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AniraBlock: A leap towards dynamic smart contracts in agriculture using blockchain based key-value format framework Saputra, Irwansyah; Arkeman, Yandra; Jaya, Indra; Hermadi, Irman; Akbar, Nur Arifin; Sutedja, Indrajani
Communications in Science and Technology Vol 8 No 2 (2023)
Publisher : Komunitas Ilmuwan dan Profesional Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21924/cst.8.2.2023.1240

Blockchain technology offers data transparency and traceability, which is particularly useful in the agricultural sector, especially within the supply chains of commodities like coffee and fish. This sector often encounters issues such as quality degradation, unclear information, and socioeconomic injustice affecting stakeholders. The implementation of Static Smart Contracts (SSCs) on blockchains provides a structured method for executing agreements. However, this approach also has limitations, including a lack of flexibility and responsiveness to dynamic changes in the supply chain. Despite these challenges, blockchain remains a valuable tool for ensuring transaction transparency, traceability, and integrity, which are vital in agriculture. These limitations involve unchangeable parameters, rigid rules, and constraints on adaptability and scalability. This study aims to tackle these issues by designing a more dynamic and responsive smart contract system. We introduce AniraBlock, a revolutionary concept for the agricultural supply chain, particularly in the coffee and fish sectors, by implementing Dynamic Smart Contracts (DSCs) based on a key-value format framework. Unlike SSCs, DSCs offer enhanced adaptability and scalability, addressing the former's limitations. Our study adopts a mixed-method approach, utilizing both qualitative and quantitative data to validate AniraBlock's effectiveness. Preliminary results show significant improvements in data management and supply chain transparency. The proposed framework has the potential to influence the agricultural sector by boosting data integrity and operational efficiency.

Health Center Innovation: Using Ai To Prevent Heart Disease Musawaris, Rubil; Sutedja, Indrajani
COMSERVA : Jurnal Penelitian dan Pengabdian Masyarakat Vol. 5 No. 2 (2025): COMSERVA: Jurnal Penelitian dan Pengabdian Masyarakat
Publisher : Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59141/comserva.v5i2.3235

Throughout the world, the disease most people suffer from is heart disease. Likewise with Indonesia, many Indonesian people have heart disease. The cause of the high death rate from heart disease is still a lack of experts who can treat heart disease well. Apart from that, there is also a low level of public awareness to carry out regular checks on the development of their heart health. Because heart disease is still one of the main causes of death throughout the world and Indonesia, this is a topic to increase public awareness and explore the role of artificial intelligence (AI) in the prevention and early detection of heart disease. We explore what AI can do to prevent and cure heart disease, AI works by performing deep machine learning, and neural network learning and how it can be applied to analyze large data sets to identify risk factors, predict outcomes, and support decisions. clinical decisions. The focus is to leverage AI and improve early predictions for the public, personally providing patient care. In this article, readers can learn about the benefits and limitations of AI in the context of heart disease and emphasize the need for high-quality data in order to obtain appropriate analysis results. Finally, this paper suggests that other researchers carry out further research, such as improving the interpretability of AI models, expanding and searching for multiple data sources, and encouraging collaboration between government, society and medical personnel. The potential of using AI can reduce people's risk of heart disease and AI offers a path to better public health outcomes and more efficient health services to treat it and detect it early.

Using Deep Learning and Cbir To Address Copyright Concerns of AI-Generated Art: A Systematic Literature Review Vivaldi, William; Sutedja, Indrajani
Devotion : Journal of Research and Community Service Vol. 5 No. 10 (2024): Devotion: Journal of Community Research
Publisher : Green Publisher Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59188/devotion.v5i10.18642

This systematic literature review explores the intersection of deep learning and content-based image retrieval (CBIR) in addressing copyright concerns related to AI-generated art. As artificial intelligence rapidly transforms various artistic domains, it raises critical questions regarding authorship, ownership, and the ethical implications of machine-generated creativity. The review examines the capabilities of CBIR systems in identifying AI-generated images by analyzing visual features such as color, texture, and shape. Additionally, it highlights the role of deep learning models in enhancing the accuracy of these systems through the detection of distinctive patterns characteristic of AI artworks. The findings underscore the importance of developing robust methodologies that leverage AI and CBIR technologies to protect intellectual property rights while fostering innovation in the creative industries. This research contributes to the broader discourse on the legal and ethical challenges posed by AI in art, providing insights for policymakers, artists, and technologists in navigating the evolving landscape of AI-generated content.

Machine Learning-based Prediction Model for Adverse Pregnancy Outcomes: A Systematic Literature Review Abdurrahman, Eka Santy; Siregar, Kemal N.; Rikawarastuti; Sutedja, Indrajani; Nasir, Narila Mutia
JURNAL INFO KESEHATAN Vol 22 No 3 (2024): JURNAL INFO KESEHATAN
Publisher : Research and Community Service Unit, Poltekkes Kemenkes Kupang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31965/infokes.Vol22.Iss3.1486

Most of Adverse Pregnancy Outcomes (APO) are preventable particularly if the health personnel can early detect the risk. This study aimed to review articles on how the machine learning model can predict APO for early detection to prevent neonatal mortality. We conducted a systematic literature review by analyzing seven articles which published between 1 January 2013 and 31 October 2022. The search strategy was the populations are pregnant women, intervention using machine learning for APO prediction, and the outcomes of APO are Low Birth Weight, preterm birth, and stillbirth. We found that the predictors of LBW were demographic, maternal, environmental, fetus characteristics, and obstetric factors. The predictors of preterm birth were demographics and lifestyle. Meanwhile, the predictors of stillbirth were demographic, lifestyle, maternal, obstetric, and fetus characteristics. It was indicated that Random Forest (Accuracy: 91.60; AUC-ROC: 96.80), Extreme Gradient Boosting (Accuracy: 90.80; AUC-ROC: 95.90), logistic regression (accuracy 90.24% and precision 87.6%) can be used to predict the risk of APO. By using a machine learning algorithm, the best APO prediction models that can be used are logistic regression, random forest, and extreme gradient boosting with sensitivity values and AUC of almost 100%. Demographic factors are the main risk factors for APO.

Health Center Innovation: Using Ai To Prevent Heart Disease Musawaris, Rubil; Sutedja, Indrajani
COMSERVA : Jurnal Penelitian dan Pengabdian Masyarakat Vol. 5 No. 2 (2025): COMSERVA: Jurnal Penelitian dan Pengabdian Masyarakat
Publisher : Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59141/comserva.v5i2.3235

Throughout the world, the disease most people suffer from is heart disease. Likewise with Indonesia, many Indonesian people have heart disease. The cause of the high death rate from heart disease is still a lack of experts who can treat heart disease well. Apart from that, there is also a low level of public awareness to carry out regular checks on the development of their heart health. Because heart disease is still one of the main causes of death throughout the world and Indonesia, this is a topic to increase public awareness and explore the role of artificial intelligence (AI) in the prevention and early detection of heart disease. We explore what AI can do to prevent and cure heart disease, AI works by performing deep machine learning, and neural network learning and how it can be applied to analyze large data sets to identify risk factors, predict outcomes, and support decisions. clinical decisions. The focus is to leverage AI and improve early predictions for the public, personally providing patient care. In this article, readers can learn about the benefits and limitations of AI in the context of heart disease and emphasize the need for high-quality data in order to obtain appropriate analysis results. Finally, this paper suggests that other researchers carry out further research, such as improving the interpretability of AI models, expanding and searching for multiple data sources, and encouraging collaboration between government, society and medical personnel. The potential of using AI can reduce people's risk of heart disease and AI offers a path to better public health outcomes and more efficient health services to treat it and detect it early.

Utilization of Machine Learning for Stunting Prediction: Case Study and Implications for Pre-Matrical and Pre-Conceptive Midwifery Services Aini, Qurotul; Rahardja, Untung; Sutedja, Indrajani; Spits Warnar, Harco Leslie Hendric; Septiani, Nanda
International Journal of Engineering, Science and Information Technology Vol 5, No 1 (2025)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v5i1.1488

Stunting, a global health challenge, affects millions of children, particularly in low- and middle-income countries, and has lasting consequences on cognitive development, physical growth, and overall well-being. Early prediction and intervention are crucial for reducing stunting, especially before conception and during early pregnancy. This paper explores the utilisation of machine learning (ML) for predicting stunting risk in the context of pre-maternal and pre-conceptive midwifery services. By analysing a case study, the research assesses the effectiveness of various machine learning algorithms in identifying stunting risk factors, including maternal health, nutrition, socioeconomic status, and environmental conditions. Using healthcare and demographic data, the study develops predictive models to assist midwives in assessing stunting risks during pre-conception and prenatal phases. The findings demonstrate that ML models, particularly random forest and support vector machine algorithms, outperform traditional risk assessment methods, providing higher accuracy and earlier detection of stunting risk. These models enable midwives to deliver personalised care and targeted interventions, optimising maternal and child health outcomes. The study also highlights the broader implications of integrating machine learning into midwifery services, including improved decision-making, resource allocation, and healthcare efficiency. In conclusion, this research underscores the transformative potential of machine learning in predicting stunting risk and enhancing the effectiveness of pre-maternal and pre-conceptive midwifery services, offering a promising approach to mitigating the global burden of stunting.

Machine Learning-Based Heart Failure Worsening Prediction Model to Build Self-Monitoring Prototype as an Effort to Prevent Readmissions and Maintain Quality of Life Rahardja, Untung; Hartomo, Kristoko Dwi; Sutedja, Indrajani; Kho, Ardi; Kamil, Muhammad Farhan
International Journal of Engineering, Science and Information Technology Vol 5, No 1 (2025)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v5i1.1467

Heart failure is a long-term condition of great concern which calls for health care services in cycles. This significantly hampers quality of life for patients and increases costs for the healthcare systems. If the worsening of heart failure could be detected early, the intervention to prevent readmission could be employed, such that readmission would be avoided, enhancing the quality of life for the patient. Accordingly, the paper explains how such a model to predict the worsening of heart failure in patients who are at high risk of this condition has been developed. The model uses information gathered from the Electronic Health Records (EHRs) (Clinical Variables, Vitals, Test Results, and Demographics) to make accurate predictions on patients. As an effective and efficient approach towards achieving this goal, comparison of different algorithms such as random forests, support vector machines and gradient boosting has been employed towards the building of the final model. At this stage, the model is embedded into a user-friendly self-monitoring device, allowing the chronic heart failure patients to assess health indices on the fly with the help of the mobile app and wearable devices. This secondary prevention strategy makes patients more responsible for their health and decreases the number of patients readmitted to the hospital by increasing their functioning and well-being. The paper further projects the future development of other forms of treatment for chronic heart failure, especially at the first line, focusing primarily on the timing and succession.

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