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Jurnal Teknik Elektro
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This is an Open Access Journal

Open Access journal
ISSN (Print) 1411-0059 - ISSN (Online) 2549-1571
Published by Universitas Negeri Semarang

[79 journals]

Tracing Knowledge States through Student Assessment in a Blended Learning
Environment
- Authors: Indriana Hidayah, Ebedia Hilda Am
  Pages: 46 - 52
  Abstract: Blended learning has recently acquired popularity in a variety of educational settings. This approach has the advantage of being able to autonomously monitor students' knowledge states using the collected learning data. Moodle is the most widely used learning management system in blended learning environments. Students can access Moodle to obtain supplementary materials, exercises, and assessments to complement their face-to-face meetings. However, its performance can be improved by more effectively tailoring students' skills and pace of learning. Several studies have been conducted on knowledge tracing; however, we have not discovered any studies that particularly investigate knowledge tracing in a blended learning setting with Moodle as a component. This study proposes a scheme for assessment using the features of the Moodle quiz platform. The assessment data is used to conduct knowledge tracing with the Bayesian Knowledge Tracing (BKT) model, which improves interpretability. The aforementioned data were collected from information engineering undergraduate students who completed 88 exercises that assessed 23 knowledge components within the course. We measure RMSE and MAE to evaluate the performance of the BKT model on our dataset. Furthermore, we compare the knowledge tracing performance to other well-known datasets. Our results show that the BKT model performed better with our dataset, with an RMSE of 0.314 and an MAE of 0.197. Moreover, the BKT model can be used to assess student performance and determine the level of mastery for each knowledge component. Thus, the outcomes can be applied to personalized learning in the future.
  PubDate: 2024-02-13
  DOI: 10.15294/jte.v15i2.47861
  Issue No: Vol. 15, No. 2 (2024)
Vivaldi Tapered Slot Antenna for Microwave Imaging in Medical Applications
- Authors: Randy Ivanal Hakim, Daffa Mahendra, Endarko Endarko
  Pages: 53 - 59
  Abstract: Microwave imaging has become an active research area in recent years, owing primarily to advancements in detecting the early stages of cancer. The study aimed to create a high-gain compact Vivaldi Tapered Slot antenna (VTSA) for microwave imaging in medical applications and also aims to address several challenges in the development of microwave imaging (MWI) technology for medical applications. These challenges include the ability to detect and identify abnormalities in human tissue and considering safe Specific Absorption Rate (SAR) limits for patients, the approach of balancing of penetration and resolution can be done on the design. The antenna operates at frequencies ranging from 1.7 to 3.1 GHz and is built on a low-cost Flame Retardant-4 (FR-4) substrate with a thickness of 1.6 mm. A compact exponential VTSA is initially presented while designing the proposed antenna for broad impedance bandwidth performances. The simulation used a back-to-back linear array of antennas with or without a phantom, specifically a without phantom (only antennas), a water phantom (cube shape), and an anomaly inside the water phantom. The results revealed a significant shift in the signal graph between the three results, indicating a difference in values between the three simulations. A transient domain solver calculation was used in the simulation. The designed antenna improved a gain of 6.09 dBi and a SAR of 0.326 W/kg by maximizing the edges of the exponential in the tapered section and the feedline slot area. The antenna exhibits differences in scattering parameters on each simulation of anomalies across the required frequency range. The result finds suitability of the experiment and simulation in assessing the microwave imaging capabilities. With the data presented, simulated antennas can be used for microwave imaging. The next study should aim on making a suitable imaging system with dimensions that supported in the antenna range and specifications.
  PubDate: 2024-03-13
  DOI: 10.15294/jte.v15i2.43955
  Issue No: Vol. 15, No. 2 (2024)
Sequential Detection under Correlated Observations using Recursive Method
- Authors: Fiky Yosef Suratman, Istiqomah Istiqomah, Dien Rahmawati
  Pages: 60 - 67
  Abstract: Sequential analysis has been used in many cases when the decision is required to be made quickly, such as for signal detection in statistical signal processing, namely sequential detector. For identical error probabilities, a sequential detector needs a smaller average sample number (ASN) than its counterpart of a fixed sample number quadrature detector based on Neyman-Pearson criteria. The optimum sequential detector was derived based on the assumption that the observations are uncorrelated (independent). However, the assumption is commonly violated in realistic scenario, such as in radar. Using a sequential detector under correlated observations is sub-optimal and it poses a problem. It demands a high computational complexity since it needs to recalculate the inverse and the determinant of the signal covariance matrix for each new sample taken. This paper presents a technique for reducing computational complexity, which involves using recursive matrix inverse to calculate conditional probability density functions (pdf). This eliminates the need to recalculate the inverse and determinant, leading to a more reasonable solution in real-world scenarios. We evaluate the performance of the proposed (recursive) sequential detector using Monte-Carlo simulations and we use the conventional and non-recursive sequential detectors for comparisons. The results show that the recursive sequential detector has equal probabilities of false alarm and miss-detection with the conventional sequential detector and performs better than the non-recursive sequential detector. In terms of ASN, it maintains results comparable to those of the two conventional detectors. The recursive approach has reduced the computational complexity for matrix multiplication to O(n2) from O(n3) and has rendered the calculation of matrix determinants unnecessary. Therefore, by having a better probability of error and reduced computational complexities under correlated observations, the proposed recursive sequential detector may become a viable alternative to obtain a more agile detection system as required in future applications, such as radar and cognitive radio.
  PubDate: 2024-03-28
  DOI: 10.15294/jte.v15i2.46941
  Issue No: Vol. 15, No. 2 (2024)
Leveraging Convolutional Neural Networks for Automated Detection and
Grading of Diabetic Retinopathy from Fundus Images
- Authors: Ibnu Uzail Yamani, Basari Basari
  First page: 68
  Abstract: This study addresses the critical challenge of Diabetic Retinopathy (DR) detection and severity grading, aiming to advance the field of medical image analysis. The research problem focuses on the need for an accurate and efficient model to discern DR conditions, thereby facilitating early diagnosis and intervention. Employing a Convolutional Neural Network (CNN), our methodology is developed to strike a balance between precision and computational efficiency, a pivotal aspect in the context of healthcare applications. The research leverages the APTOS 2019 dataset, a comprehensive collection of fundus photographs, to evaluate the efficacy of our proposed model. The dataset allows for a thorough investigation into the model's performance in binary-class and multi-class classifications, providing a robust foundation for analysis. The most important result of our study manifests in the achieved accuracy rates of 98.67% and 87.81% for binary-class and multi-class classifications, respectively. These outcomes underscore the model's reliability and innovation, surpassing established machine learning algorithms and affirming its potential as a valuable tool for early DR detection and severity assessment. In conclusion, the study marks a significant advancement in leveraging deep learning for ophthalmic diagnoses, particularly in the nuanced landscape of Diabetic Retinopathy. The implications of our findings extend to the broader realm of AI-driven healthcare solutions, presenting opportunities for enhanced clinical practices and early intervention strategies. Future research endeavors could explore further refinements to the model, considering additional datasets and collaborating with healthcare professionals for real-world validation, ensuring the continued progress of AI applications in the medical domain.
  PubDate: 2024-03-28
  DOI: 10.15294/jte.v15i2.48769
  Issue No: Vol. 15, No. 2 (2024)
Interference Management Based on Clustering Method for Ultra-Dense
Networks in Multicellular Network
- Authors: Alisha Gita Gumilang, Misfa Susanto, Helmy Fitriawan
  Abstract: One of solutions to address the increasing demands for data traffic by mobile users is to implement ultra dense network (UDN). UDN consists of many femtocells that are densely deployed on macrocellular communication networks. Since femtocells radius is very short and they are tightly packed, it faces interferences problem i.e., co-tier and cross-tier interferences. This paper proposes interference management technique based on clustering method for UDN multicellular communication networks at downlink transmission. It is purposed to reduce interference effects. To evaluate the proposed clustering method, two simulation scenarios have been designed; namely baseline system and system with the proposed clustering method. The scenarios applied is to randomly distribute femtocells following a uniform distribution in three macrocells areas. Through a clustering algorithm, adjacent femtocells are grouped into one cluster and assigned different frequency channels for each femtocell in that cluster. The same cluster pattern is repeated for all femtocells. Thus, interference effect is canceled within one cluster and reduced among clusters by widening the distance among femtocells that use the same frequency channels. Through simulation experiment, the proposed clustering method is evaluated and compared to the baseline system. The simulation results show that Signal to Interference plus Noise Ratio (SINR) of femtocells increases 0.44 dB, throughput increases 1.67%, Bit Error Rate (BER) reduces, and other parameters improve as well which include spectral efficiency, network energy, and average network energy. The proposed clustering method increases performances of the networks and provides better solution for transmission data speed in densely femtocell network.
  PubDate: 2023-12-31
  DOI: 10.15294/jte.v15i2.48738
  Issue No: Vol. 15, No. 2 (2023)

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