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All Journal IJISTECH (International Journal Of Information System & Technology) IJISTECH International Journal of Community Service (IJCS)
Mardiyah, Miftahul
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Mobile-Based Application Implementing the Dempster-Shafer Method for Diagnosing Post-Traumatic Stress Disorder (PTSD) Putri, Dini Ridha Dwiki; Fahlevi, Muhammad Reza; Putri, Fetty Ade; Indriani, Ulfah; Mardiyah, Miftahul; Andreansyah, Diaz
IJISTECH (International Journal of Information System and Technology) Vol 9, No 1 (2025): The June Edition
Publisher : Sekolah Tinggi Ilmu Komputer (STIKOM) Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/ijistech.v9i1.403

Abstract

Post-Traumatic Stress Disorder (PTSD) is a serious mental health condition that can develop after an individual experiences a traumatic event. Early diagnosis of PTSD symptoms is crucial to prevent more severe psychological and social impacts. This study aims to develop a mobile-based expert system application that implements the Dempster-Shafer method to independently diagnose the confidence level of PTSD symptoms. The system is designed with three main components: biodata input, a symptom selection interface based on 9 indicator questions, and an inference engine utilizing the Dempster-Shafer method with a Likert scale to assess the severity of trauma experienced by respondents. Testing was conducted on 47 respondents, and the analysis results showed that 19.1% were in the safe category, 4.3% in mild PTSD, 12.8% in moderate PTSD, and 63.8% in severe PTSD. Validity testing indicated that all symptom items had correlation values (r-calculated) greater than 0.2876, confirming their validity, and the instrument’s reliability was measured at α = 0.806. The application is capable of providing an initial diagnosis along with recommended actions based on the severity level of PTSD. This study demonstrates that applying the Dempster-Shafer method in a mobile application offers an effective, efficient, and accessible alternative solution for early PTSD diagnosis, especially for individuals with limited access to professional mental health services.
Mobile-Based Application Implementing the Dempster-Shafer Method for Diagnosing Post-Traumatic Stress Disorder (PTSD) Putri, Dini Ridha Dwiki; Fahlevi, Muhammad Reza; Putri, Fetty Ade; Indriani, Ulfah; Mardiyah, Miftahul; Andreansyah, Diaz
IJISTECH (International Journal of Information System and Technology) Vol 9, No 1 (2025): The June Edition
Publisher : Sekolah Tinggi Ilmu Komputer (STIKOM) Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/ijistech.v9i1.403

Abstract

Post-Traumatic Stress Disorder (PTSD) is a serious mental health condition that can develop after an individual experiences a traumatic event. Early diagnosis of PTSD symptoms is crucial to prevent more severe psychological and social impacts. This study aims to develop a mobile-based expert system application that implements the Dempster-Shafer method to independently diagnose the confidence level of PTSD symptoms. The system is designed with three main components: biodata input, a symptom selection interface based on 9 indicator questions, and an inference engine utilizing the Dempster-Shafer method with a Likert scale to assess the severity of trauma experienced by respondents. Testing was conducted on 47 respondents, and the analysis results showed that 19.1% were in the safe category, 4.3% in mild PTSD, 12.8% in moderate PTSD, and 63.8% in severe PTSD. Validity testing indicated that all symptom items had correlation values (r-calculated) greater than 0.2876, confirming their validity, and the instrument’s reliability was measured at α = 0.806. The application is capable of providing an initial diagnosis along with recommended actions based on the severity level of PTSD. This study demonstrates that applying the Dempster-Shafer method in a mobile application offers an effective, efficient, and accessible alternative solution for early PTSD diagnosis, especially for individuals with limited access to professional mental health services.
Integration of Tropical Disease Screening in Reproductive Health Services at Amplas Community Health Center, North Sumatra Irwandi, Syahlis; Anshari, Zaim; Ayu, Mayang Sari; Lubis, Marlina Elfa; Mardiyah, Miftahul
International Journal of Community Service (IJCS) Vol. 5 No. 1 (2026): January-June
Publisher : PT Inovasi Pratama Internasional

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55299/ijcs.v5i1.1772

Abstract

Tropical diseases remain important determinants of reproductive health in endemic regions; However, screening for these infections is rarely integrated into reproductive health services at the primary care level. This fragmentation contributes to low early detection and an increased risk of complications among women of reproductive age. This community service program aims to develop and implement an integrated model for tropical disease screening within reproductive health services at Puskesmas Amplas, Medan City. The program employed a pre–post intervention design involving women of reproductive age (15–49 years) selected through stratified random sampling. The intervention included clinical screening, health education, and the strengthening of referral and integrated recording systems. The results demonstrated a significant increase in early detection coverage and participants' knowledge, with a substantial proportion of respondents identified as having previously undetected risk factors for tropical diseases. Implementation of the integrated service model also enhanced the capacity of primary healthcare providers in delivering promotional and preventive services. These findings indicate that integrating tropical disease screening into reproductive health services is an effective and scalable approach to strengthening primary healthcare systems in endemic areas and improving community reproductive health outcomes.
Co-Authors Andreansyah, Diaz Anshari, Zaim Ayu, Mayang Sari Indriani, Ulfah Irwandi, Syahlis Lubis, Marlina Elfa MUHAMMAD REZA FAHLEVI Putri, Dini Ridha Dwiki Putri, Fetty Ade