Article Per Year (5 Year)
p-Index From 2021 - 2026
2.096
P-Index
This Author published in this journals
All Journal Jurnal Teknologi Informasi dan Ilmu Komputer EMITTER International Journal of Engineering Technology Jurnal Kesehatan Journal of Community Development International Journal of Health and Information System (IJHIS) International Journal of Healthcare and Information Technology Jurnal Pengabdian dan Pemberdayaan Banyuwangi
Wicaksono, Andri Permana
Unknown Affiliation
Humanities Computer Science & IT Economics, Econometrics & Finance Engineering Health Professions Library & Information Science Medicine & Pharmacology Nursing Public Health Social Sciences Other

Published : 13 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 13 Documents
Search

 1   2 
Development of VANESA (Virtual Assistant Nutritional Care Centre for Education and Consultation) for Diabetes Mellitus Management Nuraini, Novita; Wijayanti, Rossalina Adi; Dewi, Riskha Dora Candra; Wicaksono, Andri Permana
International Journal of Healthcare and Information Technology Vol. 3 No. 2 (2026): January
Publisher : P3M Politeknik Negeri Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25047/ijhitech.v3i2.6706

Abstract

Diabetes Mellitus (DM) remains a significant public health challenge in Indonesia, with rising prevalence and limited access to personalized nutritional care. The Nutrition Care Centre (NCC) at Jember State Polytechnic offers diet consultation and education services but faces constraints in human resources and client reach, with only 23% of visitation targets met since 2021. To address this gap, this study aims to develop VANESA, a virtual assistant powered by artificial intelligence (AI) and QR code technology to provide accessible, efficient, and contextual education and consultation services for DM patients. The system integrates rule-based and AI-based chatbot functionalities to deliver natural language responses regarding DM management, including dietary guidance, physical activity, and basic treatment advice. It also facilitates telehealth consultations, automated registration via QR code, and seamless integration with the existing Electronic Medical Record (EMR) system. Developed using the Waterfall model of SDLC, VANESA is designed to enhance service accessibility, reduce operational costs, and support continuous monitoring of patient nutritional status. Expected outcomes include a web-based admin system, an AI-driven Q&A chatbot, telehealth features, QR-based registration, and an analytics dashboard. This innovation not only supports the Teaching Factory NCC but also serves as a scalable model for digital health interventions in resource-limited settings. Thus, VANESA is a digital health solution that has a direct and applicable impact, is able to increase service efficiency, expand the reach of nutritional interventions, and can be replicated and scaled sustainably in various health facilities, especially in areas with limited resources.
Usability Evaluation of a Web-Based e-KMS for Toddler Nutritional Status Monitoring Using the System Usability Scale (SUS) Muna, Niyalatul; Muflihatin, Indah; Nurmawati, Ida; Mudiono, Demiawan Rachmatta Putro; Wicaksono, Andri Permana
International Journal of Healthcare and Information Technology Vol. 3 No. 2 (2026): January
Publisher : P3M Politeknik Negeri Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25047/ijhitech.v3i2.6846

Abstract

A nutritional status assessment system is an electronic health information system that records toddler growth based on age-specific weight and height measurements. Parents may not always maintain appropriate scheduling or regular visits for periodic monitoring at healthcare facilities. This study presents a usability evaluation of a web-based nutritional monitoring system that implements an Electronic Growth Monitoring Card (e-KMS). Users interact with the system through a user interface (UI) and user experience (UX) designed to support effective interaction. The main contribution of this study is the empirical validation of the usability level of a web-based e-KMS in a healthcare context using the System Usability Scale (SUS). The evaluation involved 20 respondents, consisting of parent representatives and daycare teachers or administrators as e-KMS users, and employed 10 SUS statements measured on a five-point Likert scale. The SUS analysis yielded a score of 77.78, indicating a good and acceptable level of usability. These findings demonstrate that the developed e-KMS is suitable for supporting digital-based monitoring of toddler nutritional status in healthcare services.
Comparison of The Data-Mining Methods in Predicting The Risk Level of Diabetes Wicaksono, Andri Permana; Badriyah, Tessy; Basuki, Achmad
EMITTER International Journal of Engineering Technology Vol 4 No 1 (2016)
Publisher : Politeknik Elektronika Negeri Surabaya (PENS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (813.592 KB) | DOI: 10.24003/emitter.v4i1.119

Abstract

Mellitus Diabetes is an illness that happened in consequence of the too high glucose level in blood because the body could not release or use insulin normally. The purpose of this research is to compare the two methods in The data-mining, those are a Regression Logistic method and a Bayesian method, to predict the risk level of diabetes by web-based application and nine attributes of patients data. The data which is used in this research are 1450 patients that are taken from RSD BALUNG JEMBER, by collecting data from 26 September 2014 until 30 April 2015. This research uses performance measuring from two methods by using discrimination score with ROC curve (Receiver Operating Characteristic).  On the experiment result, it showed that two methods, Regression Logistic method and Bayesian method, have different performance excess score and are good at both. From the highest accuracy measurement and ROC using the same dataset, where the excess of Bayesian has the highest accuracy with 0,91 in the score while Regression Logistic method has the highest ROC score with 0.988, meanwhile on Bayesian, the ROC is 0.964. In this research, the plus of using Bayesian is not only can use categorical but also numerical.
Co-Authors Achmad Basuki Al Ayyubi, Waridad Umais Aziz, Muhaiminul Dewi, Riskha Dora Candra Feby Erawantini Ida Nurmawati, Ida mudiono , demiawan mudiono , Demiawan rachmatta putro Mudiono, Demiawan Rachmatta Putro Mudiono, Demiawan Rahmatta Putro Muflihatin, Indah Muhammad Yunus Muna, Niyalatul Navalani, Faradita Nuraini, Novita Puspito Arum Ramadhani, Riska Isyraq Roziqin, Mochammad Choirur Sutantio, R. Alamsyah Tessy Badriyah, Tessy Wahyono, Nanang Dwi Wijayanti, Rossalina Adi