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Odi Nurdiawan
STMIK IKMI Cirebon
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Journal : JITK (Jurnal Ilmu Pengetahuan dan Komputer)

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DEEP LEARNING FOR POLYCYSTIC OVARIAN SYNDROME CLASSIFICATION USING CONVOLUTIONAL NEURAL NETWORK Odi Nurdiawan; Heliyanti Susana; Ahmad Faqih
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol 9 No 2 (2024): JITK Issue February 2024
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v9i2.4575

Abstract

Polycystic Ovarian Syndrome (PCOS) is the main cause of infertility in women. This condition results in abnormal hormone levels. Women who experience this syndrome will have irregular hormone levels and experience irregular menstrual cycles as well, thereby affecting the reproductive system. Symptoms that arise as a result of the increase in these hormones can be seen from the growth of hair on the legs, weight gain which results in not being ideal, irregular menstruation, unusual acne growth, and oily skin. The problem of Polycystic Ovarian Syndrome can cause disturbances in ovulation and cause infertility in women. Urgency This research requires a classification that has good accuracy in diagnosing early to minimize the rate of pregnancy failure. The aim of the research is to be able to model early detection of Polycystic Ovarian Syndrome with high accuracy so that it can help the health team in detecting Polycystic Ovarian Syndrome or not having Polycystic Ovarian Syndrome. The research stage has 3 stages including the first stage of identifying problems and collecting datasets from Telkom University dataverse in the form of images and literature reviews of various sources. The second stage is Pre Processing of image data, Data Training, modeling design by managing image data and classifying using the Convolutional Neural Network Algorithm deep learning model and testing. The third stage is evaluating the test results and discussing the results of accuracy in determining the status of Normal Polycystic Ovarian Syndrome or PCOS. The results of training and validation on the ovarian xray image dataset using the CNN architecture that has been made, 40 iterations (epochs), and 4 step_per_epochs show an accuracy value of 0.8947 or 89.47% and a loss value of 0.2684.
Co-Authors Abdul Robi Padri Ade Bani Riyan Ade Irma Purnamasari Ade Irma Purnamasari Ade Irma Purnamasari Ade Irma Purnamasari Ade Rizki Rinaldi Adisty Tri Putra Agis Maulana Robani Agus Surip Ahmad Faqih Ahmad Faqih Ahmad Faqih Ahmad Zam Zami Ananda Rafly Andi Setiawan Anwar Musaddad Aria Pratama Arif Rinaldi Dikananda Bambang Irawan Basysyar, Fadhil Muhammad Cep Lukman Rohmat Cep Lukman Rohmat Dias Bayu Saputra Dikananda, Arif Rinaldi Dilla Eka Lusiana Dita Rizki Amalia Dwi Teguh Afandi Edi Tohidi Edi Wahyudin Eko Wiyandi Fadhil M. Basysyar Fadrin Helmi Febriansyah, Feggy Fidya Arie Pratama Fidya Arie Pratama Gifthera Dwilestari Haidah Putri Haidar Fakhri Heliyanti Susana Hira Wahyuni Azizah Husein Subandi Ibnu Ubaedila Irfan Ali Irfan Ali Irfan Ali, Irfan Irma Purnamasari, Ade Irvandi Jaelani Sidik Julia Eka Yanti Kaslani Khamim Surya Hadi Kusuma Al Atros Kurniawan Fajar Abdulloh Lukmanul Hakim Martanto . Medina Aprilia Putri Melia Melia Melisa Hikari Mia Fijriani Mohammad Rosihin Amar Muchamad Sobri Sungkar, Muchamad Sobri Mulyana Mulyana Mulyawan Mulyawan Nana Suarna Nana Suarna Nana Suarna Nanda Permatasari Nining Rahaningsih Noval Salim Nur Atikah Nurhadiansyah Nurhadiansyah Nurhadiansyah, Nurhadiansyah Pratama, Fidya Arie Pratiwi, Fitriyani Purnamasari, Ade Irma Putriyana Putriyana R, Nining Rifki Nurcholis Rini Astuti Riyan Suryatana Rohmat, Cep Lukman Rudi Hartono Ruli Herdiana Ruli Herdiana Rully Pramudita Saeful Anwar Saeful Anwar Saeful Anwar, Saeful Saepul Hadi Salsa Billa Agistina Siti Aisyah Tio Prasetiya Tio Prasetya TOMAS TOMAS Topan Hadi Tuti Hartati Tuti Hartati Wiyandi, Eko Yudhistira Arie Wijaya