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All Journal Jurnal Ilmiah Merpati (Menara Penelitian Akademika Teknologi Informasi) Transmisi: Jurnal Ilmiah Teknik Elektro Techno.Com: Jurnal Teknologi Informasi Jurnal Simetris Proceeding SENDI_U Jurnal Informatika Upgris JOIV : International Journal on Informatics Visualization Sinkron : Jurnal dan Penelitian Teknik Informatika Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Building of Informatics, Technology and Science Infotekmesin Abdimasku : Jurnal Pengabdian Masyarakat Jurnal Ilmiah Intech : Information Technology Journal of UMUS Jurnal Teknik Informatika (JUTIF) JAMU : Jurnal Abdi Masyarakat UMUS Moneter : Jurnal Keuangan dan Perbankan
Megantara, Rama Aria
Dian Nuswantoro University
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Journal : Building of Informatics, Technology and Science

 1 
Decision Tree Classification for Reducing Alert Fatigue in Patient Monitoring Systems Herfiani, Kheisya Talitha; Nurhindarto, Aris; Alzami, Farrikh; Budi, Setyo; Megantara, Rama Aria; Soeleman, M Arief; Handoko, L Budi; Rofiani, Rofiani
Building of Informatics, Technology and Science (BITS) Vol 7 No 3 (2025): December 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i3.8414

Abstract

The development of information technology in healthcare opens new opportunities to improve continuous patient monitoring. A major challenge is alert fatigue, where medical personnel are overwhelmed by excessive notifications, reducing concentration, work efficiency, and potentially compromising patient safety. This study presents a proof-of-concept application of the Decision Tree algorithm to analyze alert triggering factors in patient monitoring systems. The dataset is a synthetic health monitoring dataset from Kaggle, containing 10,000 entries with vital parameters including blood pressure, heart rate, oxygen saturation, and glucose levels, designed with deterministic logical relationships between threshold indicators and alert outcomes. The imbalanced dataset (73.67% alert triggered, 26.33% no alert) was intentionally not processed using imbalanced learning techniques to demonstrate Decision Tree's capability in processing structured health data and producing interpretable classifications. The research methodology included data preprocessing, exploratory data analysis, data splitting (90% training, 10% testing), GridSearchCV optimization, and performance evaluation. Results showed perfect metrics (100% accuracy, precision, recall, F1-score), reflecting the deterministic nature of the synthetic dataset rather than real-world clinical complexity. Feature importance analysis identified blood pressure as the most dominant variable, followed by heart rate and glucose levels. This study demonstrates Decision Tree's interpretability and feature importance analysis capabilities in health data contexts, establishing a methodological framework that requires validation on real clinical Electronic Health Record (EHR) data for practical application in reducing alert fatigue and supporting informed clinical decisions.
Data-Driven K-Means Clustering Analysis for Stunting Risk Profiling of Pregnant Women Nazella, Desvita Dian; Hadi, Heru Pramono; Al Zami, Farrikh; Ashari, Ayu; Kusumawati, Yupie; Suharnawi, Suharnawi; Megantara, Rama Aria; Naufal, Muhammad
Building of Informatics, Technology and Science (BITS) Vol 7 No 3 (2025): December 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i3.8415

Abstract

Stunting in children is influenced by maternal health conditions during pregnancy. This study aims to classify pregnant women to prevent stunting based on clinical, demographic, and environmental factors using the K-Means Clustering algorithm. A total of 229 data from the Primadona application (Disdalduk KB Kota Semarang) were analyzed using 14 normalized variables. The optimal number of clusters was determined using the Elbow Method and validated using the Silhouette Score, Davies-Bouldin Index, and Calinski-Harabasz Index. The Kruskal-Wallis test was performed to verify differences between clusters. This study resulted in seven clusters with different profiles, with a Silhouette Score of 0.134, Davies-Bouldin Index of 1.509, and Calinski-Harabasz Index of 29.54. These values ​​indicate that the cluster structure is formed and reflects the variation in risk for pregnant women, although there is overlap due to differences in characteristics between individuals. The clustering successfully differentiated pregnant women with low to high risk, influenced by health and environmental factors. This study proves the effectiveness of K-Means in identifying stunting risk patterns in pregnant women and supports more targeted interventions, such as nutritional counseling, disease risk monitoring, education on cigarette smoke exposure, and referrals. Limitations of this study include the unbalanced distribution of data between and the use of cross-sectional data. Future research is recommended to improve pre-processing and compare other clustering methods such as K-Medoids or DBSCAN for more precise stunting risk analysis.
Comprehensive Benchmark of Yolov11n, SSD MobileNet, CenterFace, Yunet, FastMtCnn, HaarCascade, and LBP for Face Detection in Video Based Driver Drowsiness Go, Agnestia Agustine Djoenaidi; Alzami, Farrikh; Naufal, Muhammad; Azies, Harun Al; Winarno, Sri; Pramunendar, Ricardus Anggi; Megantara, Rama Aria; Maulana, Isa Iant; Arif, Mohammad
Building of Informatics, Technology and Science (BITS) Vol 7 No 3 (2025): December 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i3.8678

Abstract

Face detection is a critical foundation of video-based drowsiness monitoring systems because all downstream tasks such as eye-closure estimation, yawning detection, and head movement analysis depend entirely on correctly identifying the face region. Many previous studies rely on detector-generated outputs as ground truth, which can introduce bias and inflate model performance . To avoid this limitation, I manually constructed a ground truth dataset using 1,229 frames extracted from 129 yawning and microsleep videos in the NITYMED dataset. Ten representative frames were sampled from each video using a face-guided extraction script, and all frames were manually annotated in Roboflow following the COCO format to ensure accurate bounding box labeling under varying lighting, head poses, and facial deformation. Using this manually annotated dataset, I conducted a comprehensive benchmark of seven face-detection algorithms: YOLOv11n, SSD MobileNet, CenterFace, YuNet, FastMtCnn, HaarCascade, and LBP. The evaluation focused on localization quality using Intersection over Union (IoU ≥ 0.5) and Dice Similarity, allowing each algorithm’s predicted bounding box to be directly compared against human defined ground truth. The results show that HaarCascade achieved the highest IoU and Dice scores, particularly in frontal and well-lit frames. FastMtCnn also produced strong alignment with a high number of correctly matched frames. CenterFace and SSD MobileNet demonstrated smooth bounding box fitting with competitive Dice scores, while YOLOv11n and YuNet delivered moderate but stable performance across most samples. LBP showed the weakest results, mainly due to its sensitivity to lighting variations and soft-texture regions. Overall, this benchmark provides an unbiased and comprehensive comparison of modern and classical face-detection algorithms for video-based driver-drowsiness applications.
Co-Authors Abu Salam Adrian, Aurell Zulfa Angger Ahmad Akrom Ahmad Khotibul Umam, Ahmad Khotibul Akrom, Ahmad Al zami, Farrikh Al-Azies, Harun Alzami, Farrikh Anggi Pramunendar, Ricardus Ashari, Ayu Asih Rohmani, Asih Budi, Setyo Chaerul Umam Dewi Agustini Santoso Diana Aqmala Dibyo Adi Wibowo Dwi Puji Prabowo Dwi Puji Prabowo, Dwi Puji Erika Devi Udayanti Fahmi Amiq Fauzi Adi Rafrastara Fikri Diva Sambasri Firman Wahyudi, Firman Go, Agnestia Agustine Djoenaidi Guruh Fajar Shidik Hadi, Heru Pramono Harun Al Azies Heni Indrayani Herfiani, Kheisya Talitha Ifan Rizqa Ika Novita Dewi Irwan, Rhedy ISWAHYUDI ISWAHYUDI Khoirunnisa, Emila Kurniawan Aji Saputra Kurniawan, Defri Kusumawati, Yupie L. Budi Handoko Lalang Erawan Lesmarna, Salsabila Putri Mahendra, Syafrie Naufal Maulana, Isa Iant Moch. Sjamsul Hidajat Moh Yusuf, Moh Moh. Yusuf Mohammad Arif Muhammad Naufal, Muhammad Muslih Muslih Nazella, Desvita Dian Nurhindarto, Aris Ocky Saputra, Filmada Pergiwati, Dewi Puji Prabowo, Dwi Pulung Nurtantio Andono Puri Sulistiyawati Puri Sulistiyawati Ramadhan Rakhmat Sani Ratmana, Danny Oka Ricardus Anggi Pramunendar Rifqi Mulya Kiswanto Rini Anggraeni Ritzkal, Ritzkal Rofiani, Rofiani Rohman, Muhammad Syaifur Saputra, Filmada Ocky Saputra, Resha Mahardhika Saputri, Pungky Nabella Saraswati, Galuh Wilujeng Sasono Wibowo Sinaga, Daurat Soeleman, M Arief Sri Winarno Suharnawi Suharnawi Widyatmoko Karis Yuventius Tyas Catur Pramudi Zahro, Azzula Cerliana