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All Journal International Journal of Electrical and Computer Engineering Jurnal Media Infotama International Journal of Advances in Intelligent Informatics JOIV : International Journal on Informatics Visualization Communications in Science and Technology Conference SENATIK STT Adisutjipto Yogyakarta Jurnal Informatika IJITEE (International Journal of Information Technology and Electrical Engineering) Indonesian Journal of Electrical Engineering and Computer Science Journal of Computer System and Informatics (JoSYC) Jurnal Nasional Teknik Elektro dan Teknologi Informasi Jurnal INFOTEL JTKSI (Jurnal Teknologi Komputer dan Sistem Informasi)
Hanung Adi Nugroho
Department Of Electrical And Information Engineering, Universitas Gadjah Mada
Aerospace Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Mechanical Engineering

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Classification of Brain Tumors on MRI Images Using DenseNet and Support Vector Machine Eko Minarno, Agus; Setiyo Kantomo, Ilham; Setiawan Sumadi, Fauzi Dwi; Adi Nugroho, Hanung; Ibrahim, Zaidah
JOIV : International Journal on Informatics Visualization Vol 6, No 2 (2022)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.6.2.991

Abstract

The brain is a vital organ in the human body, performing various functions. The brain has always played a major role in the processing of sensory information, the production of muscular activity, and the performance of high-level cognitive functions. Among the most prevalent diseases of the brain is the development of aberrant tissue in brain cells, which results in the formation of brain tumors. According to data from the International Agency for Research on Cancer (IARC), more than 124,000 people worldwide were diagnosed with brain tumors in 2014, and more than 97,000 people died due to the condition. Current research indicates that magnetic resonance imaging (MRI) is the most effective means of detecting brain cancers. Because brain tumors are associated with significant mortality risk, a large number of brain tumor MRI imaging datasets were used in this research to detect brain cancers using deep learning techniques. To classify three forms of brain tumors, including glioma, meningioma, and pituitary, a deep learning model called DenseNet 201 paired with Support Vector Machines (SVM) was employed in this work included three types of brain tumors. Based on the results of the tests that were conducted, the best accuracy results obtained in this study were 99.65 percent, with a comparison ratio of 80 percent for training data and 20 percent for testing data, oversampled with the SMOTE method, with the best accuracy results obtained in this study being 99.65 percent.
Classification of Diabetic Retinopathy Disease Using Convolutional Neural Network Minarno, Agus Eko; Cokro Mandiri, Mochammad Hazmi; Azhar, Yufis; Bimantoro, Fitri; Nugroho, Hanung Adi; Ibrahim, Zaidah
JOIV : International Journal on Informatics Visualization Vol 6, No 1 (2022)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.6.1.857

Abstract

Diabetic Retinopathy (DR) is a disease that causes visual impairment and blindness in patients with it. Diabetic Retinopathy disease appears characterized by a condition of swelling and leakage in the blood vessels located at the back of the retina of the eye. Early detection through the retinal fundus image of the eye could take time and requires an experienced ophthalmologist. This study proposed a deep learning method, the Efficientnet-b7 model to identify diabetic retinopathy disease automatically. This study applies three preprocessing techniques that could be implemented in the dataset "APTOS 2019 Blindness Detection". In preprocessing technique trial scenarios, Usuyama preprocessing technique obtained the best results with accuracy of 89% of train data and 84% in test data compared to Harikrishnan preprocessing technique which has 82% accuracy in test data, and Ben Graham preprocessing has 81% accuracy in test data. In this study, Hyperparameter tuning was conducted to find the best parameters for use on the EfficientNet-B7 Model. In this study, we tested the Efficientnet-B7 model with an augmentation process that can reduce the occurrence of overfitting compared to models without augmentation. Preprocessing techniques and augmentation techniques can influence the proposed EfficientNet-B7 model in terms of performance results and reduce the overfitting of models.
Co-Authors - Nurfadilah, - A.A. Ketut Agung Cahyawan W Achmad Rizal Ade Sofa Adhistya Erna Permanasari Agus Eko Minarno Ahmad Nasikun Al-Fahsi, Resha Dwika Hefni Albert Ch. Soewongsono, Albert Ch. Alfarisi, Ikhsan Anondho Wijanarko Aqil Aqthobirrobbany Aqthobirrobbany, Aqil Aras, Rezty Amalia Arham, Aulia Arif Masthori Atmaja Perdana, Chandra Ramadhan Azof Ghazali Sujono Bhisma Murti Cahyani Windarto Chitra Octavina Cindy Claudia Febiola, Cindy Claudia Citra Prasetyawati Cokro Mandiri, Mochammad Hazmi Danny Kurnianto Dewanta, Wika Dewi Kartika Sari Dian Nova Kusuma Hardani Dianursanti Dimas, Dimas Dindin Hidayat Dwi Haryono E. Elsa Herdiana Murhandarwati Elisabeth Deta Lustiyati Erwin Setyo Nugroho Eva Yuliana Fitri Faisal Najamuddin Fathania Firwan Firdaus Faza Maula Azif Fitri Bimantoro Ganesha L Putra Guyub Nuryanto Handani, Deni Hasdani, Hasdani Hasnely, Hasnely Hastuti, Uki Retno Budi Heri Hermansyah Heru Supriyono Hesti Khuzaimah Nurul Yusufiyah Hotama, Christianus Frederick Hutami, Augustine Herini Tita I Md. Dendi Maysanjaya Ibnu Taufan, Ibnu Ibrahim, Zaidah Ichsan Setiawan Igi Ardiyanto Ignatia Dhian Estu Karisma Ratri Imelda Imelda Indah Soesanti Indriana Hidayah Ismail Setiawan Jafaruddin Jafaruddin, Jafaruddin Kartika Firdausy Kirana, Thea Koko Ondara Krisna Nuresa Qodri KZ Widhia Oktoeberza Lina Choridah Listyalina, Latifah M. Khairun Iffat Made Satria Wibawa Maemonah, Maemonah Mahdi Abdullah Syihab Marshell Tendean Momoji Kubo Muhammad Bayu Sasongko Muhammad Rausan Fikri Naomi Shibasaki-Kitakawa Nasikun, Ahmad Ndii, Meksianis Z Nenden Siti Aminah Noor Abdul Haris Noor Akhmad Setiawan Nora Anisa Br. Sinulingga Novianti Puspitasari Nugroho, Anan Nur Fadhilah Nurcahyani Wulandari Nurfauzi, Rizki Oktoeberza, Widhia KZ Oyas Wahyunggoro Perdana, Adli Waliul Persada, Anugerah Galang Pranowo, Vicko Prasojo, Sasmito Praswasti P. D.K Wulan Puspitasari, Novianti Putri Bungsu Rachman, Anung Ratna Lestari Budiani Buana Rima Fitria Adiati Rina Sri Widayati Riri Ferdiana Risanuri Hidayat Rita Arbianti Rizky Naufal Perdana Robert Silas Kabanga Rochim, Febry Putra Roekmijati W. Soemantojo Saftirta Gatra Dewantara Sandy Anwar Mursito Sarjana Sarjana Sasongko Yoni Bagas Septian Rico Hernawan Setiyo Kantomo, Ilham Sudaryanto . Sukiyo Sukiyo Sumadi, Fauzi Dwi Setiawan Sunu Wibirama Suzanna Ndraha Syahrul Purnawan Syahwami, Syahwami Tania Surya Utami TATI NURHAYATI Teguh Bharata Adji Toshiy Yonemoto Tri Lestari Ulung Jantama Widhia K.Z Oktoeberza Widhia K.Z Oktoeberza Widya Sari Wika Dewanta Willy Anugrah Cahyadi Windarta, Budi Woraratpanya, Kuntpong Yenny Rahmawati Yuda Munarko Yufis Azhar Yulaikha Istiqomah Yulyanti, Vesi Zaidah Ibrahim Zubri, Aldino