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All Journal BIOS : Jurnal Teknologi Informasi dan Rekayasa Komputer PEKAT : Jurnal Pengabdian Kepada Masyarakat International Journal of Health and Information System (IJHIS) International Journal of Healthcare and Information Technology The International Journal of Studies in Social Sciences and Humanities (IJOSSH)
Pratama, Mudafiq Riyan
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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.
Sistem Deteksi Dini Diabetes Melitus Dengan Teknik Klasifikasi Algoritma C4.5 Berdasarkan Rekam Medis di RS Tk. III Baladhika Husada Jember Agustina, Alviani Rodyatul; Pratama, Mudafiq Riyan; Yunus, Muhammad; Rachmawati, Ervina
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.v7i1.203

Abstract

Diabetes Mellitus (DM) is a chronic disease condition that occurs when the pancreas cannot produce insulin or when the body cannot use insulin effectively. At Baladhika Husada Jember Hospital, DM ranks among the top 10 diseases with the highest mortality rate of 6.99% in 2024. In efforts to prevent and control DM, a website-based early detection system was developed using the C4.5 algorithm classification technique with the Waterfall method. The research stages included creating C4.5 algorithm classification rules using RapidMiner tools, followed by development using the Waterfall method, which consists of the communication, planning, modeling, construction, and deployment stages. The classification rules were developed using preprocessed data from a total of 240 datasets, resulting in 172 clean datasets obtained from medical records at Baladhika Husada Jember Hospital. The training and testing data ratio was 50:50 using stratified sampling. Performance testing using the Confusion Matrix method yielded accuracy, precision, and recall values of 100% each, along with 8 classification rules that were subsequently implemented in the system. Based on the research results, random blood sugar is the most influential risk factor for DM, as it achieved the highest gain ratio. Recommendations for future researchers include increasing the amount of data and expanding the variety of data to help the system learn more complex patterns.
Co-Authors Adriadi Novawan Agustina, Alviani Rodyatul Alfiansyah, Gamasiano AULIA ARIF WARDANA Dahlia Indah Amareta, Dahlia Indah Haq, Rayya Nurrisqinia Ida Nurmawati, Ida Karimah, Karimah Maghfiroh, Canggih Nailil Maulana Firmansyah, Akbar Muflihatin, Indah Muhammad Yunus Nurjannah, Nada Savira Olivia, Zora Prakoso, Bakhtiyar Hadi Prawidya Destarianto Putro Mudiono, Demiawan Rachmatta Rachmawati, Ervina Rizky, Dian Selviyanti, Erna Suryana, Arinda Lironika Sustin Farlinda, Sustin Suyoso, Gandu Eko Julianto Swari, Selvia Juwita Titik Ismailia, Titik Umam, Ahmad Busyronul Utami, Lely Dian Yunitasari, Arleni Aulia Zuhro, Cholimatus