Article Per Year (5 Year)
p-Index From 2021 - 2026
0.23
P-Index
This Author published in this journals
All Journal ASEAN Journal of Systems Engineering Andalas Journal of Electrical and Electronic Engineering Technology
Rayesa, Mohammad Alfiza
Universitas Gadjah Mada, Indonesia
Control & Systems Engineering Education Electrical & Electronics Engineering Energy Engineering

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
LONG TERM PROJECTION OF ELECTRICITY GENERATION SECTOR IN WEST PAPUA PROVINCE: LEAP MODEL APPLICATION Etika Nur'Aini; Isra Nuur Darmawan; Mohammad Alfiza Rayesa
ASEAN Journal of Systems Engineering Vol 4, No 2 (2020): ASEAN Journal of Systems Engineering
Publisher : Master in Systems Engineering

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Electricity is one of the crucial infrastructures in economic development. The number of registered customers electricity increases every year based on data from the State Electricity Company (PLN) Manokwari branch office data. Electricity increase because it has become an essential part of everyday life. Therefore, in West Papua, it is necessary to fix this electricity problem where the most significant source is still from fossils. By looking at potential sources in West Papua that are more sustainable and renewable to meet public electricity demand in West Papua.In this study, LEAP software will simulate several scenarios, namely based on data from the RUPTL (Electricity Supply Business Plan) and further digging based on the potential literature in West Papua. There will be three scenarios; scenario 1 uses BAU (Business as Usual) as available in RUPTL. Scenario 2 uses BAU data and adds potential renewable energy. Scenario 3 is not using fossil energy but using renewable energy. The result is West Papua can be 100% electrified in 2025 if using scenario 2. The potential for renewable energy in West Papua is wind and sun. However, it does not rule out other sources, such as hydropower. 
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.
Co-Authors Dego Yusa Ali Elsa Lolita Anggraini Etika Nur'Aini Isra' Nuur Darmawan Neza Fadia Rayesa Togi Siholmarito Simarmata