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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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Journal : Jurnal Teknik Informatika (JUTIF)

 1 
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.
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.
Development of Mobile Quran App with Screen Time Monitoring Using DRM, Agile, and Sus-Use Testing Abdulhafidz, Yahya; Zaky, Umar; Admojo, Fadhila Tangguh
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 6 (2025): JUTIF Volume 6, Number 6, Desember 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.6.5398

Abstract

The rapid growth of mobile applications has changed user behavior in the digital age, including how individuals interact with religious content. However, excessive use of social media has led to behavioral problems such as doom scrolling, zombie scrolling, and digital addiction, phenomena collectively known as “brain rot,” which negatively impact cognitive, emotional, and spiritual well-being. This study aims to develop and evaluate Quran Break, a mobile Quran application that integrates screen time monitoring as a digital behavior intervention to encourage users to stop scrolling and engage in reading the Quran. The methodology applies the Design Research Methodology (DRM) through four iterative stages, supported by an Agile development model with short, adaptive sprints that enable continuous feedback and improvement. 18 participants were involved in usability testing using the System Usability Scale (SUS) and the Usability, Satisfaction, Ease of Learning, and Ease of Use (USE) questionnaire. The results showed that the application achieved an average SUS score of 75 (Good) and a USE score of 87.7% (Very Good), indicating that Quran Break is effective, useful, and easy to use. This discovery contributes to the fields of Religious Informatics and Human-Computer Interaction (HCI) by integrating persuasive technology into faith-based digital systems, supporting digital well-being, and promoting a balanced interaction between technology use and spiritual activities.
Co-Authors Abdulhafidz, Yahya Admojo, Fadhila Tangguh 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