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All Journal Jurnal Pengabdian Masyarakat dan Riset Pendidikan Journal of Innovative and Creativity
Bayu Laksmana Jati
Universitas Abdi Nusantara
Humanities Education Languange, Linguistic, Communication & Media Mathematics Social Sciences Other

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Pemberdayaan Kader Kesehatan Berbasis Digital dalam Deteksi Dini Risiko Kehamilan dan Pemantauan Kesehatan Ibu dan Anak di Tingkat Komunitas: Pengabdian Budi Ermanto; Bayu Laksmana Jati; Sukarni
Jurnal Pengabdian Masyarakat dan Riset Pendidikan Vol. 4 No. 4 (2026): Jurnal Pengabdian Masyarakat dan Riset Pendidikan Volume 4 Nomor 4 April - Juni
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/jerkin.v4i4.7599

Abstract

Angka kematian ibu dan bayi di Indonesia masih menjadi tantangan kesehatan masyarakat yang serius, terutama di wilayah dengan akses layanan kesehatan yang terbatas. Kader kesehatan di tingkat posyandu memiliki peran strategis dalam deteksi dini risiko kehamilan, namun kemampuan mereka masih terbatas pada metode pencatatan manual yang rentan terhadap keterlambatan pelaporan dan kesalahan data. Kegiatan pengabdian ini bertujuan untuk memberdayakan kader kesehatan melalui pelatihan pemanfaatan aplikasi digital dalam deteksi dini risiko kehamilan serta pemantauan kesehatan ibu dan anak di tingkat komunitas. Metode yang digunakan adalah pendekatan participatory action research yang terdiri atas tahap sosialisasi, pelatihan penggunaan aplikasi, pendampingan lapangan, dan evaluasi, yang dilaksanakan terhadap 30 kader kesehatan di wilayah kerja Puskesmas selama empat bulan. Data dikumpulkan melalui kuesioner pengetahuan dan keterampilan sebelum dan sesudah pelatihan, serta observasi penggunaan aplikasi. Hasil kegiatan menunjukkan peningkatan rata-rata skor pengetahuan kader dari 58,4 menjadi 86,7 dan peningkatan keterampilan pengisian data digital sebesar 45,9%. Kader juga mampu mengidentifikasi tanda bahaya kehamilan lebih cepat dan melaporkan data secara real-time kepada tenaga kesehatan. Kegiatan ini menyimpulkan bahwa pemberdayaan kader berbasis digital efektif meningkatkan kualitas deteksi dini risiko kehamilan dan mempercepat rujukan kasus berisiko di tingkat komunitas.
Pengaruh Predictive Risk Assessment Berbasis Artificial Intelligence terhadap Kecepatan Rujukan Ibu Hamil Berisiko di Pelayanan Kebidanan Primer Udur Diana Tumanggor; Bambang Sugiharto; Bayu Laksmana Jati
Journal of Innovative and Creativity (Joecy) Vol. 6 No. 2 (2026)
Publisher : Fakultas Ilmu Pendidikan Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/joecy.v6i2.14044

Abstract

Timely referral of pregnant women with high-risk conditions is an important component of maternal healthcare. Delayed referral may increase the risk of maternal and fetal complications, particularly when clinical deterioration occurs rapidly. Conventional risk identification in primary midwifery care may depend on routine screening and clinical judgment, which can result in variation in referral decision-making. Artificial intelligence (AI)-based predictive risk assessment may provide additional decision support by identifying high-risk pregnancies and facilitating earlier referral. This study aimed to determine the effect of AI-based predictive risk assessment on the referral time of high-risk pregnant women in primary midwifery care. A quantitative quasi-experimental study with a pretest-posttest control group design was conducted among 80 pregnant women identified as having pregnancy risk factors. Participants were allocated into an intervention group receiving AI-based predictive risk assessment in addition to standard care (n = 40) and a control group receiving standard risk assessment (n = 40). The primary outcome was referral response time, defined as the interval between identification of referral indications and initiation of the referral process. Data were analyzed using the independent t-test or Mann–Whitney U test and multivariable regression analysis. Statistical significance was set at p < 0.05. The mean referral response time was 42.6 ± 16.8 minutes in the AI intervention group compared with 67.8 ± 24.5 minutes in the control group. The intervention group demonstrated a significantly shorter referral response time (mean difference = −25.2 minutes; 95% CI: −34.4 to −16.0; p < 0.001). After adjustment for maternal age, gestational age, parity, distance to referral facility, and previous pregnancy complications, AI-based predictive risk assessment remained significantly associated with faster referral (adjusted β = −21.7 minutes; 95% CI: −31.8 to −11.6; p < 0.001). AI-based predictive risk assessment was associated with significantly faster referral of high-risk pregnant women in primary midwifery care. AI-supported risk stratification may serve as a decision-support tool to strengthen early identification and timely referral while maintaining professional clinical judgment.
Co-Authors Bambang Sugiharto Budi Ermanto Sukarni Udur Diana Tumanggor