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All Journal Jurnal Pendidikan dan Pembelajaran Khatulistiwa (JPPK) JURNAL INSTEK (Informatika Sains dan Teknologi) Jurnal Teknologi Elekterika Jurnal At-Taghyir : Jurnal Dakwah dan Pengembangan Masyarakat Desa Lontara Journal Of Health Science And Technology
Usman Umar
Program Studi Pendidikan Guru Sekolah Dasar FKIP Untan, Pontianak
Humanities Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Mathematics Medicine & Pharmacology Social Sciences Other

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Journal : Jurnal Teknologi Elekterika

 1 
Application of Artificial Neural Network and Gray Level Co-occurrence Matrix to detect blood glucose levels through the skin of the hands. Umar, Usman; Syarif, Syafruddin; Nurtanio, Ingrid; Indrabayu, Indrabayu
Jurnal Teknologi Elekterika Vol. 19 No. 2 (2022): Nopember
Publisher : Jurusan Teknik Elektro Politeknik Negeri Ujung Pandang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31963/elekterika.v6i2.3756

Abstract

Increased glucose in the blood can cause a buildup so that it cannot be absorbed by all of the body's cells, this problem can cause various disorders in the body's organs. To avoid problems, it is necessary to check the blood glucose level regularly. Monitoring blood sugar levels is currently still using invasive techniques that are painful, non-invasive monitoring is needed. This study develops a non-invasive method to predict blood glucose through image processing. For investigation, several invasive images and glucose levels were taken. Types of samples based on age classification, 20-60 years. For accuracy and simple analysis, 37 images of participants as volunteers, samples were evaluated and investigated under the gray level co-occurrence matrix (GLCM). In this study, an artificial neural network (ANN) was used for all training and hand texture testing to detect glucose levels. The performance of this model is evaluated using Root Mean Square Error (RMSE) and correlation coefficient (r). Clarke Error Grid Analysis (EGA) variance was used in this investigation to determine the accuracy of the method. The results showed that the RMSE was close to the standard value, the regression coefficient was 0.95, and the Clarke EGA analysis: 81.08 % was in the A zone. So that the blood glucose prediction model using the GLCM-ANN method is feasible to apply.
Simulation Model Design of VHF Omni-Directional Range (VOR) Based on Microcontroller Tristiantoro, Roby; Umar, Usman; Alyah, Risnawaty
Jurnal Teknologi Elekterika Vol. 21 No. 1 (2024)
Publisher : Jurusan Teknik Elektro Politeknik Negeri Ujung Pandang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31963/elekterika.v21i1.4754

Abstract

The process of giving directional information to assist airplanes in making an accurate landing at the airport of destination is known as aviation navigation. The VHF Omnidirectional Range is a crucial navigation tool for guiding planes to the airport (VOR). Since it costs a lot of money to learn how to fly an airplane, a VOR simulator was developed that can mimic the movement of an aircraft passing through the VOR. The simulation that was produced is a prototype that sends and receives signals to the aircraft utilizing an antenna and microcontroller as supporting hardware. The VOR/DME flight navigation system is constructed in this study using mathematical modeling; a formulation representing the essential features of the system is expressed as a set of connected variables. The ESP32 module that powers the system serves as a DME by sending out Bluetooth radio signals. where one ESP32 module serves as a DME object (moving DME) and three ESP32 modules serve as DME stations (ground DME). Information on the distance between the DME station and the DME object will be communicated using the MQTT protocol, and this data will be processed using the trilateration method to predict the location and movement of the DME object. The measurement accuracy at DME station 1, DME station 2, and DME station 3 are 99.52%, 99.92%, and 99.98% respectively. The enhanced capabilities to estimate the position of objects observed from different directions or omni-directionally on a two-dimensional scale are made possible by the performance results of combining the performance of three ESP32 devices as Distance Measurement Equipment (DME). 
Co-Authors ALYAH, RISNAWATY Amin, Imran Dandi, LD Muh fatimang, sitti Hakam, Muhammad Haris Hasmah Hasmah, Hasmah Hery Kresnady Indrabayu Indrabayu Ingrid Nurtanio K. Y. Margiati Kholid, Hidayat Nurhaidah, Siti Nuri Syafruddin Syarif Syam, Anita Nur TRISTIANTORO, ROBY Yahya, Muhammad Ridwan