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All Journal Jurnal Pengabdian Ilmu Kesehatan Technema: Journal of Intelligent Engineering and Computing
Vindi Tyastutik
Sekolah Tinggi Ilmu Kesehatan Bakti Utama Pati
Public Health

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Edukasi dan Praktik Prenatal Yoga sebagai Strategi Promotif Partisipatif untuk Mengurangi Nyeri Punggung Ibu Hamil Trimester II–III di Pademonegoro, Sidoarjo Vindi Tyastutik; Nopri Padma Nudesti
Jurnal Pengabdian Ilmu Kesehatan Vol. 5 No. 2 (2025): Juli: Jurnal Pengabdian Ilmu Kesehatan
Publisher : Lembaga Pengembangan Kinerja Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/jpikes.v5i2.5675

Abstract

Back pain is a common musculoskeletal complaint among pregnant women, particularly in the second and third trimesters. This complaint is caused by anatomical and hormonal changes that occur during pregnancy. Given the negative impact that back pain can have on the comfort and quality of life of pregnant women, non-pharmacological interventions are an effective option. One approach that has proven beneficial is Prenatal Yoga, which can help reduce back pain and improve maternal comfort. This Community Service Program (KKN) aimed to improve the knowledge and skills of pregnant women in Pademonegoro Village, Sukodono District, Sidoarjo, in performing Prenatal Yoga movements as a participatory, promotive effort. The methods used in this activity included educational counseling, interactive discussions, and hands-on practice of safe yoga movements for pregnant women. This activity was designed to help pregnant women understand and apply yoga movements appropriate to their physical conditions during pregnancy. Evaluation conducted after the activity showed encouraging results. 85% of participants understood the material presented, while 90% were able to imitate the main Prenatal Yoga movements, namely the cat-cow pose and pelvic tilts. This second movement is known to be effective in reducing back tension and increasing body comfort. The evaluation results indicate that a practice-based educational approach can improve pregnant women's understanding and equip them with the skills to perform yoga independently at home. This program not only contributes to improving maternal health but also supports a promotive approach within the community. By providing the appropriate knowledge and skills, pregnant women can become more independent in maintaining their health during pregnancy, thereby improving their quality of life and supporting a smoother delivery.
Federated Learning for Privacy-Preserving Intelligent Systems Riska Suryani; Andri Cahyo Purnomo; Arif Budimansyah Purba; Leonardus Teguh Handoyo; Vindi Tyastutik
Technema: Journal of Intelligent Engineering and Computing Vol. 1 No. 2 (2026): : June: Technema: Journal of Intelligent Engineering and Computing
Publisher : CV SCRIPTA INTELEKTUAL MANDIRI

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Abstract

The increasing deployment of intelligent systems across healthcare, industrial automation, smart cities, transportation, and edge-computing environments has intensified concerns regarding privacy, data sovereignty, and secure collaborative learning. This study evaluates the effectiveness of Federated Learning (FL) as a privacy-preserving paradigm capable of supporting distributed intelligence without requiring centralized data collection. An empirical experimental design was implemented using a federated architecture consisting of decentralized client nodes, a central aggregation server, the Federated Averaging algorithm, and integrated secure aggregation with differential privacy mechanisms. Experimental evaluation was conducted through repeated validation under heterogeneous client configurations and varying data distributions. The results demonstrate that the proposed framework achieved strong predictive performance, attaining 93.41% accuracy and 95.28% AUC-ROC while maintaining stable convergence under non-identically distributed data conditions. Security evaluation revealed substantial reductions in model inversion, membership inference, and gradient leakage attacks, confirming the effectiveness of the implemented privacy-preserving mechanisms. Scalability analysis further indicated that the framework maintained reliable performance across expanding client populations with acceptable communication overhead and computational efficiency. The findings confirm that Federated Learning provides a practical and scalable foundation for trustworthy intelligent systems by balancing predictive effectiveness, privacy protection, security resilience, and operational feasibility in distributed environments.
Co-Authors Andri Cahyo Purnomo Arif Budimansyah Purba Leonardus Teguh Handoyo Nopri Padma Nudesti Riska Suryani