanti Haryadi, Alya Nabila Putri Holilur Rohman, Muhammad Juwita Juwita, Juwita Kawindra, Revalina KHOIRUL ANAM Kholij Zukhrufin, Fina Koeshardianto, Meidya Kori Cahyono Krisnawan, Yudha Jarod Kurniawan, Bambang Dwi Kusumaputri, Aditya Kusworo Adi M. Alauddin Helmi Mala Sari Rochman, Eka Moch Faiz Ansori Moh. Imam Utoyo Mohammad Syarief Mohammad Syarief Mohammad Syarief Mohammad Syarief Mohammad Syarief, Mohammad Muh. Tajab Muh. Tajab, Muh. Muhammad Bakriansyah Muhammad Bakriansyah Muhammad Farid Fahmi Muhammad Fuad Muhammad Mushlih Suhadi Mukhlis Nur Pancahari Mukminin, Amirul Novi Prastiti Novi Prastiti, Novi Nur Kholik Afandi Nurul Iman Nurul Iman, Nurul Oktalina, Yuyun Prastiti, Novi Rachman, Fika Hastarita Reny Pujiastutik Riries Rulaningtyas Riries Rulaningtyas Rochman, Eka Mala Sari Rohmah, Nisaur Safitri, Eka Ayu Sofie Ratnasari Syam, Aldo Redho Syarief, Mohammad Wahyudi Agustiono Yudha Dwi Putra Negara, Yudha