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All Journal Jurnal Mata Optik Jurnal Ilmiah Ilmu Terapan Universitas Jambi
Umami, Nisa Zakiati
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KESENJANGAN PRAKTIK DALAM PELAYANAN OPTOMETRI: ANALISIS KUALITATIF TERHADAP KOMPETENSI, LINGKUNGAN PRAKTIK, DAN REGULASI Silaban , Stepanus; Simarmata, Murni; Umami, Nisa Zakiati
Jurnal Mata Optik Vol. 7 No. 1 (2026): JURNAL MATA OPTIK
Publisher : Akademi Refraksi Optisi dan Optometry Gapopin

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54363/jmo.v7i1.304

Abstract

Penelitian ini bertujuan untuk mengeksplorasi kesenjangan antara standar dan praktik nyata dalam pelayanan optometri melalui pendekatan kualitatif. Data dikumpulkan melalui wawancara mendalam dengan optometris dan dianalisis menggunakan Thematic Analysis. Hasil penelitian mengungkapkan empat tema utama, yaitu: (1) perluasan peran preventif optometris, (2) variasi dalam kompetensi profesional, (3) pengaruh lingkungan praktik terhadap pengambilan keputusan klinis, dan (4) lemahnya penegakan regulasi. Temuan ini menunjukkan bahwa kualitas layanan tidak hanya ditentukan oleh kompetensi individu, tetapi juga dibentuk oleh interaksi antara faktor individu, lingkungan, dan sistem. Penelitian ini mengusulkan Model Interaksi Sistem–Individu dalam Praktik Optometri, yang menekankan pentingnya intervensi terintegrasi pada berbagai tingkat. Secara teoretis, penelitian ini memberikan kontribusi berupa model konseptual berbasis data lapangan, dan secara praktis memberikan rekomendasi untuk meningkatkan kualitas layanan optometri.
DIAGNOSTIC ACCURACY AND RELIABILITY OF GEMINI AI FOR MENTAL HEALTH SCREENING Maryani, Febri; Abdillah, Bunyamin Rizki; Nugraha, Opep Cahya; Habiba, Putri Ghanim Septia; Umami, Nisa Zakiati; Simarmata, Murni Marlina; Budiana, M Wahyu
Jurnal Ilmiah Ilmu Terapan Universitas Jambi Vol. 10 No. 4 (2026): Volume 10, Nomor 4, August 2026
Publisher : LPPM Universitas Jambi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22437/jiituj.v10i4.58806

Abstract

Mental health problems among university students are increasing, while stigma and limited access to professional services often delay help-seeking. Artificial intelligence (AI)-based screening tools may provide an accessible approach for early mental health detection. This study aimed to evaluate the diagnostic accuracy and reliability of Gemini AI for mental health screening within Indonesia’s SATU SEHAT framework. A pilot cross-sectional diagnostic accuracy study was conducted among 63 undergraduate students. Participants completed mental health screening using both Gemini AI and the SATU SEHAT platform. Diagnostic performance was assessed using receiver operating characteristic (ROC) analysis, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), overall accuracy, and Cohen’s Kappa coefficient. Gemini AI demonstrated excellent discriminative ability with an area under the curve (AUC) of 0.957 (95% CI: 0.89–1.00). Sensitivity was 86.36%, specificity 97.56%, PPV 95.00%, NPV 93.02%, and overall accuracy 93.65%. Agreement with the SATU SEHAT reference standard was almost perfect (Cohen’s Kappa = 0.857, p < 0.001). Gemini AI showed high diagnostic accuracy and reliability for early mental health screening. These findings suggest that AI-assisted screening may complement existing digital mental health services by supporting early identification of individuals at risk and providing a foundation for larger validation studies.
Co-Authors Abdillah, Bunyamin Rizki Budiana, M Wahyu Habiba, Putri Ghanim Septia Maryani, Febri Nugraha, Opep Cahya Silaban , Stepanus Simarmata, Murni Simarmata, Murni Marlina