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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 Inovatif : Inovasi Teknologi Informasi dan Informatika 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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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.
Diabetes Mellitus Prediction from Primary Health Care Laboratory Data Using Random Forest, Extreme Gradient Boosting, and Light Gradient Boosting Machine with Resampling and Optuna-Based Hyperparameter Optimization Zaky, Umar; Agustia, Yuwanis Fazlina
Jurnal Teknik Informatika (Jutif) Vol. 7 No. 3 (2026): JUTIF Volume 7, Number 3, June 2026
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2026.7.3.5751

Abstract

This study examines the use of machine learning models to classify diabetes mellitus status based on laboratory test data. The dataset consists of 484 laboratory test results with 10 clinical parameters, which were used as the main input for model development. Three algorithms, Random Forest, Extreme Gradient Boosting, and Light Gradient Boosting Machine, were compared by applying several resampling techniques and hyperparameter tuning using Optuna to address class imbalance and improve overall model performance. The results show that each algorithm responded differently to the applied resampling methods and tuning strategies, indicating that model performance is influenced by these approaches. Among the evaluated models, Random Forest combined with Synthetic Minority Oversampling Technique and hyperparameter optimization achieved the best performance, with an accuracy of 72.60% and an area under the receiver operating characteristic curve of 76.74%. This performance indicates a moderate ability to distinguish between diabetes and non-diabetes cases based on the available laboratory parameters. Overall, the findings suggest that machine learning can be considered as a potential tool to support clinical decision making, especially when using structured laboratory data. However, given that the performance is still not optimal, further improvement, validation, and exploration of additional data are necessary before considering its implementation in real clinical settings.
Co-Authors Abdulhafidz, Yahya Admojo, Fadhila Tangguh Adri Priadana Afwan Anggara Agustia, Yuwanis Fazlina 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 Habibi 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 Thomas Edyson Tarigan Victoria Ferdina Widianto, Hafizh Athallah