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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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Applications For Detecting The Rate Of Fruit In Mangrove Plants Sharfina Faza; Meyatul Husna; Ajulio Padly Sembiring; Rina Anugrahwaty; Silmi; Romi Fadillah Rahmat; Rhama Permadi Ahmad
JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Vol. 6 No. 2 (2023): Issues January 2023
Publisher : Universitas Medan Area

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31289/jite.v6i2.8379

Abstract

Mangrove plants are one of the plants that really help aquatic ecosystems between the sea, coast, and land. Mangrove plants provide many ecological, social, and economic benefits. In Indonesia, mangrove plants have 202 species with the same anatomy as other plants in general, consisting of roots, fruits, stems and leaves. Nowadays, the location of mangrove plants in Indonesia has experienced the fastest damage in the world due to conversion to ponds, settlements, industry and plantations. One of the efforts to restore aesthetic value and restore the ecological function of mangrove forest areas is rehabilitation using mangrove fruit. In the rehabilitation process, farmers generally use the manual method with the naked eye to determine fruit ripeness on mangrove plants, so the resulting level of accuracy is not optimal. To overcome this problem, an application is needed that can facilitate farmers in determining fruit maturity in mangrove plants so that it can help determine the maturity level of mangrove fruit. The development of this application utilizes the Deep Learning method as well as the utilization of digital image processing techniques with Grayscaling, Adaptive Threshold, Sharpening and Smoothing techniques. The results of this study are an application that can detect the level of fruit maturity in mangrove plants with an accuracy of 99.11%. With this application, determining the maturity level of fruit on mangrove plants can be easily done.
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%.
Modification of Multilayer Perceptron Using Detection Rate Model for Prediction of Nominal Exchange Rate Al-Khowarizmi Al-Khowarizmi; Romi Fadillah Rahmat; Michael J Watts; Akrim Akrim; Arif Ridho Lubis; Muhammad Basri
Journal of Applied Engineering and Technological Science (JAETS) Vol. 6 No. 2 (2025): Journal of Applied Engineering and Technological Science (JAETS)
Publisher : Yayasan Riset dan Pengembangan Intelektual (YRPI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37385/jaets.v6i2.6117

Abstract

An artificial neural network (ANN) is a network of a group of units to be processed which is modeled based on the behavior of human neural networks. ANN has one of its tasks, namely prediction. Multilayer perceptron (MLP) is one of the ANN methods that can be prediction all of data. Where the prediction needs to be reviewed because the prediction process does not always run normally. So, it takes a good measurement accuracy in order to get an accuracy sensitivity. The accuracy technique in this paper is carried out using Mean Absolute Percentage Error (MAPE) based on absolute error and detection rate. The results obtained with absolute error achieve an accuracy of 99.73% while the accuracy based on the detection rate achieves an accuracy of 99.49%. this can be seen in the case of the prediction of (Indonesian Rupiah) IDR exchange rate against United State Dollar (USD) with the MLP algorithm by testing using MAPE to achieve sensitivity with absolute error.
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