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All Journal Jurnal ELTIKOM : Jurnal Teknik Elektro, Teknologi Informasi dan Komputer MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Jurnal Teknologi Informasi dan Multimedia
Wafi, Ahmad Zein Al
Unknown Affiliation
Aerospace Engineering Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering

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Pengembangan Aplikasi Pose Detection untuk Asesmen Kemajuan Fisioterapi Pasien Pasca Stroke dari Jarak Jauh Febry Putra Rochim; Nugroho, Anan; Sukamta, Sri; Wafi, Ahmad Zein Al; Fathurrahman, Muhammad; Damayanti, Amelia; Wardah, Hildatul
JTIM : Jurnal Teknologi Informasi dan Multimedia Vol 5 No 4 (2024): February
Publisher : Puslitbang Sekawan Institute Nusa Tenggara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35746/jtim.v5i4.415

Abstract

Assessment has an important role in determining the diagnosis and subsequent treatment plan. In an effort to increase access and effectiveness of rehabilitation, this research aims to develop a mobile application that is able to report the results of post-stroke patient pose assessment remotely. Telemedicine approaches in post-stroke rehabilitation have become increasingly popular, allowing patients to access rehabilitation services remotely. This is especially beneficial for patients who live in remote areas or have limited mobility. Telemedicine also allows for real-time patient monitoring, allowing adjustments to rehabilitation plans as needed. The mobile app is designed to provide easy access to rehabilitation programs that can be tailored to individual patient needs. In addition to making access easier, this application is equipped with a monitoring feature that allows health professionals to follow patient progress in detail. Data collected from patients' daily exercise and activities provides valuable insight into their progress, which can be used in tailoring rehabilitation plans in real-time. The development of this mobile application technology has great potential to improve rehabilitation outcomes for post-stroke patients. Testing by three experts with two experts as healthy patients and stroke patients, as well as one patient who acts as a medical personel to monitor, shows that from the graph, healthy patients tend to be consistent. On the other hand, post-stroke patients tend to be inconsistent. These results indicate that this application is effective for identifying patient movements during the rehabilitation process. Although there are several obstacles, such as delays in predictions on some devices, this application has great potential to improve the quality of life of post-stroke patients. Thus, the development of a pose detection application for remotely assessing the progress of physiotherapy in post-stroke patients has great potential in improving rehabilitation outcomes. The app facilitates patient access to appropriate, personalized and effective care, while providing medical personnel with objective and accurate data for monitoring and adjusting rehabilitation plans. This is a significant step in advancing the care of post-stroke patients.
Deep Learning Approach for Pneumonia Prediction from X-Rays using A Pretrained Densenet Model Wafi, Ahmad Zein Al; Rochim, Febry Putra; Fathimah, Aisya
Jurnal ELTIKOM : Jurnal Teknik Elektro, Teknologi Informasi dan Komputer Vol. 9 No. 1 (2025)
Publisher : P3M Politeknik Negeri Banjarmasin

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31961/eltikom.v9i1.1457

Abstract

Pneumonia remains a major global health concern, particularly affecting young children and older adults, contributing to significant morbidity and mortality. Traditional diagnostic methods using chest CT scans are time-consuming and prone to errors due to the reliance on manual interpretation. This study investigates the application of DenseNet architectures DenseNet121, DenseNet169, and DenseNet201—for automated pneumonia detection from chest X-ray images. The dataset, obtained from the Guangzhou Women and Children’s Medical Center, consists of 5,216 training images and 624 testing images categorized into normal and pneumonia cases. Data augmentation techniques, including rotation, normalization, and shear, were applied to improve training efficiency. The DenseNet models were pre-trained on ImageNet and fine-tuned by adding fully connected layers with 256 neurons and sigmoid activation. The models were trained for 20 epochs using the Adam optimizer and binary cross-entropy loss function. Performance evaluation revealed that DenseNet201 outperformed the other models, achieving a precision of 0.99 and a recall of 0.61 for normal cases (F1-score of 0.75) and a precision of 0.81 with a recall of 0.99 for pneumonia cases (F1-score of 0.89). These findings demonstrate that DenseNet201 provides a reliable and effective solution for automated pneumonia detection, offering improved diagnostic efficiency and accuracy compared to traditional methods.
Investigating Liver Disease Machine Learning Prediction Performancethrough Various Feature Selection Methods Wafi, Ahmad Zein Al; Rochim, Febry Putra; Bezaleel, Veda
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 24 No. 3 (2025)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v24i3.4531

Abstract

Given the increasing prevalence and significant health burden of liver diseases globally, improving the accuracy of predictive models is essential for early diagnosis and effective treatment. The purpose of the study is to systematically analyze how different feature selection methods impact the performance of various machine learning classifiers for liver disease prediction. The research method involved evaluating four distinct feature selection techniques—regular, analysis of variance (ANOVA), univariate, and model-based on a suite of classifiers, including decision forest, decision tree, support vector classifier, multi-layer perceptron, and linear discriminant analysis. The result revealed a significant and variable impact of feature selection on model accuracy. Notably, the ANOVA method paired with the multi-layer perceptron achieved the highest accuracy of 0.801724, while the univariate method was optimal for the decision forest classifier (0.741379). In contrast, model-based selection often degraded performance, particularly for the decision tree classifier, likely due to the introduction of noise and overfitting. The support vector classifier, however, demonstrated robust and consistent accuracy across all selection techniques. These findings underscore that there is no universally superior feature selection method; instead, optimal predictive performance hinges on tailoring the selection technique to the specific machine learning model. This study contributes practical, evidence-based insights into the critical interplay between feature selection and model choice in medical data analysis, offering a guide for improving classification accuracy in liver disease prediction. Future work should explore more sophisticated and hybrid feature selection methods to enhance model performance further.
Co-Authors Bezaleel, Veda Damayanti, Amelia Fathimah, Aisya Muhammad Fathurrahman Nugroho, Anan Rochim, Febry Putra Sri Sukamta Wardah, Hildatul