Indonesian Journal of Electrical Engineering and Computer Science
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Development of unified college admission system for Philippine state universities and colleges: a data-driven approach to equity and access
Get PDFThis paper presents the development and pilot evaluation of the unified college admission system (UCAS), a centralized and equity-oriented digital platform designed to streamline admissions across Philippine state universities and colleges (SUCs). Anchored on Republic Act No. 10931, UCAS functions as a unified application repository that standardizes admissions data, consolidates applicant records, and enables real-time monitoring of equity target students (ETS) to support fair and transparent access to higher education. The system integrates student-facing and administrative portals that facilitate application submission, institutional coordination, and equity-focused analytics. A pilot evaluation involving student applicants and administrators assessed usability, efficiency, and reliability, yielding consistently positive results across user groups. Findings indicate that UCAS is technically robust, user-centered, and suitable for multi-level admissions governance. Overall, the study demonstrates the potential of a centralized, data-driven admissions platform to complement tuition-free education policies by addressing inequities at the admissions stage
A novel approach for detecting diabetic retinopathy using two-stream CNNs model
Get PDFMajor causes of visual impairment, particularly diabetic retinopathy (DR) and aged-related macular degeneration (AMD), has posed significant challenges for clinical diagnosis and treatment. Early detection and prompt intervention can help prevent severe consequences for patients. The study presents a novel approach for detecting eye diseases using a two-stream convolutional neural network (CNN) model. The first stream processes preprocessed fundus images, while the second stream analyzes high-pass filtered fundus images in the spatial frequency domain. To assess the model’s performance, we use the APTOS 2019 dataset, which was originally compiled for the Asia Pacific Tele-Ophthalmology Society 2019 Blindness Detection competition and is publicly available on Kaggle. Our method shows promise as an early screening tool for DR detection with an accuracy of 0.986
Enhancing wind energy prediction accuracy with a hybrid Weibull distribution and ANN model: a case study across ten locations in Java Island, Indonesia
Get PDFAccurate wind speed forecasting is essential for optimizing renewable energy (RE) systems, especially in coastal and island regions with high variability. This study proposes a hybrid predictive model that combines Weibull distribution parameters with artificial neural networks (ANN) to enhance forecasting accuracy. Using ten years of hourly NASA POWER data from 10 locations across Java Island, 24 scenarios were tested with varying combinations of Weibull and meteorological variables. Results demonstrate that incorporating both Weibull shape (k) and scale (c) parameters significantly improves performance, with the best configuration (Scenario 1) achieving a MAPE of 0.44% in Garut. Excluding one or both parameters sharply reduced accuracy, with errors rising up to 35.12%. Beyond technical accuracy, the findings emphasize the practical relevance of Weibull-informed ANN models for energy planning. Reliable forecasts support better wind resource assessment, grid integration, and investment decisions, reducing uncertainties that often hinder wind power deployment. By providing accurate and stable predictions across diverse locations, this approach offers policymakers and planners a robust tool to accelerate RE development and meet national energy targets
Remaining useful life estimation of turbofan engine: a sliding time window approach using deep learning
Get PDFSystem degradation is a common and unavoidable process that frequently oc curs in aerospace sector. Thus, prognostics is employed to avoid unforeseen breakdowns in intricate industrial systems. In prognostics, the system health status, and its remaining useful life (RUL) are evaluated using numerous sen sors. Numerous researchers have utilized deep-learning techniques to estimate RUL based on sensor data. Most of the studies proposed solving this problem with a single deep neural network (DNN) model. This paper developed a novel turbofan engine RUL predictor based on several DNN models. The method includes a time window technique for sample preparation, enhancing DNN’s ability to extract features and learn the pattern of turbofan engine degradation. Furthermore, the effectiveness of the proposed approach was confirmed using well-known model evaluation metrics. The experimental results demonstrated that among four different DNNs, the long short-term memory (LSTM)-based predictor achieved the better scores on an independent testing dataset with a root mean-square error of 15.30, mean absolute error score of 2.03, and R-squared score of 0.4354, which outperformed the previously reported results of turbofan RULestimation methods
Enhancing cybersecurity in 5G networks systems through optical wireless communications
Get PDFIn this paper we will discuss with the recent global deployment of 5G networks, it has become imperative to ensure secure and reliable communications in addi tion to basic responsibility. Given that standard radio frequency (RF) communi cations have security flaws such as eavesdropping, signal jamming, and cyber attacks, wireless optical communications (WOC) offers a viable alternative. Us ing technologies such as visible light communications (VLC) and the free space optics (FSO) technologies, 5G networks can enhance the speed and efficiency of data transmission, while simultaneously enhancing cyber security. In addition to discussing the advantages of wireless on-chip communication technology com pared to RF solutions and the challenges that need to be addressed, this paper examines how WOC technology can enhance cyber security in 5G networks
Predictive control strategy for a novel 15-level inverter with reduced power components
Get PDFThis paper proposes a novel fifteen-level H-PTC inverter topology controlled by model predictive control (MPC), which reduces the number of components. The design employs only two DC sources, nine switches, including one bidirectional switch, and a single capacitor. The system’s performance is validated through MATLAB/Simulink simulations under various scenarios, such as steady-state operation, load variations, nonlinear loads, and sudden supply voltage disturbances. Compared to existing topologies, the proposed inverter demonstrates hardware simplicity, high output quality, and enhanced dynamic robustness. Notably, it features very low total standing voltage (TSV) and a minimized cost function value of 2.05. For a load characterized by R = 20 Ω and L = 20 mH, the total harmonic distortion (THD) of the load current is 0.88%, confirming excellent power quality without the need for output filters. The MPC controller ensures a fast dynamic response and strong adaptability, making this topology ideal for modern energy conversion applications
Evaluation of the performance of mobile telephone networks: literature review
Get PDFImproving the quality of service (QoS) of telephone networks inevitably involves studying previous work on the evaluation of its performance indicators. Several researchers have addressed the subject of evaluating the performance of service of mobile telephone networks. Some proceeded through user surveys and others opted for more objective methods using either professional scanners or developed: hyper text markup language (HTML) or Android applications. The results show that whether by subjective or objective methods, this work has made it possible to advance research and allow other researchers to progress further in the process of evaluating mobile networks. In this study which constitutes a review of the literature, we investigated the different approaches, methods, and most recent results mentioned by researchers to evaluate the QoS by relying much more on objective evaluation. Despite the advances and their limits, in our proposal we intend to rely on data sciences through their tools to evaluate the QoS with more precision
An energy-efficient hardware module for edge detection using XNOR-Popcount in resource-constrained devices
Get PDFEdge detection is a fundamental building block in many embedded vision tasks, including drone navigation, IoT cameras, and wearable devices. However, traditional edge detectors based on multiply–accumulate (MAC) operations are poorly suited to the tight power and area budgets of such resource-constrained hardware. This work introduces a fully synthesizable Prewitt edge detector that replaces MAC operations with 1-bit XNOR– Popcount logic. Incoming 8-bit pixels and ±1 kernel coefficients are binarized, processed by parallel XNOR gates, and tallied by a lightweight Popcount adder tree, eliminating all multipliers and DSP slices. Prototyped on a Xilinx Zynq-7020 FPGA, the proposed design reduces lookup-table usage by 55% and flip-flop count by 26%, cuts dynamic power by about 60%, and supports clock frequencies up to five times higher than a MACbased core. Frame-level evaluations on the MNIST and ORL datasets show near-lossless edge fidelity, with per-image dissimilarity scores below 0.08 and throughput gains approaching four times. These results demonstrate that hardware-aware binary approximations can enable real-time, energyefficient edge detection for embedded AI systems without sacrificing functional accuracy
Cyber hygiene awareness among Malaysian youth
Get PDFThe study examined cyber hygiene awareness among Malaysian youth by analyzing the roles played by individual knowledge, awareness, attitudes, gender differences, and educational level. An online survey was conducted with 414 respondents in Peninsular Malaysia. The results showed no significant differences in cyber hygiene awareness based on gender and educational level. This suggests equal access to cybersecurity information and training across genders and education levels in Malaysia. This study also found significant relationships between individual characteristics (knowledge, rationality, and attitude) and cyber hygiene awareness. These findings indicate that individuals who are more knowledgeable, have positive attitudes, and make rational decisions tend to have higher cyber hygiene awareness. The results highlight the importance of fostering rationality and consistency in approaches to cybersecurity practices. The study contributes to the thoughtfully reflective decision-making (TRDM) theory, providing insights for developing targeted cybersecurity training programs and policies. Future research could explore additional factors influencing cyber hygiene awareness and examine how these findings translate to actual cybersecurity behaviors in professional settings
Design and construction of an Arduino-based baby incubator simulator using IoT
Get PDFThis study aims to create a baby incubator simulator equipped with an internet of things (IoT)-based temperature control system using Arduino UNO. We use a DHT22 sensor to measure temperature and humidity, as well as fuzzy logic to ensure more accurate and responsive temperature control. The Thinger.io platform enables real-time monitoring and control of the incubator, providing flexibility and ease of supervision. With fuzzy logic, the temperature control system can handle changes and uncertainties in the incubator environment, providing a smoother response compared to traditional on-off methods. Testing shows that this system has a very low error rate, with an error value of only 0.97%, meaning that the measured temperature is almost identical to the actual conditions inside the incubator. Additionally, the authors used mice as a model for premature infants in the testing. The results showed that the mice's body temperature increased gradually and stably in line with the incubator conditions, reaching the desired temperature within 90 minutes. This demonstrates that our temperature control system is capable of maintaining optimal environmental conditions for premature infants