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All Journal Viva Medika: Jurnal Kesehatan, Kebidanan dan Keperawatan Jurnal Kolaboratif Sains Jurnal Pengabdian Pada Masyarakat METHABDI Journal of Advanced Health Informatics Research
Sony Kartika Wibisono
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Private Blockchain in the Field of Health Services Purwono, Purwono; Nisa, Khoirun; Sony Kartika Wibisono; Bala Putra Dewa
Journal of Advanced Health Informatics Research Vol. 1 No. 1 (2023)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/jahir.v1i1.14

Abstract

Blockchain is a technology that is quite popular and has been adopted in various fields in recent years. This technology has caught the attention of researchers in the health sector because of its innovation which is considered capable of providing the necessary guarantees for the safe processing, sharing, and management of sensitive patient data. There are many problems with falsifying reports and withholding important information from patients, which is considered medical fraud. Hyperledger, a type of private Blockchain, is very suitable for healthcare applications. A private blockchain is a restricted type of blockchain network created by an entity. This type of network is limited to those with access permissions. In addition, private blockchains usually use a centralized verification system and are controlled by the network's creators. Hyperledger Fabric is one example of a permissioned blockchain that can play a role in implementing patient-centric, interoperable healthcare systems
Analisis Deep Learning Metode Convolutional Neural Network Dalam Klasifikasi Varietas Gandum: Analysis of Convolutional Neural Network Deep Learning Method in Durum Wheat Variety Classification Rian Ardianto; Sony Kartika Wibisono
Jurnal Kolaboratif Sains Vol. 6 No. 12: DESEMBER 2023
Publisher : Universitas Muhammadiyah Palu

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Abstract

Selama ini, Indonesia memenuhi kebutuhan gandum dengan mengimpor dari beberapa negara, seperti Australia, Ukraina, Kanada, Argentina, Amerika Serikat, Bulgaria, Moldova, Rusia, India, dan lain-lain. Tanaman ini umumnya tumbuh subur di wilayah subtropis dengan suhu berkisar 10–25°C dan curah hujan antara 350–1.250 mm. Penelitian ini bertujuan untuk menjelaskan metode transfer learning pada arsitektur Convolutional Neural Network (CNN) guna mendukung identifikasi otomatis. Keunggulan CNN terletak pada kemampuannya yang tidak memerlukan ekstraksi fitur karena fitur ekstraksi sudah terintegrasi secara otomatis dalam CNN. Studi ini melakukan perbandingan antara dua arsitektur CNN pada tiga jenis gandum yang berbeda. Hasil analisis menggunakan 150 citra data latih dan 45 citra data uji menunjukkan bahwa arsitektur MobileNet mampu memodelkan dataset dengan tingkat akurasi mencapai 98%, sementara tingkat kesalahan mencapai 0,02%.
Pendampingan Ibu Balita Melalui Model Edukasi Gizi Berbasis Aplikasi Digital untuk Pencegahan Stunting di Kabupaten Brebes Sandi A., Arif Setia; Deny Nugroho Triwibowo; Azhar Bashir; Sony Kartika Wibisono; Fitri Ayuningtyas; Iis Setiawan Mangku Negara
Jurnal Pengabdian Pada Masyarakat METHABDI Vol 5 No 2 (2025): Jurnal Pengabdian Pada Masyarakat METHABDI
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/methabdi.Vol5No2.pp266-271

Abstract

Stunting is a chronic nutritional problem requiring sustainable intervention, particularly in priority areas such as Brebes Regency. Conventional nutritional interventions often lack sustainability in monitoring child growth and development. This program aims to evaluate the effectiveness of the Digital Application-Based Nutrition Education Model (Si Penting)—a collaboration between Universitas Harapan Bangsa and Universitas Muhammadiyah Brebes—in strengthening caregiver capacity. The activity was conducted in Bantarkawung Sub-district, involving 45 caregivers of toddlers aged 6–24 months. A one-group pre-test post-test design was employed. The intervention consisted of face-to-face nutrition education followed by training and mentoring on the Si Penting Digital Application for two weeks. This program emphasized intensive mentoring for caregivers to operate the application for independent monitoring. Results showed a significant increase in participants' knowledge and attitudes (p<0.05), followed by proactive behavioral changes in monitoring child growth. The intervention model was successful, as shown by a 95.6% adoption rate for the application. It is concluded that the Digital Application-Based Nutrition Education Model effectively helped caregivers improve their ability to monitor nutrition on their own and is suggested to be used alongside Posyandu services to create lasting benefits in lowering stunting risks.
Understanding the Role of Artificial Intelligence in Community and Home Nursing Care: A Systematic Literature Review Sony Kartika Wibisono; Oktavia Putri Handayani; Burhanuddin bin Mohd Aboobaider
Viva Medika Vol 18 No 3 (2025)
Publisher : LPPM Universitas Harapan Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35960/vm.v18i3.2225

Abstract

Community and home nursing care are increasingly central to health systems in response to population ageing, rising chronic disease burden, and the need to reduce avoidable hospital utilization. Artificial intelligence (AI) has emerged as a technological innovation with potential to support nursing practice in non-hospital settings. However, the role and implications of AI within community and home nursing care have not been systematically synthesized. This systematic literature review aimed to examine how AI supports community and home nursing practice, identify the types of AI technologies applied, and analyze their reported outcomes and implications for nursing care. The review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. A total of 237 records were identified through electronic database searches. After duplicate removal and screening, 38 full-text articles were assessed for eligibility, and 15 studies were included in the final qualitative synthesis. The included studies, published between 2024 and 2026, encompassed diverse methodological designs and were conducted in community-based, home health, telemonitoring, and mobile nursing contexts. The findings indicate that AI technologies primarily include machine learning–based predictive models, clinical decision support systems, telemonitoring platforms, digital wound assessment tools, and large language model–supported analytics are used to enhance risk prediction, remote monitoring, chronic disease management, and care coordination. Across studies, AI was associated with improved early detection of clinical deterioration, enhanced workflow efficiency, and potential reductions in hospital admissions. Nevertheless, effective implementation depended on nurse engagement, system usability, digital literacy, and organizational support. AI demonstrates substantial potential to strengthen community and home nursing care when integrated within a human-centered and ethically grounded framework that preserves professional nursing judgment.
Co-Authors Arif Setia Sandi A. Azhar Bashir Bala Putra Dewa Burhanuddin bin Mohd Aboobaider Deny Nugroho Triwibowo Fitri Ayuningtyas Iis Setiawan Mangku Negara Khoirun Nisa Oktavia Putri Handayani Purwono, Purwono Rian Ardianto