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All Journal JUITA : Jurnal Informatika JUSIFO : Jurnal Sistem Informasi JOIN (Jurnal Online Informatika) Journal of Economic, Bussines and Accounting (COSTING) INTECOMS: Journal of Information Technology and Computer Science INTEK: Informatika dan Teknologi Informasi International Journal of Engineering, Technology and Natural Sciences (IJETS) Indonesian Journal of Data and Science G-Couns: Jurnal Bimbingan dan Konseling Jurnal Teknik Informatika (JUTIF) Jurnal Informatika Teknologi dan Sains (Jinteks) Malcom: Indonesian Journal of Machine Learning and Computer Science Journal of Scientific Research, Education, and Technology Jurnal INFOTEL International Journal of Artificial Intelligence in Medical Issues Jurnal Teknokes
Umar Zaky
University Technology Of Yogyakarta
Biochemistry, Genetics & Molecular Biology Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Mathematics Medicine & Pharmacology Physics Public Health Social Sciences Other

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Poincaré Plot Method for Physiological Analysis of the Gadget Use Effect on Children Stress Level Zaky, Umar; Anggara, Afwan; Zakariyah, Muhammad; Fathullah, Ilham
JOIN (Jurnal Online Informatika) Vol 7 No 1 (2022)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v7i1.809

Abstract

Stress in children can affect the way they think, act, and feel. The habit of using gadgets has several advantages and disadvantages, but there has been no in-depth study of the effect of using gadgets on stress levels in children. This study aims to determine the representation of the physiological condition of using gadgets on stress levels in children. A total of 18 electrocardiogram data were extracted with poincaré plot features. This research has found that there is no difference in the level of stress in children between before and after using gadgets in terms of autonomic nervous activity (Sig. > 0.05). However, there is an increase in sympathetic activity that occurs in children even though they have finished using gadgets. Such conditions certainly need to get more attention, especially related to the duration of gadget use and accessible content.
Systematic Optimization of Ensemble Learning for Heart Failure Survival Prediction using SHAP and Optuna Setia, Bayu; Zaky, Umar
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 5 (2025): JUTIF Volume 6, Number 5, Oktober 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.5.5324

Abstract

Heart failure (HF) stands as a major global health problem where precise and early prediction of patient prognosis is essential for improving clinical management and patient care. A common obstacle for standard machine learning models in this domain is the prevalent issue of class imbalance within clinical datasets. To overcome this challenge, this study introduces a systematically optimized ensemble learning model for the accurate classification of patient survival. The methodology was applied to a publicly accessible clinical dataset of 299 heart failure patients. Its comprehensive framework included logarithmic transformation, stratified data splitting (80:20), SHAP-based selection of eight key features, and hyperparameter tuning with Optuna over 75 trials, with the specific objective of maximizing the F1-score using 10-fold cross-validation. The performance of three ensemble models (Random Forest, XGBoost, and LightGBM) was refined using decision threshold tuning. The results revealed that the fully optimized Random Forest model yielded superior outcomes, attaining an accuracy of 96.67%, an F1-score of 0.9474, and precision and recall values of 0.95, demonstrating high reliability with only a single instance of a False Negative and False Positive. The study concludes that the systematic application of SHAP, SMOTE, and Optuna within an ensemble framework substantially improves classification performance for imbalanced HF data, surpassing existing benchmarks. This work thus provides a replicable and systematic framework for developing reliable machine learning models from complex, imbalanced medical datasets, contributing a valuable methodology to the field of computational science.
Strategi Resampling dan Pengaruhnya terhadap Fitur Variabilitas Denyut Jantung pada Data Elektrokardiogram Berfrekuensi Sampling Rendah Zakariyah, Muhammad; Zaky, Umar; Nurjaman, Muhammad; Istikmal, Agil Ghani; Widianto, Hafizh Athallah
JURNAL INFOTEL Vol 16 No 1 (2024): February 2024
Publisher : LPPM INSTITUT TEKNOLOGI TELKOM PURWOKERTO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/infotel.v16i1.1049

Abstract

Heart rate variability (HRV) is a parameter to measure fluctuations in the interval between heartbeats. HRV provides essential insights into the cardiovascular function and autonomic nervous system. Electrocardiograms (ECG) on wearable devices are often recorded at low sampling rates, limiting temporal resolution and information. Resampling is a technique of changing the sampling rate from a high sampling rate to a lower sampling rate and vice versa. This research aims to evaluate the effect of resampling ECG data with a low sampling rate on HRV features. ECG data consists of 50 Hz and 100 Hz sampling rates. Data with a 50 Hz sampling rate is up-sampled up to 100 Hz, while 100 Hz data is down-sampled up to 50 Hz and up-sampled up to 250 Hz using the Fast Fourier Transform Interpolation Method. Upsampling from 50 Hz to 100 Hz shows unsatisfactory results, except for some HRV features such as NN20, pNN20, and CVI. Better results were found when up sampling from 100 Hz up to 250 Hz, with some HRV features showing good concordance values. However, downsampling from 100 Hz up to 50 Hz is unsuitable for HRV feature analysis. To obtain accurate HRV analysis results in all domains, it is highly recommended to use a sampling rate above 100 Hz.
Optimizing Data Augmentation Parameters in YOLOv11 for Enhanced Rip Current Detection on Small Datasets from Depok-Parangtritis Coastline Putri, Madina Hayva; Zaky, Umar; Prabawa, Bayu Argadyanto
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 5 (2025): JUTIF Volume 6, Number 5, Oktober 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.5.5352

Abstract

Rip currents are powerful ocean currents that can suddenly pull swimmer offshore and are often difficult to recognize visually. However, automatic monitoring technology for detecting rip currents is still limited, while small datasets often lead to overfitting problems and reduce detection accuracy. This study aims to optimize data augmentation parameters in YOLOv11 to improve the mean Average Precision (mAP) value and enable rip current detection even with limited data. The dataset was collected from Google Earth and aerial photographs from the Depok-Parangtritis coastline. Preprocessing includes manual labelling, cropping, and resizing to 640 x 640 pixels. Four augmentation techniques were applied, namely crop (0-10%), rotation (-10% to +10%), brightness adjustment (-10% to +10%), and 1 pixel blur using Roboflow. The dataset was split into 70% training and 30% validation. The YOLOv11 model was then trained and evaluated with precision, recall, and mAP metrics. Results show that data augmentation significantly improves model performance. Dataset 2 without augmentation achieved only 31.8% precision, 32.8% recall, and 23.8% mAP50, while the best model from a combination of the original Dataset 1 and the augmented Dataset 3 reached 90.6% precision, 85.7% recall, and 90.4% mAP50. The integration of YOLOv11 into a web application enables automatic detection in both images and videos with bounding box and confidence score. This study emphasizes the importance of visual variation in the dataset for improving the model generalization and provide a practical foundation of real-time coastal monitoring system.
Vision Transformer Enhanced by Contrastive Learning: A Self-Supervised Strategy for Pulmonary Tuberculosis Diagnosis  Marlina, Widia; Zaky, Umar
Jurnal Teknokes Vol. 18 No. 4 (2025): Desember
Publisher : Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jteknokes.v18i4.126

Abstract

Tuberculosis (TB) diagnosis from Chest X-ray (CXR) images poses a significant challenge in radiology due to the inherent data imbalance and subtle lesion heterogeneity. These factors cause traditional deep learning models, like standard CNNs and conventional Vision Transformers (ViT), to exhibit poor generalization and inadequate sensitivity (recall) for the minority TB class. We address this critical research gap by introducing a novel methodology, an enhanced ViT architecture that leverages Self-Supervised Learning (SSL) via the SimCLR framework, subsequently optimized with an Adaptive Weighted Focal Loss. Our primary objective was to develop a generalizable model that minimizes false negatives without sacrificing overall precision, thereby establishing a new performance benchmark for automated TB detection. The methodology conceptually separates feature learning from SSL pre-training on unlabeled data to generate robust and domain-invariant features, distinct from classification optimization. Adaptive Weighted Focal Loss is employed during fine-tuning to counter majority class gradient dominance mechanistically. We validated this approach using K-Fold Cross-Validation. The final ViT SSL Weighted model achieved a peak internal accuracy of 0.9861 and an AUPRC of 0.9781. Crucially, it maintained generalization stability when externally tested on the TBX11K dataset, securing an AUPRC of 0.9795 and a high recall of 0.9527. This minimal variance strongly confirms the reproducibility and robustness of our features against institutional variation. The resulting high recall directly translates to enhanced diagnostic decision-making, significantly lowering the clinical risk associated with a missed TB diagnosis. This study establishes an effective, stable, and generalizable SSL-based ViT framework, offering a scalable solution for public health efforts in resource-constrained settings.
Automated Water Level Control System Using IoT Under Diverse Conditions Manullang, Ali Sahat Pardomuan; Zaky, Umar
Journal of Scientific Research, Education, and Technology (JSRET) Vol. 4 No. 4 (2025): Vol. 4 No. 4 2025
Publisher : Kirana Publisher (KNPub)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58526/jsret.v4i4.945

Abstract

Water level management in pools and tanks is critical, but sensor accuracy can be compromised by dynamic surface conditions. This research presents the design and implementation of an Internet of Things (IoT) based system for automatically monitoring and controlling water levels. The system utilizes an HY-SRF05 ultrasonic sensor to measure water height and integrates with an automated valve (tap) control mechanism to maintain the level within predefined thresholds. A comparative analysis was initially performed with a Sharp GP2Y0A21YK0F infrared sensor, which was found to be unsuitable for the required short-range measurements. The primary system was then tested under four distinct water surface conditions: calm, wavy, foamy, and foamy-wavy, at target heights of 4 cm, 8 cm, and 12 cm. The system demonstrated high accuracy and responsiveness in all scenarios. A web and mobile application were also developed for real-time monitoring and remote management. This study confirms the robustness of the ultrasonic sensor for reliable water level automation, even in challenging environments.
Optimizing Agricultural Yield and Supply Chain via Client-Server Architecture Kusuma, Alif Arya; Zaky, Umar
Journal of Scientific Research, Education, and Technology (JSRET) Vol. 4 No. 4 (2025): Vol. 4 No. 4 2025
Publisher : Kirana Publisher (KNPub)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58526/jsret.v4i4.951

Abstract

The agricultural sector in developing regions often faces dual challenges: the lack of systematic harvest data recording and inefficient supply chains dominated by intermediaries. These issues result in suboptimal land productivity management and reduced profit margins for farmers. This study addresses these problems by designing a system based on Client-Server Architecture to digitize harvest records and facilitate direct-to-partner transactions. Utilizing Flutter as the mobile client and Laravel as the server-side API provider backed by PostgreSQL, the system implements a specific Land Productivity Yield (LPY) algorithm to map high-performing cultivation methods. The architecture connects three strategic stakeholders: Farmers, Regional Coordinators, and Industrial Partners. Usability testing using the System Usability Scale (SUS) yielded a score of 79.25, indicating high acceptance among the user demographic. The results demonstrate that the proposed architecture successfully reduces information asymmetry and provides a robust framework for data-driven decision-making in regional agriculture.
Co-Authors Afwan Anggara Alwiah, Izmy Anggara, Afwan Ari Prasetyoaji Aris Wahyu Murdiyanto Diansah, Febri Nur Fathullah, Ilham Habibi, Farid Afif Hanggraini Dea Astuti Ilham Fathullah Ilham Fathullah Iman Yanuar Istikmal, Agil Ghani Kholis, Muhammad Yunus Kusuma, Alif Arya Kusumojakti, Mochamad Aqsha Pasalewa Laksana, Danu Dwiki Manullang, Ali Sahat Pardomuan Marlina, Widia Muhammad Zakariyah Naswin, Ahmad Norfajriyah, Norfajriyah Nurjaman, Muhammad Prasetyo, Sushilo Pratama, Satria Yoga Purnamasari, Dwi Amalia Putri, Madina Hayva Rabbani, Muhammad Riefky Raden Andy Kurniawan Ramadhan, Idham Rismayanti, Nurul Rizka Amanah Setia, Bayu Sumiyatun Tati Indriani Widianto, Hafizh Athallah