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All Journal International Journal of Electrical and Computer Engineering JUITA : Jurnal Informatika ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika JOURNAL OF ELECTRICAL AND SYSTEM CONTROL ENGINEERING JURNAL LENTERA : Kajian Keagamaan, Keilmuan dan Teknologi MIND (Multimedia Artificial Intelligent Networking Database) Journal TESLA: Jurnal Teknik Elektro Jurnal Tekinkom (Teknik Informasi dan Komputer) Jurnal Teknik Informatika (JUTIF) Almufi Jurnal Pengabdian kepada Masyarakat eProceedings of Engineering
Nur Ibrahim
Telkom University
Religion Humanities Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Social Sciences Other

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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.
Co-Authors Abdul Hafiz Suherman Adhi Irianto Mastur, Adhi Irianto Afifah Amatulla Suaib Andrean David Chrismadandi Anindita Fitriani Annisa Adlina Mulyaningrum Annisa Bianca Hayuningtyas Ari Ashari Jaelani Asyraf Fakhri AZIZAH AULIA RAHMAN BACHERAMSYAH, TASYA FIKRIYAH Bambang Hidayat Bambang Hidayat Begita Wahyuningtyas Carudin, Carudin Citra Marshela Danish Ario Wirawan Denis Ramadana Efri Suhartono Eka Wulandari Fajar Dwi Septria Fanny Oksa Salindri Faturachman Faturachman Fiky Yosef Supratman Firdaus, Muhammad Ilham Zuhruf Frisnanda Aditya Galuh Lintang Permatasari Gelar Budiman HAFIZHANA, YASQI HANAFI, FANIESA SAUFANA Heri Syahrian Heri Syahrian, Heri Hilman Fauzi, Hilman Hurianti Vidya Ibnu Da’wan Salim Ubaidah Ilva Herdayanti Inung Wijayanto Iqbal Afriadi Irma Safitri Iwan Iwut Tritoasmoro Iwan Iwut Tritosmoro Jangkung Raharjo Kevin Aglianry KHAERUDIN SALEH Koredianto Usman Krisma Asmoro Ledya Novamizanti LESTARY, GITA AYU Mas, Muhammad Sabri Muh, Ipnu Udjie Hasiru Muh. Gazali Saleh Muhammad Khais Prayoga Muhammad Rizqi Rahmawan Muthia Syafika Haq, Muthia Syafika Nabila Herman Nasywan Azrial Fariqin NOR CAECAR KUMALASARI Nor Kumalasari Caecar Pratiwi Nur Fu'adah, R. Yunendah PRAMUDITHO, MUHAMMAD ADNAN PRATIWI, NOR KUMALASARI CAESAR R. Yunendah Nur Fu’adah Rahma Nur Auliasari Ramadhan Prasetya Dahlan Ramdhan Nugraha Reyhan Ivandhani Reza Yudistira Rezki Diar Amelia Rita Magdalena Rita Purnamasari Rustam Sa’idah, Sofia Satrio Ardhimasetyo SISLY DESTRI AGUSTIN Sjafril Darana SOFIA SAIDAH SY, NIDAAN KHOFIYA Syamsu Rizal Syamsul Rizal Syamsul Rizal Syifa Maliah Rachmawati TALININGSING, FAUZI FRAHMA UBAIDULLAH, IBNU DAWAN Vidiya Rossa Atfira Vidya, Hurianti Vitria Puspitasari Rahadi Vitria Puspitasari Rahadi WIDIANTO, MOCHAMMAD HALDI Yasman, Fudhla Ramadhana YOGASWARA, HERLAMBANG Yusup, Dadang