Oktavia Putri Handayani
Universitas Harapan Bangsa

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Revolusi Kesehatan Digital: Peran AI dan Pembelajaran Mesin dalam Diagnosa Perawatan Oktavia Putri Handayani
KORISA: Jurnal Kolaborasi Riset Sarjana Vol. 1 No. 2 (2024): Jurnal Kolaborasi Riset Sarjana Volume 1 Nomor 2 Juli 2024
Publisher : Lembaga Penelitian dan Pengabdian kepada Masyarakat (LP2M) Universitas Harapan Bangsa

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Abstract

Pemanfaatan Kecerdasan Buatan (Artificial Intelligence/AI) dan Pembelajaran Mesin (Machine Learning/ML) semakin berkembang dalam sistem kesehatan modern, khususnya dalam meningkatkan akurasi diagnosis, perencanaan perawatan, dan pengelolaan data medis. Kajian tinjauan naratif ini bertujuan untuk merangkum bukti ilmiah terkini mengenai penerapan AI dan ML dalam diagnosis medis dan layanan perawatan. Pencarian literatur dilakukan melalui database ScienceDirect, IEEE Xplore, dan SpringerLink, dengan fokus pada studi yang membahas diagnostik berbasis AI, pemodelan prediktif, serta sistem pendukung keputusan klinis. Sebanyak 15 artikel relevan yang terbit pada rentang tahun 2015 hingga 2024 disertakan berdasarkan kesesuaian topik dan kualitas metodologis. Hasil kajian menunjukkan bahwa AI dan ML memberikan manfaat besar dalam diagnosis berbasis citra, interpretasi data genomik, manajemen penyakit kronis, serta prediksi risiko klinis. Beberapa studi bahkan melaporkan tingkat akurasi diagnosis yang menyamai atau melampaui tenaga medis manusia. Namun, tantangan tetap muncul, termasuk bias algoritma, isu privasi data, serta keterbatasan infrastruktur kesehatan terutama di negara berkembang. Kajian ini menyimpulkan bahwa integrasi AI dalam layanan kesehatan membutuhkan kolaborasi erat antara tenaga medis dan pengembang teknologi, disertai pengawasan etis dan regulasi yang kuat. Dengan implementasi yang bertanggung jawab, AI dan ML berpotensi besar mendorong peningkatan presisi diagnosis, optimalisasi jalur perawatan, serta penguatan ekosistem kesehatan digital di masa depan.
Algoritma Cocke-Younger-Kasami untuk Sistem Deteksi Kesalahan Pola Kalimat Bahasa Sunda Oktavia Putri Handayani; Sony Kartika Wibisono; Khoirun Nisa
KORISA: Jurnal Kolaborasi Riset Sarjana Vol. 2 No. 1 (2025): Jurnal Kolaborasi Riset Sarjana Volume 2 Nomor 1 Januari 2025
Publisher : Lembaga Penelitian dan Pengabdian kepada Masyarakat (LP2M) Universitas Harapan Bangsa

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Abstract

This study develops a sentence error detection system for the Sundanese language using the Cocke-Younger-Kasami (CYK) algorithm, implemented through a website platform. The system aims to efficiently and accurately detect errors in the structure of Sundanese sentences. The CYK algorithm is used to analyze sentences based on a predefined context-free grammar (CFG). The system is built using HTML, PHP, and CSS to create the user interface and process the data. Based on testing results, the system successfully identifies both valid and invalid sentence patterns with an accuracy of 94.44%. This research is expected to contribute to the preservation and understanding of the Sundanese language, as well as make it easier for people to correct errors in their everyday communication.
Strategi Implementasi Telehealth Informatika dan Hasil Mutu dalam Keperawatan Komunitas Awal Tunis Yantoro; Oktavia Putri Handayani; Purwono Purwono
KORISA: Jurnal Kolaborasi Riset Sarjana Vol. 3 No. 1 (2026): Vol. 3 No. 1 (2026): Jurnal Kolaborasi Riset Sarjana Volume 3 Nomor 1 Januari 2
Publisher : Lembaga Penelitian dan Pengabdian kepada Masyarakat (LP2M) Universitas Harapan Bangsa

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The rapid expansion of digital health technologies has significantly transformed the delivery of community-based nursing services. Telehealth informatics has increasingly been adopted to enhance access, continuity, and quality of care, particularly in chronic disease management and home-based services. However, implementation approaches vary across health systems, and the relationship between implementation strategies and quality outcomes remains insufficiently synthesized. This study aimed to identify telehealth informatics implementation strategies in community nursing, analyze their impact on quality outcomes, and explore barriers and facilitators influencing adoption. A systematic literature review was conducted in accordance with PRISMA guidelines. Searches were performed in Scopus, ScienceDirect, PubMed, and CINAHL for studies published between 2022 and 2026. Eligible studies focused on telehealth interventions implemented in community or home care nursing contexts and reported measurable quality outcomes. Fifteen international studies met the inclusion criteria and were analyzed using a descriptive-comparative thematic synthesis. The findings indicate that successful telehealth implementation is associated with interoperable digital infrastructure, structured workflow integration, managerial support, digital competency development, and regulatory alignment. Quality improvements were observed across multiple domains, including reduced hospital admissions, improved disease-specific clinical indicators, enhanced patient satisfaction, and favorable cost-effectiveness estimates. Nevertheless, variability in long-term sustainability and readmission outcomes was noted. Key barriers included limited interoperability, digital literacy gaps, reimbursement constraints, and regulatory ambiguity, whereas structured training, organizational readiness, and interdisciplinary collaboration facilitated adoption. It is concluded that telehealth informatics has substantial potential to enhance quality of care in community nursing when implemented through systematic, competency-based, and policy-supported strategies. Sustainable integration requires coordinated investment in digital infrastructure, workforce preparedness, and governance frameworks that promote equitable and effective community care delivery.