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All Journal Journal of Computer Networks, Architecture and High Performance Computing International Journal of Healthcare and Information Technology Proceeding International Conference Of Innovation Science, Technology, Education, Children And Health Proceeding International Conference Of Innovation Science, Technology, Education, Children And Health
Salihati Hanifa
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Digital Transformation of Health Services in Indonesia Through the Utilization of Artificial Intelligence, Big Data, and Telemedicine: Systematic Literature Review-VOSviewer Salihati Hanifa; Kurniawan Erman Wicaksono
Proceeding International Conference Of Innovation Science, Technology, Education, Children And Health Vol. 5 No. 1 (2025): Proceeding of The International Conference of Inovation, Science, Technology, E
Publisher : Program Studi DIII Rekam Medis dan Informasi Kesehatan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/icistech.v5i1.270

Abstract

Indonesia's healthcare system continues to face significant challenges in delivering equitable services across diverse and remote regions. The digital transformation of healthcare—through the integration of Artificial Intelligence (AI), big data, and telemedicine—offers promising solutions to overcome disparities in access, infrastructure, and service delivery. This study aims to comprehensively analyze global and national research trends related to the digital transformation of healthcare using a Systematic Literature Review (SLR) approach, supported by bibliometric analysis through the VOSviewer software. Following the PRISMA protocol, a total of 30 relevant articles published between 2020 and 2025 were identified and analyzed. The network and overlay visualizations generated reveal four major thematic clusters: digital transformation and service quality, big data and pandemic response, AI and data privacy, and community engagement in digital health services. Overlay visualization also shows a clear shift in research focus—from early pandemic responses toward system optimization, ethical governance, and technological inclusivity in recent years. The study concludes that the integration of AI, big data, and telemedicine not only enhances healthcare efficiency but also requires strong regulatory frameworks, infrastructure readiness, and public engagement. Future research should incorporate co-citation and cross-country comparative analyses to enrich the understanding of digital health transformation in a global context.
Determinants of Health Data Utilization by Posyandu Cadres for Toddlers as a Stunting Prevention Effort in Geneng Subdistrict, Ngawi Regency Kurniawan Erman Wicaksono; Sena Wahyu Purwanza; Ida Nurmawati; Salihati Hanifa; Ika Arum Dewi Satiti
Proceeding International Conference Of Innovation Science, Technology, Education, Children And Health Vol. 5 No. 1 (2025): Proceeding of The International Conference of Inovation, Science, Technology, E
Publisher : Program Studi DIII Rekam Medis dan Informasi Kesehatan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/icistech.v5i1.272

Abstract

Stunting remains one of the major public health issues at the national level in Indonesia. As an archipelagic country, Indonesia faces unique challenges in tackling stunting, particularly in regions with limited access to healthcare services. Advances in information technology offer new opportunities to support stunting prevention efforts, including through the utilization of health data. Such data can be used to detect stunting risks early and to monitor children's nutritional status more effectively. The use of health data applications or systems by Posyandu cadres is influenced by various factors, including availability of time, cost, level of trust, and perceptions of ease of use and usefulness. This study aims to identify the determinants of health data utilization by Posyandu cadres for toddlers as a stunting prevention effort in Geneng Subdistrict, Ngawi Regency. This research is an analytical quantitative study with a cross-sectional approach. A sample of 80 Posyandu cadres for toddlers in Geneng Subdistrict was selected using purposive sampling. Data were collected through questionnaires and analyzed using univariate, bivariate, and multivariate logistic regression tests to identify the factors influencing the use of health data in stunting prevention. The results show that the significant determinants include the age of the Posyandu cadre, their education level, and the amount of time they dedicate to Posyandu activities. The determinants of age, education level, and time significantly influence the utilization of health data and thereby affect the optimization of stunting prevention. Strengthening cadre capacity in these aspects is necessary to support more effective stunting prevention.
Digital Transformation of Health Services in Indonesia Through the Utilization of Artificial Intelligence, Big Data, and Telemedicine: Systematic Literature Review-VOSviewer Salihati Hanifa; Kurniawan Erman Wicaksono
Proceeding International Conference Of Innovation Science, Technology, Education, Children And Health Vol. 5 No. 1 (2025): Proceeding of The International Conference of Inovation, Science, Technology, E
Publisher : Program Studi DIII Rekam Medis dan Informasi Kesehatan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/icistech.v5i1.270

Abstract

Indonesia's healthcare system continues to face significant challenges in delivering equitable services across diverse and remote regions. The digital transformation of healthcare—through the integration of Artificial Intelligence (AI), big data, and telemedicine—offers promising solutions to overcome disparities in access, infrastructure, and service delivery. This study aims to comprehensively analyze global and national research trends related to the digital transformation of healthcare using a Systematic Literature Review (SLR) approach, supported by bibliometric analysis through the VOSviewer software. Following the PRISMA protocol, a total of 30 relevant articles published between 2020 and 2025 were identified and analyzed. The network and overlay visualizations generated reveal four major thematic clusters: digital transformation and service quality, big data and pandemic response, AI and data privacy, and community engagement in digital health services. Overlay visualization also shows a clear shift in research focus—from early pandemic responses toward system optimization, ethical governance, and technological inclusivity in recent years. The study concludes that the integration of AI, big data, and telemedicine not only enhances healthcare efficiency but also requires strong regulatory frameworks, infrastructure readiness, and public engagement. Future research should incorporate co-citation and cross-country comparative analyses to enrich the understanding of digital health transformation in a global context.
Determinants of Health Data Utilization by Posyandu Cadres for Toddlers as a Stunting Prevention Effort in Geneng Subdistrict, Ngawi Regency Kurniawan Erman Wicaksono; Sena Wahyu Purwanza; Ida Nurmawati; Salihati Hanifa; Ika Arum Dewi Satiti
Proceeding International Conference Of Innovation Science, Technology, Education, Children And Health Vol. 5 No. 1 (2025): Proceeding of The International Conference of Inovation, Science, Technology, E
Publisher : Program Studi DIII Rekam Medis dan Informasi Kesehatan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/icistech.v5i1.272

Abstract

Stunting remains one of the major public health issues at the national level in Indonesia. As an archipelagic country, Indonesia faces unique challenges in tackling stunting, particularly in regions with limited access to healthcare services. Advances in information technology offer new opportunities to support stunting prevention efforts, including through the utilization of health data. Such data can be used to detect stunting risks early and to monitor children's nutritional status more effectively. The use of health data applications or systems by Posyandu cadres is influenced by various factors, including availability of time, cost, level of trust, and perceptions of ease of use and usefulness. This study aims to identify the determinants of health data utilization by Posyandu cadres for toddlers as a stunting prevention effort in Geneng Subdistrict, Ngawi Regency. This research is an analytical quantitative study with a cross-sectional approach. A sample of 80 Posyandu cadres for toddlers in Geneng Subdistrict was selected using purposive sampling. Data were collected through questionnaires and analyzed using univariate, bivariate, and multivariate logistic regression tests to identify the factors influencing the use of health data in stunting prevention. The results show that the significant determinants include the age of the Posyandu cadre, their education level, and the amount of time they dedicate to Posyandu activities. The determinants of age, education level, and time significantly influence the utilization of health data and thereby affect the optimization of stunting prevention. Strengthening cadre capacity in these aspects is necessary to support more effective stunting prevention.
Digital Transformation of Health Services in Indonesia through the Utilization of Artificial Intelligence, Big Data, and Telemedicine: Systematic Literature Review-VOSviewer Salihati Hanifa; Kurniawan Erman Wicaksono
Proceeding International Conference Of Innovation Science, Technology, Education, Children And Health Vol. 5 No. 1 (2025): Proceeding of The International Conference of Inovation, Science, Technology, E
Publisher : Program Studi DIII Rekam Medis dan Informasi Kesehatan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/icistech.v5i1.280

Abstract

Indonesia's healthcare system continues to face significant challenges in delivering equitable services across diverse and remote regions. The digital transformation of healthcare—through the integration of Artificial Intelligence (AI), big data, and telemedicine—offers promising solutions to overcome disparities in access, infrastructure, and service delivery. This study aims to comprehensively analyze global and national research trends related to the digital transformation of healthcare using a Systematic Literature Review (SLR) approach, supported by bibliometric analysis through the VOSviewer software. Following the PRISMA protocol, a total of 30 relevant articles published between 2020 and 2025 were identified and analyzed. The network and overlay visualizations generated reveal four major thematic clusters: digital transformation and service quality, big data and pandemic response, AI and data privacy, and community engagement in digital health services. Overlay visualization also shows a clear shift in research focus—from early pandemic responses toward system optimization, ethical governance, and technological inclusivity in recent years. The findings highlight that digital healthcare transformation has increasingly evolved from emergency responses to COVID-19 into a strategic framework for long-term system improvement. Moreover, AI and big data have played pivotal roles in enhancing diagnostics, predicting outbreaks, and improving resource allocation. However, concerns related to privacy, digital literacy, and unequal technological access remain prominent. The study concludes that the integration of AI, big data, and telemedicine not only enhances healthcare efficiency but also requires strong regulatory frameworks, infrastructure readiness, and public engagement. Future research should incorporate co-citation and cross-country comparative analyses to enrich the understanding of digital health transformation in a global context, especially in low- and middle-income countries like Indonesia.
Digital Transformation of Health Services in Indonesia through the Utilization of Artificial Intelligence, Big Data, and Telemedicine: Systematic Literature Review-VOSviewer Salihati Hanifa; Kurniawan Erman Wicaksono
Proceeding International Conference Of Innovation Science, Technology, Education, Children And Health Vol. 5 No. 1 (2025): Proceeding of The International Conference of Inovation, Science, Technology, E
Publisher : Program Studi DIII Rekam Medis dan Informasi Kesehatan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/icistech.v5i1.280

Abstract

Indonesia's healthcare system continues to face significant challenges in delivering equitable services across diverse and remote regions. The digital transformation of healthcare—through the integration of Artificial Intelligence (AI), big data, and telemedicine—offers promising solutions to overcome disparities in access, infrastructure, and service delivery. This study aims to comprehensively analyze global and national research trends related to the digital transformation of healthcare using a Systematic Literature Review (SLR) approach, supported by bibliometric analysis through the VOSviewer software. Following the PRISMA protocol, a total of 30 relevant articles published between 2020 and 2025 were identified and analyzed. The network and overlay visualizations generated reveal four major thematic clusters: digital transformation and service quality, big data and pandemic response, AI and data privacy, and community engagement in digital health services. Overlay visualization also shows a clear shift in research focus—from early pandemic responses toward system optimization, ethical governance, and technological inclusivity in recent years. The findings highlight that digital healthcare transformation has increasingly evolved from emergency responses to COVID-19 into a strategic framework for long-term system improvement. Moreover, AI and big data have played pivotal roles in enhancing diagnostics, predicting outbreaks, and improving resource allocation. However, concerns related to privacy, digital literacy, and unequal technological access remain prominent. The study concludes that the integration of AI, big data, and telemedicine not only enhances healthcare efficiency but also requires strong regulatory frameworks, infrastructure readiness, and public engagement. Future research should incorporate co-citation and cross-country comparative analyses to enrich the understanding of digital health transformation in a global context, especially in low- and middle-income countries like Indonesia.
Integrating AHP and TBATS for Infectious Disease Prioritization and Forecasting in East Java Supriyanto, Budi Fajar; Salihati Hanifa; Nesa Ayu Murthisari Putri; Titin Andriyani Atmojo; Waridad Umais Al Ayyubi
Journal of Computer Networks, Architecture and High Performance Computing Vol. 7 No. 4 (2025): Articles Research October 2025
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v7i4.7151

Abstract

Agrarian regions like East Java province face complex public health challenges. Some cases are caused by the interaction between social factors, and others by agribusiness factors. An integrative approach is needed to understand the dynamics of disease cases. This study aims to analyse the disease with the highest number of cases and project case trends in East Java using an integrated quantitative approach. Using methods such as the Analytic Hierarchy Process (AHP) to determine disease weights, the TBATS model is used to project case trends through 2028. Standardised multiple regression models were used to assess the influence of social factors (population density, poverty) and agribusiness (rice harvest area, agricultural labour). The data used are secondary time-series data from 2013 to 2023 obtained from BPS, the Health Department, and BMKG. The AHP results show diarrhoea as the disease with the highest weight (0.494), followed by pneumonia (0.112), tuberculosis (0.090), malaria (0.051), and dengue fever (0.049). The TBATS projection indicates medium-term fluctuations with the potential for an increase in dengue fever cases. Meanwhile, the regression results show that people in the agricultural sector are at increased risk of malaria (p = 0.037), while other variables have an influence but are not significant. Therefore, integrating health, social, and agribusiness data is an urgent need. And it can be used for early disease warning systems and more precise public health policy strengthening.
GeoAI for Precision Public Health in Agrarian Economies: Multi-Disease Risk Profiling in Rice Belt in East Java Budi Fajar Supriyanto; Salihati Hanifa; Nesa Ayu Murthisari Putri; Titin Andriyani Atmojo; Waridad Umais Al Ayyubi
International Journal of Healthcare and Information Technology Vol. 3 No. 2 (2026): January
Publisher : P3M Politeknik Negeri Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25047/ijhitech.v3i2.6646

Abstract

Public health and food security, particularly in the agribusiness sector, are interconnected. As one of the largest rice-producing provinces in Indonesia, East Java faces numerous infectious diseases. To develop a spatial typology of health-agribusiness risks, this study combines epidemiology, agribusiness, and computer science with a Geospatial Artificial Intelligence (GeoAI) approach.The data includes cases of ten infectious diseases (2015–2024), rice harvested area, number of farmers, and district/city population in East Java. Cases were normalized per 100,000 population, and agribusiness indicators were converted to harvested area per farmer ratios. The analysis used internal validation (silhouette score, Davies–Bouldin Index), K-Means clustering, and spatial validation (Moran's I). Results are displayed on OpenStreetMap.Agribusiness can be divided into three main typologies: (1) strong agribusiness with moderate risk; (2) multisector agribusiness with high risk and moderate agribusiness; and (3) moderate agribusiness with a prevalence of lung disease and diarrhea. Moran's I = -0.0263 (p=0.5678), indicating that spatial distribution is not significant. The results suggest that public health does not always correlate with food production intensity. By integrating epidemiology, agribusiness, and GeoAI to support appropriate public health in agricultural areas, this study adds to the international literature.
Co-Authors Al Ayyubi, Waridad Umais Budi Fajar Supriyanto Ida Nurmawati Nesa Ayu Murthisari Putri Satiti, Ika Arum Dewi Sena Wahyu Purwanza, Sena Wahyu Supriyanto, Budi Fajar Titin Andriyani Atmojo Wicaksono, Kurniawan Erman