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All Journal Seminar Nasional Aplikasi Teknologi Informasi (SNATI) International Conference on Engineering and Technology Development (ICETD) METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi Jurnal ULTIMA InfoSys Jurnal ULTIMATICS METHODIKA: Jurnal Teknik Informatika dan Sistem Informasi Jurnal Pengabdian Pada Masyarakat METHABDI
Sri Agustina Rumapea
Universitas Methodist Indonesia
Religion Agriculture, Biological Sciences & Forestry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Immunology & microbiology Languange, Linguistic, Communication & Media Medicine & Pharmacology Physics Public Health

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Applied Machine learning for Pediatric Nutrition A K-Means Clustering Application Based on WHO Z-Scores Julius Sepadan Maruli Tua Manurung; Sri Agustina Rumapea; Edward Rajagukguk; Fernando Rumapea
ULTIMA InfoSys Vol 17 No 1 (2026): Ultima InfoSys : Jurnal Ilmu Sistem Informasi
Publisher : Universitas Multimedia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31937/si.v17i1.4495

Abstract

Abstract—Nutritional status in early childhood is a key indicator of public health and serves as a basis for targeted nutritional interventions. This study applies the K-Means Clustering algorithm to anthropometric data—including weight, height, and head circumference—to classify children's nutritional status into five categories: severely undernourished (−3 SD), moderately undernourished (−2 SD), normal, mildly overnourished (+1 SD), and moderately overnourished (+2 SD). The clustering process is based on Z-scores derived from WHO standards, namely Weight-for-Age (WAZ), Height-for-Age (HAZ), and Head Circumference-for-Age (HCAZ), which serve as input features for the clustering algorithm. The number of clusters (k = 5) is aligned with national nutritional classification guidelines. The clustering results are visualized to illustrate the data distribution across the three indicators, and evaluated using Euclidean distance to assess the proximity of each data point to its assigned cluster centroid. The results demonstrate that K-Means Clustering can effectively classify children's nutritional status in a manner consistent with manual classification based on WHO thresholds. This study highlights the potential of data mining approaches in supporting health information systems for the early and automated detection of child malnutrition. Index Terms—Nutritional Status; Anthropometry; Z-Score; K-Means Clustering
Pelatihan Desain UI/UX Menggunakan Figma pada SMA PGRI Siborongborong Samuel V. B. Manurung; Mufria J. Purba; Humuntal Rumapea; Darwis Robinson Manalu; Sri Agustina Rumapea; Indra M. Sarkis S.; Jimmy F. Naibaho; Yolanda Y. P. Rumapea; Arina Prima Silalahi; Agus Syahputra
Jurnal Pengabdian Pada Masyarakat METHABDI Vol 6 No 1 (2026): Jurnal Pengabdian Pada Masyarakat METHABDI
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/methabdi.Vol6No1.pp13-17

Abstract

This Community Service Program (PKM) aimed to enhance students’ digital literacy and skills through UI/UX design training using the Figma application for students of SMA PGRI Siborongborong, Tapanuli Utara Regency, North Sumatra. This activity represents the implementation of the Tri Dharma of Higher Education by the Faculty of Computer Science, Universitas Methodist Indonesia Medan, in the form of knowledge and technology transfer to secondary education institutions.The training was conducted over two days using a combination of lectures, demonstrations, and hands-on practice in designing mobile-based application interfaces. The materials covered included an introduction to digital application concepts, the fundamentals of User Interface (UI) and User Experience (UX), and the utilization of key features in Figma to design simple application prototypes. The results indicated that participants were able to understand basic UI/UX concepts and independently produce application interface designs. The students’ enthusiasm and active participation throughout the program reflected a strong interest in developing digital design skills. This activity contributed positively to improving students’ information technology competencies while strengthening collaboration between the university and the school as a community service partner.
Integrasi Algoritma YOLOv8 dan Streamlit untuk Visualisasi Real-Time dan Akurat dalam Penghitungan Kerumunan di Kawasan Stasiun Bekasi Prihandoko Prihandoko; Sri Agustina Rumapea; Abdul Hanif Pratama
METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi Vol. 9 No. 1 (2025): METHOMIKA: Jurnal Manajemen Informatika & Komputersisasi Akuntansi
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/jmika.Vol9No1.pp179-187

Abstract

Crowd management in public transportation areas has become a critical challenge with the rise of urban populations. This study develops a real-time web-based people detection and counting system by integrating the YOLOv8 algorithm with the Streamlit framework. A case study was conducted at the entrance of Bekasi Station. The model was developed using the AI Project Life Cycle approach, and the system was built following the Waterfall methodology. Data were obtained from video recordings, which were extracted into images, annotated, and processed into training and testing datasets. The YOLOv8 model was trained for 50 epochs, yielding strong performance with an mAP@0.5 of 91.7%, a maximum precision of 93.6%, and an F1-score of 87%. Tests on 15 images showed an average accuracy of 80.37% and an error rate of 19.63%. The model's performance declined on out-of-dataset images due to variations in lighting and extreme crowd density. The system was tested using black-box testing and demonstrated that all main features—image upload, object detection, visualization, and result download—functioned correctly. The system has been successfully deployed on Streamlit Cloud. These results indicate that the system offers a practical, lightweight, and responsive solution to support crowd monitoring in public areas. In future development phases, the system can be extended to support real-time video stream processing and integrated with an object tracking and classification module to accurately identify and differentiate the ingress and egress flow of individuals within a defined surveillance area.
Co-Authors Abdul Hanif Pratama Agus Syahputra Darwis Robinson Manalu Edward Rajagukguk Edward Rajagukguk Fati Gratianus Nafiri Larosa Fawwaz, Muhamad Faishal Fernando Rumapea Gea, Asaziduhu Hasibuan, Doli Humuntal Rumapea Indra M. Sarkis S. Jhoni Maslan, Jhoni Jimmy F. Naibaho Julius Sepadan Maruli Tua Manurung Kridanto Surendro Lumbanraja, Posma Mendarissan Aritonang Mufria J. Purba Nettina Samosir Pratama, Abdul Hanif Prihandoko . Prihandoko Prihandoko Purba, Nova Ferawati Rajagukguk, Edward Rumapea, Yolanda Y. P. Samosir, Nettina Samuel Bakkara Samuel Van Basten Halomoan Manurung Sarkis, Indra M. Silalahi, Arina Prima Yolanda Y. P. Rumapea