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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) TELKOMNIKA (Telecommunication Computing Electronics and Control) Bulletin of Electrical Engineering and Informatics JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Abdimas Talenta : Jurnal Pengabdian Kepada Masyarakat Journal of Applied Engineering and Technological Science (JAETS) Indonesian Journal of Electrical Engineering and Computer Science Indonesian Journal of Education, Social Sciences and Research (IJESSR)
Romi Fadillah Rahmat
Universitas Sumatera Utara
Agriculture, Biological Sciences & Forestry Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management 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 : Bulletin of Electrical Engineering and Informatics

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Human blood group type detection prototype focusing on agglutinin using microcontroller based photodiode Lubis, Arif Ridho; Harefa, Hafid Rahman; Al-Khowarizmi, Al-Khowarizmi; Julham, Julham; Lubis, Muharman; Rahmat, Romi Fadillah
Bulletin of Electrical Engineering and Informatics Vol 13, No 4: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i4.7007

Abstract

Blood is a fluid in the body that mainly serves as a medium for transporting various substances in the body. Detection of human blood group types with this microcontroller utilizes dark and light properties. The dark character appears due to agglomeration, while the light nature arises because of no agglomeration, for this to happen, a liquid reagent is needed. Administration of this liquid uses the aviator's breathing oxygen (ABO) system, which consists of reagent a, reagent b, and reagent c and mixing it with blood on the test paper. The number of blood samples in each reagent is based on blood lancet. Furthermore, the sensors used to detect these properties are photodiode and light emitting diode (LED) each of 3 pieces. The Arduino Uno is used to process sensor input while at the same time producing displayed human blood group type on the display screen. The test is carried out involving 12 blood samples and a medical officer. Medical officer are tasked reading directly the results of mixing between reagents and blood samples, after that are compared with the system. The results show that the deviation of the system reading is 0.167 for the sensor reading distance with the sample as far as 0.5 cm.
Braille letter recognition in deep convolutional neural network with horizontal and vertical projection Rahmat, Romi Fadillah; Purnamawati, Sarah; Mardianto, Willy; Faza, Sharfina; Sulaiman, Riza; Nadi, Farhad; Lubis, Arif Ridho
Bulletin of Electrical Engineering and Informatics Vol 13, No 5: October 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i5.7222

Abstract

Brail is a written mode of communication utilized by individuals with visual impairments to engage in interpersonal exchanges. The braille writing system consists of patterns printed on specialized paper that feature embossed dots. Braille documents enable the visually impaired to acquire knowledge and information exclusively through the application of their sense of contact. Comprehending braille is not a simple undertaking, particularly for the general populace. Because braille is not a required subject in Indonesian education, the majority of the population lacks proficiency in the language. This may therefore result in a minor communication barrier between visually impaired individuals and non-impaired individuals. In order to address this challenge, the present study employs digital image processing via the deep convolutional neural network (DCNN) technique to facilitate comprehension of braille document contents by non-braille speakers. This study employs a deep learning technique that is highly accurate, effective at image processing, and capable of recognizing complex patterns. This study employed the following image processing methods: grayscaling, filtering, contrast enhancement, thresholding, morphological operation, and resizing. Following testing in this investigation, it was determined that the proposed method accurately identifies embossed braille images with a precision of 99.63%.
Co-Authors Ajulio Padly Sembiring Al Khowarizmi Andrew Handoko Dani Gunawan Dedy Arisandi Dennis Dennis Erna Budhiarti Nababan Faza, Sharfina Handra Saito Harefa, Hafid Rahman Husna, Meryatul Janferson Panggabean Joko Kurnianto Jonatan Sianturi Julham Julham Lubis, Arif Ridho M.Pd, Akrim Mardianto, Willy Meyatul Husna Mhd Faris Pratama Michael J Watts Muhammad Basri Muhammad Fariz Ichwan Muhammad Fermi Pasha Muharman Lubis Nadi, Farhad Opim Salim Sitompul Purnamawati, Sarah Rhama Permadi Ahmad Rina Anugrahwaty Riza Sulaiman Sarah Purnamawati Sawaluddin Seniman Sianturi, Jonatan Silmi Tri Murti Lubis Yundari, Yundari