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All Journal Indonesian Journal of Electrical Engineering and Computer Science International Journal of Engineering, Science and Information Technology
Indrajani Sutedja
Bina Nusantara University
Astronomy Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Social Sciences Transportation

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Blockchain-based key-value store to support dynamic smart contract interaction in the agricultural sector Irwansyah Saputra; Yandra Arkeman; Indra Jaya; Irman Hermadi; Indrajani Sutedja
Indonesian Journal of Electrical Engineering and Computer Science Vol 33, No 1: January 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v33.i1.pp622-633

Abstract

In the era of supply chain digitalization, adaptability and transparency are key to enhancing efficiency and trust. Although blockchain technology and smart contract offer innovative solutions, the limitations of static smart contract hinder their full potential. This article introduces a new approach using dynamic smart contract capable of managing various commodities in the supply chain with a key-value store. While this advantage provides flexibility, it still poses challenges in managing increasingly complex interactions among various actors, especially when the number of commodities increases. To address these challenges, this study introduces the concept of smart contract interaction that facilitates the automation and management of interactions with high efficiency. The implementation results show that smart contract interaction outperforms conventional approaches in terms of speed, resilience, and ease of management. Through the combination of dynamic smart contract and smart contract interaction, demonstrating how efficiency, transparency, adaptability, and scalability can be achieved in the supply chain, providing new insights into the utilization of blockchain technology for the modern industry.
Utilization of Machine Learning for Stunting Prediction: Case Study and Implications for Pre-Matrical and Pre-Conceptive Midwifery Services Qurotul Aini; Untung Rahardja; Indrajani Sutedja; Harco Leslie Hendric Spits Warnar; Nanda Septiani
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

Abstract

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 Untung Rahardja; Kristoko Dwi Hartomo; Indrajani Sutedja; Ardi Kho; Muhammad Farhan Kamil
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

Abstract

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.
Co-Authors Ardi Kho Harco Leslie Hendric Spits Warnar Indra Jaya Irman Hermadi Irwansyah Saputra Kristoko Dwi Hartomo Muhammad Farhan Kamil Nanda Septiani Qurotul Aini Untung Rahardja Yandra Arkeman