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All Journal Jurnal Teknologi Informasi dan Ilmu Komputer Gema Wiralodra TEKNOKOM : Jurnal Teknologi dan Rekayasa Sistem Komputer Jurnal Nasional Teknik Elektro dan Teknologi Informasi International Journal of Engineering Continuity
Indri Yanti
Universitas Wiralodra
Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Energy Engineering Materials Science & Nanotechnology Mechanical Engineering Other

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Journal : International Journal of Engineering Continuity

 1 
Non-Invasive Blood Sugar Measuring Tool Using Arduino-Based Linear Regression Method Nilu Widia Ningsih; Indri Yanti; Muh Pauzan
International Journal of Engineering Continuity Vol. 3 No. 1 (2024): ijec
Publisher : Sultan Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58291/ijec.v3i1.226

Abstract

Diabetes Mellitus is a disease characterized by high blood sugar levels caused by decreased production or function of the hormone insulin in the body. Traditional tests are usually invasive, involving skin puncture to obtain a blood sample, which can be unsuitable for some sufferers. Non-invasive methods provide a viable alternative for monitoring blood sugar levels. This research aims to create an Arduino-based non-invasive blood sugar level measuring device, leveraging the optical property of laser absorption in liquid media, detected by a photodiode sensor. The primary objective is to develop a device that accurately measures blood sugar levels without the need for invasive procedures. The photodiode sensor outputs voltage, which is then converted into blood sugar level (mg/dl) using a linear regression equation. The derived linear regression equation is y = 31.401 + 36.002x, with a previously obtained correlation value of 0.971 between voltage and blood sugar levels at a significance level of 0.01. The average error value (errata) of this device is 0.0905. The smallest measurement error was observed in patients C and Q, at 0.01 or approximately 1%, while the largest error was in patient L, at 0.22 or around 22%. The contributions of this research include the development of a non-invasive, accurate, and cost-effective method for blood sugar monitoring, potentially improving patient compliance and comfort.
Monitoring And Controlling System Based on Internet of Things (IoT) for Ornamental Chrysanthemum Plants Roihan Noval; Indri Yanti; Muh Pauzan
International Journal of Engineering Continuity Vol. 3 No. 1 (2024): ijec
Publisher : Sultan Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58291/ijec.v3i1.228

Abstract

Monitoring and controlling system based on Internet of Things has been developed for ornamental chrysanthemum plants. User interface of the system is a website application created using PHP programming language and MySQL as Database Management Systems. The IoT system built are the result of literature review of chrysanthemum plants, such as an ideal soil moisture for chrysanthemum plants is in the range of 40% to 60%, the average plant water requirement is 16.05 ml/day, it is also recommended to apply liquid fertilizer with a high N content of 2g per litre of water every week. This knowledge then applied in the IoT system. Research findings indicated that the device could be controlled through the website and was capable of transmitting real-time soil moisture data. This system not only triggers watering when the soil humidity falls below 41%RH and stops it when reaching 60%RH but also incorporates scheduled fertilizer according to predetermined schedules, meeting the anticipated outcomes. Water and fertilizer pumps produced volumetric flow rate that changed as the level of liquid in the storage tank decreases. The lower the height, the smaller the volumetric flow rate produced, this is due to the influence of hydrostatic pressure.
Electronic Nose Based on Sensor Array for Classification of Beef and Rat Meat Using Backpropagation Artificial Neural Network Method Diana Rusjayanti; Indri Yanti; Muh Pauzan
International Journal of Engineering Continuity Vol. 3 No. 1 (2024): ijec
Publisher : Sultan Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58291/ijec.v3i1.241

Abstract

The differentiation of beef and rat meat is crucial for food safety and consumer protection. This research aims to create a tool to distinguish between beef and rat meat and to analyze the training data patterns for both types of meat. A sensor array consisting of three gas sensors—TGS822, TGS2602, and TGS2610—was used to detect the presence of Metal Oxide Semiconductor (MOS) gases in the meat samples. The classification method employed was a backpropagation artificial neural network (ANN). Results indicate that the classification tool performs well in differentiating beef from rat meat, with distinct patterns observed in the training data for each type of meat. The model achieved a precision of 100%, a recall (sensitivity) of 80%, and an accuracy of 90%. However, the TGS2610 sensor did not show a significant difference between beef and rat meat, suggesting no variance in the gas content detected by this sensor. These findings highlight the potential of using such sensors in practical applications for meat detection and underscore the need for further refinement in sensor selection and system integration to improve classification performance.
Co-Authors Cyntiya Laxmi Haura Diana Rusjayanti Lysaa Qothrunnada Muh Pauzan Nilu Widia Ningsih Rinto Anugraha NQZ Roihan Noval