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All Journal ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika JOURNAL OF ELECTRICAL AND SYSTEM CONTROL ENGINEERING Jurnal Ilmiah FIFO MIND (Multimedia Artificial Intelligent Networking Database) Journal TELKA - Telekomunikasi, Elektronika, Komputasi dan Kontrol TEKTRIKA - Jurnal Penelitian dan Pengembangan Telekomunikasi, Kendali, Komputer, Elektrik, dan Elektronika JMECS (Journal of Measurements, Electronics, Communications, and Systems) Jurnal Teknik Informatika (JUTIF) Charity : Jurnal Pengabdian Masyarakat Prosiding SNAST Jurnal Abdi Masyarakat Indonesia eProceedings of Engineering Jurnal INFOTEL
R. Yunendah Nur Fu’adah
Telkom University
Arts Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mechanical Engineering Public Health Social Sciences

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Journal : Jurnal Teknik Informatika (JUTIF)

 1 
CONVOLUTIONAL NEURAL NETWORK FOR ANEMIA DETECTION BASED ON CONJUNCTIVA PALPEBRAL IMAGES Rita Magdalena; Sofia Saidah; Ibnu Da’wan Salim Ubaidah; Yunendah Nur Fuadah; Nabila Herman; Nur Ibrahim
Jurnal Teknik Informatika (Jutif) Vol. 3 No. 2 (2022): JUTIF Volume 3, Number 2, April 2022
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20884/1.jutif.2022.3.2.197

Abstract

Anemia is a condition in which the level of hemoglobin in a person's blood is below normal. Hemoglobin concentration is one of the parameters commonly used to determine a person's physical condition. Anemia can attack anyone, especially pregnant women. Currently, many non-invasive anemia detection methods have been developed. One of non-invasive methods in detecting anemia can be seen through its physiological characteristics, namely palpebral conjunctiva images. In this study, conjunctival image-based anemia detection was carried out using one of the deep learning methods, namely Convolutional Neural Netwok (CNN). This CNN method is used with the aim of obtaining more specific characteristics in distinguishing normal and anemic conditions based on the image of the palpebral conjunctiva. The Convolutional Neural Network proposed model in this study consists of five hidden layers, each of which uses a filter size of 3x3, 5x5, 7x7, 9x9, and 11x11 and output channels 16, 32, 64, 128 respectively. Fully connected layer and sigmoid activation function are used to classify normal and anemic conditions. The study was conducted using 2000 images of the palpebral conjunctiva which contained anemia and normal conditions. Furthermore, the dataset is divided into 1,440 images for training, 160 images for validation and 400 images for model testing. The study obtained the best accuracy of 94%, with the average value of precision, recall and f-1 score respectively 0.935; 0.94; 0.935. The results of the study indicate that the system is able to classify normal and anemic conditions with minimal errors. Furthermore, the system that has been designed can be implemented in an Android-based application so that the detection of anemia based on this palpebral conjunctival image can be carried out in real-tim.
GLAUCOMA CLASSIFICATION BASED ON FUNDUS IMAGES PROCESSING WITH CONVOLUTIONAL NEURAL NETWORK Yunendah Nur Fu'adah; Sofia Saidah; Nidaan Khofiya; Rita Magdalena; Ibnu Da'wan Salim
Jurnal Teknik Informatika (Jutif) Vol. 3 No. 3 (2022): JUTIF Volume 3, Number 3, June 2022
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20884/1.jutif.2022.3.3.276

Abstract

Glaucoma is an eye disease that causes damage to the optic nerve due to increased pressure in the eyeball. Delay in diagnosis and treatment of optic nerve damage due to glaucoma can lead to permanent blindness. Thus, several studies have developed a glaucoma early detection system based on digital image processing and machine learning. This study carried out glaucoma classification based on fundus image processing using Convolutional Neural Network (CNN). The CNN architecture proposed in this study consists of three convolutional layers with output channels 8, 16, 32 sequentially and a filter size of 5×5 at each layer, followed by a pooling layer and a dropout layer at the feature extraction stage. Furthermore, a fully connected layer and softmax activation function was implemented at the classification stage to classify fundus images into normal conditions, early glaucoma, moderate glaucoma, deep glaucoma, and ocular hypertension (OHT). The total amount of fundus image data used in this study consisted of 2000 fundus images divided into 1280 training data, 320 validation data, and 400 test data. 5-fold cross-validation is implemented in the training phase to select the best model. At the testing stage, the best accuracy generated by 99%, with the precision value, recall, f-1 scores and the AUC score are close to 1. According to the system performance results obtained, the proposed model can be used as a tool for medical personnel in classifying glaucoma conditions to provide appropriate medical treatment and reduce the risk of permanent blindness due to glaucoma.
Co-Authors Achmad Rizal Adam Agus Kurniawan Adinda Maulida Agung Aditama Putra Ahmad Fauzan Fauzan Ahmad Zendhaf Allisha Septariani Ahmad Alva Rischa Qhisthana Pratika Ardhi Fibrianto Avon Budiono Azis Ansori Wahid Daulay, Muhammad Agil Syaifullah Dian Ayu Nurlitasari Dyah Retno Mutia Edwar Efri Suhartono FAUZI FRAHMA TALININGSIH Febriani Ruming Sari Firdaus, Muhammad Naufal Firos Fathul Alam Gelar Budiman Hurianti Vidya Hurianti Vidyaningtyas Ibnu Da'wan Salim Ibnu Da’wan Salim Ubaidah Ihsan Budi Purwono Ilma Rahma Dewi Imanuel Boyke Nainggolan Inung Wijayanto Irdin Arjulian Irham Bani Alfafa Jangkung Raharjo Koredianto Usman Ledya Novamizanti Lugina Perceka Putri M Teguh Kurniawan Maghfira Rifki Hariadi Miftahul Fawaz Muhamad Reinaldi Kurniawan Muhamad Rokhmat Isnaini MUHAMMAD ADNAN PRAMUDITO Muhammad Akhyar Ghifari Muhammad Ardhi Prakasa Muhammad Dwi Cahyo Muhammad Yuqdha Faza Mulyantini, Agustien N Kumalasari Caecar Pratiwi Nabila Herman Naufal Adi Gifran Nidaan Khofiya Nivadirrokhman, Dhanendra Nor Kumalasari Nor Kumalasari Caecar Pratiwi Nur Alyyu Nur Ibrahim Ocky Tiaramukti Pandu Jati Utomo PRAMUDITHO, MUHAMMAD ADNAN PRATIWI, NOR KUMALASARI CAESAR Putra, Rafly Fasha Purnomo Raditiana Patmasari Rafid Fakhri Rahmad Hidayatullah Salam Rahmiati Aulia Ramadhan, Ardiansyah Ratna Sari Ratri Dwi Atmaja Razief Moch Diar Rd. Rohmat Saedudin Rezki Ariz Rahadian Rifky Abdul Khafid Rifqi Muhammad Fikri Rita Magdalena Rita Purnamasari Rizki Muhammad Iqbal Rizky Gilang Gumilar Saiful Azis Santosa, Atharizky Ade Sari, Febriani Ruming Siti Hajar Komariah SOFIA SAIDAH Sony Sumaryo Steven Palondongan Sugondo Hadiyoso SY, NIDAAN KHOFIYA Syafiq Hilmi Abdullah Syamsul Rizal Syamsul Rizal TALININGSING, FAUZI FRAHMA Teguh Dian Arifandi Teguh Musaharpa Gunawan UBAIDULLAH, IBNU DAWAN Vidya, Hurianti Wawan Tripiawan Yoga Yuniadi Zuhri, Hamdan Syaifuddin