cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Triwiyanto
Contact Email
triwiyanto123@gmail.com
Phone
+628155126883
Journal Mail Official
triwiyanto123@gmail.com
Editorial Address
Pucang Jajar Timur No. 10, Surabaya, Indonesia
Location
Kota surabaya,
Jawa timur
INDONESIA
Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics
Published by Polilteknik Kesehatan Kemenkes Surabaya
ISSN : -     EISSN : 26568624     DOI : https://doi.org/10.35882/ijeeemi
Core Subject : Health, Science, Engineering,
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Health Professions Materials Science & Nanotechnology
Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics (IJEEEMI) publishes peer-reviewed, original research and review articles in an open-access format. Accepted articles span the full extent of the Electronics, Biomedical, and Medical Informatics. IJEEEMI seeks to be the world’s premier open-access outlet for academic research. As such, unlike traditional journals, IJEEEMI does not limit content due to page budgets or thematic significance. Rather, IJEEEMI evaluates the scientific and research methods of each article for validity and accepts articles solely on the basis of the research. Likewise, by not restricting papers to a narrow discipline, IJEEEMI facilitates the discovery of the connections between papers, whether within or between disciplines. The scope of the IJEEEMI, covers: Electronics: Intelligent Systems, Neural Networks, Machine Learning, Fuzzy Systems, Digital Signal Processing, Image Processing, Electromedical: Biomedical Signal Processing and Control, Artificial intelligence in biomedical imaging, Machine learning and Pattern Recognition in a biomedical signal, Medical Diagnostic Instrumentation, Laboratorium Instrumentation, Medical Calibrator Design. Medical Informatics: Intelligent Biomedical Informatics, Computer-aided medical decision support systems using heuristic, Educational computer-based programs pertaining to medical informatics
Arjuna Subject : Teknik (semua kategori) - Teknik Listrik dan Elektro
Articles 231 Documents
 20   21   22   23   24 
Data Splitting Strategies for Down Syndrome Facial Classification: A Comparative Study Using EfficientNet-B0 and MobileNetV2 Dzaki Dhiya Ul-Haq; Yunidar Yunidar; Melinda Melinda; Nurlida Basir; Rosmawinda Rosmawinda
Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol. 8 No. 3 (2026): August
Publisher : Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/ijeeemi.v8i3.338

Abstract

Early identification of Down Syndrome (DS) is essential for timely intervention; however, conventional diagnostic approaches often require specialized clinical expertise and significant resources. Recent advances in deep learning-based facial image analysis offer a promising alternative, yet the impact of data partitioning strategies on model performance and stability remains insufficiently explored. This study investigates the effects of different data-splitting strategies on DS facial image classification using EfficientNet-B0. A total of 3,030 facial images were collected from Roboflow and curated through preprocessing techniques, including Gaussian noise reduction, image sharpening, and contrast enhancement. Two data partitioning configurations, 70:20:10 and 80:10:10, were evaluated using five-fold cross-validation. Model performance was assessed using accuracy, precision, recall, and F1-score, while statistical significance was examined using the Friedman test. The results show that the 70:20:10 configuration achieved an average accuracy of 87.88% ± 3.03%, while the 80:10:10 configuration achieved a slightly higher accuracy of 89.09% ± 2.53%. The Friedman test indicates statistically significant differences (p < 0.05). However, the improvement is relatively marginal, with a small-to-moderate effect size (Cohen’s d = 0.43) and no significant difference in variance (p > 0.05), indicating limited practical significance. A trade-off between accuracy and evaluation stability was observed. While the 80:10:10 configuration benefits from a larger training set, the 70:20:10 configuration provides more stable and balanced performance, particularly in minimizing false negatives. These findings highlight that higher accuracy does not necessarily imply more reliable or clinically meaningful performance, emphasizing the importance of appropriate data partitioning in medical image classification.
Optimization of Intelligent Grid Strength and Grid Ratio to Improve Pelvic Radiographic Image Quality in Digital Radiography Fani Susanto; Hernastiti Sedya Utami; Kusnanto Mukti Wibowo; Samudra Prihatin Hendra Basuki; Widya Mufida; Nurul Fadhlina Binti Ismail
Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol. 8 No. 2 (2026): May
Publisher : Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/ijeeemi.v8i2.288

Abstract

Pelvic radiography is an essential diagnostic procedure; however, its image quality is often degraded by scattered radiation, which reduces contrast and diagnostic accuracy. Conventional physical grids can reduce scatter but have limitations, including increased patient dose and workflow constraints, particularly in mobile radiography. The Intelligent Grid, a digital scatter correction technology, provides a promising alternative, yet the optimal combination of grid ratio and strength parameters remains unclear. This study aims to optimize the strength and grid ratio of the Intelligent Grid to improve pelvic radiographic image quality. This study introduces a novel quantitative optimization approach that systematically integrates grid ratio and strength variations within the Intelligent Grid system to enhance pelvic radiographic image quality in digital radiography. A quantitative experimental approach with a post-test only design was conducted using a mobile X-ray system, digital radiography, an Aero DR detector, and pelvic phantoms. Data were acquired by performing anteroposterior pelvic radiography exposures (60 kV & 10 mAs) with the Intelligent Grid. Strength variations (slightly strong, normal, slightly weak) and grid ratios (3:1, 6:1, 8:1, 10:1, 12:1) were applied to generate different parameter combinations. Image quality was evaluated quantitatively (histogram and SNR) and qualitatively through visual grading by three radiologists. Statistical analysis was performed using Repeated ANOVA and Friedman tests. Statistical analysis showed significant differences (p < 0.001) in pelvic radiographic image quality across Intelligent Grid ratio and strength combinations. The 12:1 grid ratio with slightly strong strength yielded the highest mean SNR, balanced grayscale distribution, and top visual grading scores, confirming its optimal performance in enhancing image contrast and anatomical visibility of the iliac contour, acetabulum, and pubic symphysis. In conclusion, optimal adjustment of Intelligent Grid strength and grid ratio parameters can substantially improve pelvic radiographic image quality, providing a practical alternative to conventional physical grids in digital radiography
Improved Glucose Detection in Urine Using a   Silver-Based Plasmonic Sensor with Ni and TiO2 Thin Films in Kretschmann Configuration Yuant Tiandho; Erna Julianti; Zamziri Zamziri; Putri Cahyani; Fitri Afriani
Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol. 8 No. 2 (2026): May
Publisher : Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/ijeeemi.v8i2.292

Abstract

Surface plasmon resonance (SPR) sensors have emerged as powerful tools for label-free and real-time detection of biomolecules, yet their performance often depends on optimizing the sensing structure. This work proposes and analyzes a silver-based SPR sensor in the Kretschmann configuration, enhanced with thin layers of Ni and TiO2, for non-invasive glucose detection in urine. The optical response was calculated using the transfer matrix method, while sensitivity and figure of merit (FOM) were employed as performance indicators. The incorporation of ultrathin Ni and TiO2 layers atop the silver film induces a pronounced shift in the SPR toward higher incidence angles, accompanied by an enhanced angular response to refractive index variations, thereby indicating improved sensitivity. However, further increases in the thickness of these layers lead to an excessive redshift of the resonance dip toward the extreme angular range, reducing resonance definition and ultimately limiting the sensor's detection capability. The results show that the introduction of Ni and TiO2 significantly improves both sensitivity and spectral sharpness, with the optimal BK7/Ag(30 nm)/Ni(14 nm)/TiO2(5 nm) structure achieving a maximum sensitivity of 432 deg/RIU. This configuration detected glucose concentrations as low as 0.625 g/dL, corresponding to a refractive index change of only 0.001 RIU. Finite-difference time-domain analysis further confirmed that the performance enhancement originates from stronger localization of the electric field at the TiO2 interface, as evidenced by the presence of the Ni layer. These findings demonstrate the effectiveness of Ni and TiO2 layers in enhancing plasmonic responses and highlight the strong potential of this sensor design for practical biomedical applications. In particular, the proposed structure offers a promising pathway for developing simple, non-invasive urine-based glucose monitoring systems.
Determinants of User Satisfaction and Net Benefits of Electronic Medical Record Information Systems in Eastern Military Hospitals Aldy Fitrah Bramantio; Merita Arini
Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol. 8 No. 2 (2026): May
Publisher : Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/ijeeemi.v8i2.339

Abstract

The rapid digital transformation in healthcare has accelerated the adoption of Electronic Medical Records (EMR) to improve service quality, data management, and patient safety. However, despite national policies mandating EMR implementation in Indonesia, adoption remains uneven across healthcare facilities, particularly in regions with limited digital infrastructure and technological readiness. Hospitals in eastern Indonesia face challenges such as unstable internet connectivity, limited technological resources, and varying levels of digital literacy, which may reduce the effectiveness and benefits of EMR implementation. This study aims to analyze the influence of system quality, information quality, and service quality on user satisfaction and the net benefits of EMR implementation using the DeLone and McLean Information System Success Model. A quantitative cross-sectional design was employed using survey data collected from 84 EMR users at Tk. III J.A. Dimara Hospital, Manokwari. Total sampling was applied to include all active EMR users who met the inclusion criteria. Data were analyzed using SmartPLS 4.0 software with Partial Least Squares Structural Equation Modeling (PLS-SEM). Statistical significance was determined using a two-tailed test with a critical t-value > 1.96 and p < 0.05 at the 95% confidence level. The results indicate moderate explanatory power, with R² values of 0.534 for system use, 0.638 for user satisfaction, and 0.578 for net benefits. Seven out of nine hypotheses were supported. System quality and information quality significantly influence system use, while information quality and service quality significantly influence user satisfaction. However, system quality does not significantly affect user satisfaction, and service quality does not significantly affect system use. Furthermore, system use and user satisfaction significantly contribute to net benefits, with user satisfaction demonstrating the strongest effect. These findings suggest that improving information quality, service support, and effective system utilization is essential for enhancing EMR performance and achieving meaningful organizational benefits in hospital settings
Comparative Evaluation of TabKANet with Oversampling and Feature Selection Ablation for Software Defect Prediction Muhammad Faza Azhiman Saputra; Setyo Wahyu Saputro; Mohammad Reza Faisal; Radityo Adi Nugroho; Andi Farmadi
Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol. 8 No. 3 (2026): August
Publisher : Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/ijeeemi.v8i3.351

Abstract

Software defect prediction (SDP) focuses limited testing resources on the modules most likely to fail, but real-world software metric data are tabular, noisy, and severely class-imbalanced, which degrades conventional learners. The Kolmogorov-Arnold Network (KAN) and Transformer architectures recently achieved strong results on tabular data, yet their combined form, TabKANet, has not been evaluated for SDP, nor has the contribution of common preprocessing techniques been quantified. This study adapts and comparatively evaluates TabKANet against established baselines and measures the contribution of oversampling and feature selection through a structured ablation. Twelve all-numerical NASA Metrics Data Program datasets were used. The pipeline applied duplicate removal, MinMax normalization, effective class weighting, and stratified five-fold cross-validation, with oversampling (SMOTE) and Recursive Feature Elimination (RFE) inserted inside the training folds. Four TabKANet variants (A: base, B: +SMOTE, C: +RFE, D: +SMOTE+RFE) were compared with Multi-Layer Perceptron (MLP), standalone KAN, and TabNet, and differences were tested with the Wilcoxon signed-rank test at a 0.05 significance level. The base TabKANet (variant A) achieved the highest mean AUC of 0.7603, slightly ahead of MLP (0.7594) and KAN (0.7583) and well above TabNet (0.7092). Its advantage over TabNet was significant (p = 0.002), whereas it was statistically equivalent to MLP and KAN (p = 0.733). TabNet attained the highest recall (0.739) but the lowest precision (0.228), indicating over-prediction of defects, while TabKANet kept precision and recall balanced. In the ablation, SMOTE significantly reduced AUC (p = 0.042), RFE caused no significant change (p = 0.733), and their combination stayed neutral (p = 0.266). TabKANet therefore performed best without additional resampling. TabKANet is thus a competitive architecture for all-numerical, highly imbalanced SDP, matching strong neural baselines and surpassing TabNet, where effective class weighting alone suffices and SMOTE is counter-productive.
Enhancing Low-Resource Healthcare Chatbots via Multi-Stage Data Augmentation and IndoBERT Fine-Tuning Ahmad Wahyu Rosyadi; Taufiqur Rohman; Moh. Rizki Fajar; Muhammad Qomaruz Zaman; Siti Ma&#039;shumah
Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol. 8 No. 3 (2026): August
Publisher : Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/ijeeemi.v8i3.327

Abstract

Indonesian healthcare question-answering (QA) systems often operate in low-resource settings and must handle substantial linguistic variability in real-world user queries, including paraphrasing, informal expressions, and implicit intent. These challenges are compounded by limited annotated healthcare data and the diverse ways patients express similar medical needs in everyday language, causing QA models to rely heavily on surface-level wording. This study proposes a new multi-stage data augmentation method to improve the robustness of a healthcare chatbot based on IndoBERT Fine-Tuned. The proposed method integrates domain-specific fine-tuning with an augmentation pipeline that introduces paraphrased question variants with IndoT5, normalizes informal language, and incorporates controlled lexical variation through Part-of-Speech (POS) based verb synonym replacement while preserving medical entities, thereby expanding linguistic coverage without requiring additional manual annotation. The augmentation process preserves medical intent while generating diverse surface forms, enabling the model to learn more flexible representations of user queries. Experimental results demonstrate that the proposed multi-stage augmentation substantially improves the robustness of the IndoBERT-based healthcare question answering system. Full augmentation expands the training data to 1,424 question–answer pairs and achieves 67.37 Exact Match (EM) and 85.59 F1. Compared with training without augmentation, this corresponds to relative improvements of approximately 11.8% in EM and 7.0% in F1, indicating more reliable answer span extraction under paraphrased, informal, and lexically varied queries. These gains reflect improved alignment between conversational user input and structured healthcare information. Overall, this work highlights the importance of integrated data augmentation for enhancing low-resource Indonesian healthcare question-answering systems. By exposing the model to broader linguistic variations during training, the proposed approach supports more stable real-world performance while maintaining medical intent consistency and provides insights into remaining challenges for reliable healthcare QA deployment.
Image Quality of Decomposition Based On Near-Infrared Transmission Using The Gram-Schmidt Process Toto Aminoto
Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol. 8 No. 3 (2026): August
Publisher : Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/ijeeemi.v8i3.335

Abstract

Near-infrared tomography (NIR) is highly developed. The weakness of NIR tomography is that it displays all tissue in a single image. To display a single image of a specific tissue from various tissues with unknown thickness, an inverse matrix decomposition method is used. The image decomposition results are not good. To overcome this, use the Gram-Schmidt process. The aim of this study is to measure the quality of the decomposition results using the Gram-Schmidt process.The indicators used to measure the quality of the decomposition results are the MSE and PSNR values. Using the Gram-Schmidt process results in a better decomposition process because it maximizes the independent linear properties by creating mutually orthogonal column vectors. The Lambert-Beer equation performs a natural logarithmic operation, producing a linear relationship between intensity level, attenuation coefficient, and thickness. By varying three different wavelengths and three different materials, three linear equations are obtained. The solution to these three linear equations can be expressed in matrix form. This equation produces a 3x3 matrix of attenuation coefficients. The rows of the matrix represent the differences in attenuation coefficient values ​​for the three materials at a single wavelength, while the columns represent the differences in attenuation coefficient values ​​due to different wavelengths within the same material. By solving this inverse matrix, the thickness of a specific material can be determined at a single pixel. This thickness value can be used to create an image reconstruction that can decompose the material's composition.The results show that the 780 nm-830 nm- 980 nm wavelengths successfully decomposed margarine and PVC. Meanwhile, the 780 nm-808nm-980 nm wavelengths successfully decomposed silicone rubber. The Gram-Schmidt process is able to improve the quality of a decomposition image
Design and Evaluation of a Pre-Impact Fall Detection System with Wearable Airbag Vest for Elderly Injury Mitigation Willy Anugrah Cahyadi; Husneni Mukhtar; Suto Setiyadi; Dita Puspitasari; Nigel Bryan Tang; Mohamad Ramdhani
Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol. 8 No. 3 (2026): August
Publisher : Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/ijeeemi.v8i3.344

Abstract

The increase in the elderly population in Indonesia raises concerns about fall accidents. Many mitigation strategies are available to reduce fall accidents among the elderly. Two main strategies have been widely explored, i.e., fall detection and impact reduction. Despite its disadvantages, fall detection devices remain useful and important for future research. In addition, it is also urgent to address the impact reduction method by providing cushioning to minimize the physical impact on the subject of the fall. This study proposes a wearable safety vest equipped with a pre-impact fall-detection system and an automatically inflating airbag to protect the wearer during a fall. The main contributions of this study are the design and implementation of a lightweight wearable airbag vest for elderly protection, the development of a machine-learning-based pre-impact fall detection, and a quantitative evaluation of impact reduction using accelerometer-based measurements. The proposed safety vest with airbag has been trained, validated, and tested, with the subject achieving fall detection accuracy beyond 90% and the airbag deployment delay of less than 1 second after detection. The experiment results also show that the use of the airbag could reduce the impact shock down to 40-50% based on the accelerometer measurements.
Real-Time Drowsiness Detection Using Dual MobileNetV2 Models on Desktop and Edge Devices Rafi&#039;e
Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol. 8 No. 3 (2026): August
Publisher : Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/ijeeemi.v8i3.371

Abstract

Drowsiness is a leading cause of human error in transportation and in shift-based occupational work, yet delivering reliable real-time detection on affordable, resource-constrained hardware remains difficult. This study aims to develop and evaluate a vision-based drowsiness detection system that behaves consistently across a full-power desktop and a low-cost edge device. The system couples two independent MobileNetV2 transfer-learning classifiers — one for eye state (Open/Closed) and one for mouth state (Yawn/No_yawn) — with a temporal decision engine that converts frame-level predictions into microsleep and excessive-yawning alerts. Both classifiers were trained on a merged multi-source dataset (8,548 training images) and evaluated with a class-balanced protocol (186 images/class for the eye branch and 448 images/class for the mouth branch) to remove test-set imbalance bias. The decision engine was realised as two platform-appropriate pipelines that share an offline-first, retry-capable event architecture: a duration-based, two-tier hysteresis alert on a desktop application (Haar-cascade detection, H5/float32 models) and a frame-count alert designed for a Raspberry Pi 5 edge board (MediaPipe detection, quantised TensorFlow Lite models). On the class-balanced test set the eye branch reached 95.16% (H5) / 95.97% (TFLite) accuracy and the mouth branch reached 96.65% for both formats, with above-99% cross-format prediction agreement. Converting to TFLite cut model size by 73.4% (8.99 to 2.39 MB) and single-frame model inference latency roughly thirteen-fold (about 25 to 1.9 ms, measured on the development machine). Real-time desktop sessions sustained 6.9–12.0 FPS, and the Haar detector located a face in only 15.8% of off-angle frames versus 96.9–98.6% of frontal frames. This single-session result offers a preliminary, rather than definitive, indication of the detector's pose sensitivity. A lightweight dual-MobileNetV2 design with platform-appropriate detectors shows promise for delivering consistent real-time drowsiness alerts across heterogeneous hardware tiers.
Comparative Analysis of Image Augmentation and Class Weighting on ResNet50-CBAM for Pneumonia Detection from Chest X-Ray Images Kafilah Akhmad Fatahillah; Triando Hamonangan Saragih; Dwi Kartini; Fatma Indriani; Irwan Budiman
Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol. 8 No. 3 (2026): August
Publisher : Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/ijeeemi.v8i3.375

Abstract

Pneumonia remains one of the leading causes of morbidity and mortality worldwide, particularly among children, older adults, and immunocompromised individuals. Although chest X-ray (CXR) imaging is widely used for pneumonia diagnosis, manual interpretation is time-consuming, subjective, and highly dependent on radiologist expertise. Deep learning has shown promising performance for automated pneumonia classification; however, class imbalance remains a major challenge that can lead to biased predictions and reduced model generalization. Therefore, this study investigates the effectiveness of image augmentation and class weighting for handling class imbalance in pneumonia classification using chest X-ray images. The main contribution of this study is a systematic comparison of four experimental scenarios: Baseline, Augmentation Only, Class Weighting Only, and Hybrid (Image Augmentation and Class Weighting) implemented on a ResNet50 architecture integrated with the Convolutional Block Attention Module (CBAM). Experiments were conducted using the publicly available Chest X-Ray Images (Pneumonia) dataset from Kaggle, comprising 1,583 Normal and 4,273 Pneumonia images. Model performance was evaluated using Accuracy, ROC-AUC, Precision, Recall, F1-Score, confusion matrix analysis, and Youden’s J-Statistic for threshold optimization. The Hybrid model achieved the best overall performance, with an Accuracy of 95.74%, a ROC-AUC of 98.83%, a Macro Precision of 96.01%, a Macro Recall of 93.13%, and a Macro F1-Score of 94.44%. Moreover, the number of false negative predictions decreased from 25 in the Baseline model to 5 in the Hybrid model. These findings demonstrate that integrating image augmentation and class weighting within the ResNet50-CBAM framework effectively mitigates class imbalance and improves the reliability of automated pneumonia classification.

Page 23 of 24 | Total Record : 231

 20   21   22   23   24 

Filter by Year

2019 2026


Reset
Filter By Issues
All Issue Vol. 8 No. 3 (2026): August Vol. 8 No. 2 (2026): May Vol. 8 No. 1 (2026): February Vol. 7 No. 4 (2025): November Vol. 7 No. 3 (2025): August Vol. 7 No. 2 (2025): May Vol. 7 No. 1 (2025): February Vol. 6 No. 4 (2024): November Vol. 6 No. 3 (2024): August Vol. 6 No. 2 (2024): May Vol. 6 No. 1 (2024): February Vol. 5 No. 4 (2023): November Vol. 5 No. 3 (2023): August Vol. 5 No. 2 (2023): May Vol. 5 No. 1 (2023): February Vol. 4 No. 4 (2022): November Vol. 4 No. 3 (2022): August Vol. 4 No. 2 (2022): May Vol. 4 No. 1 (2022): February Vol. 3 No. 4 (2021): November Vol. 3 No. 3 (2021): August Vol. 3 No. 2 (2021): May Vol. 3 No. 1 (2021): February Vol. 2 No. 3 (2020): November Vol. 2 No. 2 (2020): August Vol. 2 No. 1 (2020): February Vol. 1 No. 2 (2019): November Vol. 1 No. 1 (2019): August More Issue