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All Journal Journal of Public Health for Tropical and Coastal Region JURIKOM (Jurnal Riset Komputer) ARTERI : Jurnal Ilmu Kesehatan BIOS : Jurnal Teknologi Informasi dan Rekayasa Komputer PELS (Procedia of Engineering and Life Science) International Journal of Health and Information System (IJHIS) International Journal of Healthcare and Information Technology
Suyoso, Gandu Eko Julianto
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering Health Professions Library & Information Science Medicine & Pharmacology Nursing Public Health

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Pemetaan Literatur Sistem Digital Pemantauan Anak: Menyusun Kerangka Kebutuhan untuk Platform E-Kesehatan Terintegrasi di Indonesia Selviyanti, Erna; Suyoso, Gandu Eko Julianto; Pratama, Mudafiq Riyan; Yunus, Muhammad
BIOS : Jurnal Teknologi Informasi dan Rekayasa Komputer Vol 7 No 1 (2026): March
Publisher : Puslitbang Sinergis Asa Professional

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37148/bios.v7i1.197

Abstract

Background: Child growth and development monitoring is a fundamental intervention in preventing stunting and developmental delays. Although various digital systems have been developed, fragmentation between physical growth and child development monitoring remains a major challenge. Objective: This study aims to map the research landscape of digital systems for child growth and development monitoring and identify gaps and the most frequently appearing system requirements in the literature. Method: A systematic mapping study was conducted on six data sources consisting of direct access and Publish or Perish searches, including Scopus, Scopus via PoP, PubMed, PubMed via PoP, IEEE Xplore, and Web of Science via PoP for the 2020-2025 period. A total of 16 research articles were analyzed using VOSviewer for bibliometric mapping and thematic analysis for system requirements identification. Results: The mapping revealed six research clusters with digital health positioned peripherally. Temporal analysis showed a shifting focus toward mental health integration, stunting prevention, and digital tool utilization. Three main gaps were identified: the peripheral position of digital health, separation between growth and development domains, and dominance of observational studies. The most dominant functional requirements were growth monitoring (12 articles) and developmental screening (11 articles), while usability (9 articles) was the main non-functional priority. Conclusion: Integration of growth and development monitoring in a single platform remains understudied. The resulting requirements list can serve as an initial reference for future integrated system development.
GROWTH: An Innovative Approach to Monitoring Child Health and Development Suyoso, Gandu Eko julianto; Selviyanti, Erna; Pratama, Mudafiq Riyan; Yunus, Muhammad
International Journal of Healthcare and Information Technology Vol. 4 No. 1 (2026): July (In Progress)
Publisher : P3M Politeknik Negeri Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25047/ijhitech.v4i1.6725

Abstract

Monitoring child growth and development during the First 1,000 Days of Life is critical for preventing stunting and developmental delays. Despite national policies mandating routine monitoring, current practices in Indonesia remain largely manual and fragmented, limiting real-time data access and early intervention. This gap highlights the need for an integrated, real-time digital monitoring tool that connects parents and health workers.This study aims to develop GROWTH (Global Real-time Observation of Well-being, Tracking, and Health), a web-based system that integrates physical growth and developmental monitoring for children aged 0–72 months. The system was developed using the Scrum framework, involving parents and midwives in the requirements elicitation process. System functionality was evaluated through black-box testing using simulated child growth and development data, as well as its usability was assessed by end-users (parents and midwives). The results indicate that all core features functioned as expected, including growth chart visualization based on WHO Anthro standards and age-appropriate developmental screening. The GROWTH system offers a practical digital solution to support timely decision-making by parents and healthcare workers, with potential for integration into national child health information systems.
Sistem Deteksi Dini Diabetes Mellitus Berdasarkan Rekam Medis Menggunakan Algoritma K-Nearest Neighbor Yunitasari, Arleni Aulia; Pratama, Mudafiq Riyan; Yunus, Muhammad; Suyoso, Gandu Eko Julianto
BIOS : Jurnal Teknologi Informasi dan Rekayasa Komputer Vol 7 No 1 (2026): March
Publisher : Puslitbang Sinergis Asa Professional

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37148/bios.v7i1.204

Abstract

At RSD dr. Soebandi Jember, 51% of Diabetes Mellitus (DM) patients are diagnosed after complications occur, and DM is the third leading cause of death among non-communicable diseases, accounting for 13.6%. This situation indicates a high rate of delayed case identification. Delayed diagnosis significantly increases patient mortality and morbidity rates, emphasizing the urgent need for an effective, integrated early, and detection system. This study developed a web-based early detection system for DM using the K-Nearest Neighbor (K-NN) algorithm with the Waterfall development method, consisting of the stages of communication, planning, modeling, construction, and deployment. The data comprised from 342 inpatient medical records, and after preprocessing, 164 clean data were obtained with variables including age, gender, family history, blood pressure, random blood sugar, and body mass index. The data were split using stratified sampling (50:50), with K=5 value selected based on the best performance. Blackbox testing was conducted to ensure the system’s functionality, while performance testing compared the system’s classification results with the test data. The performance of the K-NN algorithm for DM detection was evaluated using a Confusion Matrix, resulting in an accuracy of 97.56%, precision of 100%, and recall of 95.83%, which were consistent with the results from the WEKA tool. This system is expected to serve as an early screening tool and support DM prevention efforts.
Co-Authors Alfiansyah, Gamasiano Bakhtiyar Hadi Prakoso Deharja, Atma Hastri, Encik Ameilia Permata Iskandar, Abdullah Istianingsih, Noviana Mudiono, Demiawan Rachmatta Putro Muhammad Yunus Muhammad Yunus Nasution, Nurlia Susanti Prakoso, Bakhtiyar Hadi Pratama, Mudafiq Riyan Putra, Dony Setiawan Hendyca Rachmawati, Ervina Rahagiyanto, Angga Santoso, Noviva Maulita Selviyanti, Erna Vestine, Veronika Yestina, Elsa Adinda Yunitasari, Arleni Aulia