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All Journal Journal of Computer Networks, Architecture and High Performance Computing Public Health and Safety International Journal (PHASIJ) Engineering and Technology International Journal (EATIJ) Journal of Engineering and Technology Innovation MPKM
sherly agustini
Universitas Ibnu Sina
Aerospace Engineering Agriculture, Biological Sciences & Forestry Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Mechanical Engineering Medicine & Pharmacology Nursing Public Health Social Sciences Transportation Veterinary Other

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Journal : journal of engineering and technology innovation

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Challenges And Opportunities For Implementing Midwifery Information Systems In Remote Areas Sherly Agustini
Journal Of Engineering And Technology Innovation ( JETI ) Vol. 2 No. 01 (2025): Journal Of Engineering And Technology Innovation ( JETI )
Publisher : Rey Media Grafika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66084/jeti.v2i01.417

Abstract

Maternal and child health is a top priority in Indonesia's healthcare system, especially in remote areas that face limited access, medical personnel shortages, and inadequate healthcare facilities. The implementation of a midwifery information system based on information technology is a potential solution to improve the quality of maternal and neonatal healthcare services. However, the adoption of this system still encounters various challenges, such as limited technological infrastructure, lack of training for healthcare workers, and internet accessibility issues. This study aims to analyze the challenges and opportunities in implementing a midwifery information system in remote areas using a qualitative descriptive approach. Data were collected through literature studies and interviews with midwives, health department officials, and healthcare information system developers. The results indicate that despite significant obstacles in implementing the midwifery information system, opportunities such as mobile applications, cloud-based systems, and integration with national health systems can enhance the efficiency of midwifery healthcare services. Government policy support and capacity-building programs for healthcare workers are key factors in the successful implementation of this system.
Development of Deep Learning Techniques for Dental Caries Detection: A Systematic Literature Review Sherly Agustini; Nofri Yudi Arifin
Journal Of Engineering And Technology Innovation ( JETI ) Vol. 2 No. 02 (2025): Journal Of Engineering And Technology Innovation ( JETI )
Publisher : Rey Media Grafika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66084/jeti.v2i02.504

Abstract

Dental caries remains one of the most prevalent oral health problems worldwide, requiring early and accurate detection to prevent extensive damage and reduce treatment costs. Recent advances in artificial intelligence particularly deep learning have led to significant improvements in diagnostic accuracy and consistency compared to traditional visual or radiographic assessments. This systematic literature review evaluates the development of deep learning techniques for dental caries detection based on studies published between 2020 and 2024. Following the PRISMA 2020 guidelines, six eligible studies were identified from Scopus, PubMed, IEEE Xplore, and ScienceDirect databases. The review synthesizes findings related to model architectures, imaging modalities, dataset characteristics, and performance metrics. The results show that models such as CNN, YOLO, U-Net, and EfficientNet consistently demonstrate high accuracy in identifying carious lesions, with bitewing and panoramic radiographs producing the most reliable diagnostic outcomes. However, limitations remain, including dataset variability, limited sample sizes, and reduced sensitivity for early-stage lesions. This review highlights current progress, methodological challenges, and potential research opportunities, emphasizing the need for standardized datasets, improved clinical validation, and stronger multidisciplinary collaboration to support the integration of deep learning into dental diagnostic workflows.
Co-Authors Albertus L. Setyabudhi David Saro Larisang Mahdi Aulia Suheri Nofri Yudi Arifin Okta Veza