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All Journal Journal of Applied Electrical Engineering Andalas Journal of Electrical and Electronic Engineering Technology
Togi Siholmarito Simarmata
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Control & Systems Engineering Education Electrical & Electronics Engineering Energy Engineering

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The Convergence of Artificial Intelligence and Electronic Devices for Rapid Food Quality Measurement: A Systematic Review Mohammad Alfiza Rayesa; Dego Yusa Ali; Neza Fadia Rayesa; Elsa Lolita Anggraini; Togi Siholmarito Simarmata
Andalas Journal of Electrical and Electronic Engineering Technology Vol. 5 No. 2 (2025): November 2025
Publisher : Electrical Engineering Dept, Engineering Faculty, Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/ajeeet.v5i2.44

Abstract

Ensuring the quality and safety of food is a critical global challenge intensified by complex supply chains and increasing consumer demand for transparency. Traditional measurement techniques—ranging from microbial plating to sensory panels- are often destructive, time-consuming, labor-intensive, and expensive. Recently, non-invasive electronic sensing technologies, coupled with Artificial Intelligence, have emerged as powerful alternatives for rapid and objective assessment. This review aims to identify, synthesize, and appraise peer-reviewed research published between 2005 and 2025 that incorporates AI into electronic devices: electronic noses, computer vision, and spectroscopy for food quality measurement. A systematic literature search was conducted across ScienceDirect, SpringerLink, and IEEE Xplore. The review followed the PRISMA guidelines by identifying 63 studies that met strict inclusion criteria for integrating sensing, hardware, and machine learning algorithms. Analyses show that Computer Vision Systems (CVS), Hyperspectral Imaging (HSI), and Electronic Noses (e-noses) technologies. Deep Learning, in particular Convolutional Neural Networks (CNNs), has surpassed traditional machine learning techniques, such as SVM and PCA, in performance. Key applications include ripeness grading of fruits, detection of adulteration in powders, and freshness monitoring of vegetables and meat products. Integrating AI with electronic sensors provides a scalable, accurate, and non-destructive path forward for Industry 4.0 in the food sector. However, challenges to the issues of model interpretability, data standardization, and real-world robustness remain.
Implementasi Sistem Pemantauan Energi Listrik dan Perbaikan Faktor Daya Berbasis Android dengan MIT App Inventor dan Firebase Andre Rabiula; Dasrinal Tessal; Aldy Pratama; Togi Siholmarito Simarmata; Elsa Lolita Anggraini; Illa Aryeni
Journal of Applied Electrical Engineering Vol. 9 No. 2 (2025): JAEE, December 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaee.v9i2.11615

Abstract

As inductive loads like washing machines and water pumps become more common, they utilize more reactive energy and lower the power factor. If the power factor is less than 0,85 PLN, electrical protection devices like MCBs and ELCBs could stop working. To utilize less power, things need to change. Adding a capacitor bank to add reactive power can make the power factor value better. Using the MIT App Inventor and Firebase platforms, this study built an app that lets you keep track of and improve your power factor. The app had an average data latency of 11,20 seconds. After 11 load changes, the average power factor went up from 0,64 (before improvement) to 0,88 (after). So, this method for optimizing power factor helps to get a low power factor up to the right level.
Co-Authors ALDY PRATAMA Andre Rabiula Dasrinal Tessal Dego Yusa Ali Elsa Lolita Anggraini Illa Aryeni Neza Fadia Rayesa Rayesa, Mohammad Alfiza