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All Journal PEDAMAS (Pengabdian Kepada Masyarakat) Journal of Scientific Insights Jurnal Teknologi dan Sains Modern Issues in Mathematics Educations (IMED)
Sahriani
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Agriculture, Biological Sciences & Forestry Humanities Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Energy Engineering Health Professions Law, Crime, Criminology & Criminal Justice Mathematics Public Health Social Sciences Other

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Journal : Journal of Scientific Insights

 1 
Transforming the Diabetes Mellitus Diagnosis and Treatment Using Data Technology: Comprehensive Analysis of Deep Learning and Machine Learning Methodologies Anggriani, Dwi; Mustamin, Syaiful Bachri; Sahriani; Atnang, Muhammad; Fatmah, Siti; Mar, Nur Azaliah; Fajar, Nurhikmah
Journal of Scientific Insights Vol. 1 No. 1 (2024): June
Publisher : Science Tech Group

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69930/jsi.v1i1.71

Abstract

Recent research in health data analysis has transformed our understanding, prediction, and management of diabetes mellitus. This review explores various approaches used in related studies to enhance understanding and management strategies of diabetes through data analysis. Various data analysis methods, including machine learning such as neural networks, Gaussian Process Classification (GPC), and deep learning, have been used to enhance illness management and forecast accuracy. One of the included studies created customised care plans and used data to forecast the likelihood of complications in diabetes.. Another focused on comparative approaches for diabetes diagnosis using artificial intelligence, while others explored disease classification techniques using GPC algorithms. On the other hand, some studies utilized deep learning to identify diverse trajectories of type 2 diabetes from routine medical records, while others developed wide and deep learning models to predict diabetes onset. This review notes that data analysis approaches have significantly advanced accuracy in diagnosis, predictive modeling, and disease management of diabetes. Integrating these technologies allows for more personalized treatment approaches, where patient data can tailor individualized care strategies. Study findings indicate that machine learning and deep learning applications not only enhance prediction accuracy but also unlock new potentials in identifying risk factors, managing complications, and preventing diseases. Thus, this review provides profound insights into how data analysis has shifted paradigms in diabetes management, extending beyond diagnosis and treatment to encompass prevention and long-term management of chronic diseases. These studies lay a robust foundation for further research in developing more sophisticated and effective approaches in health data analysis, ultimately aiming to enhance the overall quality of life for patients with diabetes.
Research Techniques for IoT Use, Wearable Technology, and Smart Sensors in Mental Well-Being: A Literature Review from Several Studies Sahriani; Surahmawanti, Mita; Samsidar; Fatmah, Siti; Mustamin, Syaiful Bachri; Atnang, Muhammad; Fajar, Nurhikmah; Mar, Nur Azaliah
Journal of Scientific Insights Vol. 1 No. 1 (2024): June
Publisher : Science Tech Group

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69930/jsi.v1i1.72

Abstract

This study reviews the literature on the application of technology to wearables, smart sensors, and the Internet of Things (IoT) in the monitoring and treatment of mental health. Several studies analyzed employ systematic review, experimental, and literature survey approaches to explore various aspects of technology implementation in the context of mental health. The studies adopt a systematic review design without involving specific samples or measurement tools but highlight the application of IoT in mental health monitoring. Meanwhile, other studies conduct systematic reviews encompassing 41 studies utilizing smart devices and wearable technology in mental health monitoring, yet without specifying the software used. Another research proposes an experimental design to test a wearable sensor-based machine learning stress monitoring system. On the other hand, there are literature survey reports on the use of wearable sensors in mental health monitoring without providing details of the reviewed study methodologies. Other studies explore the literature using a scoping review method to gather information on mental health technology, identifying 37 relevant scientific articles. This review emphasizes the need for rigorous methodological approaches to effectively understand and apply technology in mental health monitoring and intervention. Overall, this literature review highlights the importance of developing technology that can enhance mental health monitoring and intervention. The application of IoT, wearable devices, and smart sensors can be a potential solution but requires a multidisciplinary approach and meticulous methodology to optimize their use in clinical practice
A Review on Growth Factors in Digital Start-ups: Digital Marketing, Scaling, Adaptation, Advanced Tech Fatmah, Siti; Samsidar; Atnang, Muhammad; Mustamin, Syaiful Bachri; Sahriani; Mar, Nur Azaliah; Fajar, Nurhikmah
Journal of Scientific Insights Vol. 1 No. 1 (2024): June
Publisher : Science Tech Group

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69930/jsi.v1i1.73

Abstract

Understanding MRBS (Massive and Rapid Business Scaling) is critical in the context of digital start-ups as it helps maximize the use of limited office space, better manage time, and support effective collaboration. This study aims to explore the concept of MRBS in the context of digital start-ups and identify the factors that drive the phenomenon. The focus of this study is on the significant increase in MRBS driven by recent advances in digitization, despite only about 3% of start-ups ever reaching a market valuation of $1 billion (USD) or more. Using an inductive qualitative research approach through 53 semi-structured interviews with start-up founders, executives, and advisors, this study seeks to fill the gap in previous literature that has not comprehensively explored the drivers of MRBS in the context of digital start-ups. The findings of this study reveal seven core drivers that contribute to the MRBS process, namely access to capital, product innovation, technology adoption, competent team, marketing strategy, networks and partnerships, and scale of operations. In addition, this study also identified several areas of tension that arise in the MRBS process, such as pressure for rapid growth, risk of failure, and challenges in maintaining corporate culture. Other related literature studies also explored the potential impact of extended digital marketing and its influence on the growth of startups. This research develops a macrodynamic framework that describes the drivers of startup growth supported by digital marketing and analyzes the differences in the use of B2B and B2C digital marketing, as well as the impact of new technologies on digital marketing. The results of these two studies are expected to provide researchers and practitioners with valuable insights into the MRBS phenomenon and the potential of digital marketing in supporting startup growth. Thus, this research contributes to understanding how start-ups can achieve large and rapid business scale in today's digital era.
Technological Challenges and Opportunities in Telemedicine: Advancements and Barriers in the Pandemic Era Samsidar; Muhammad Atnang; Syaiful Bachri Mustamin; Sahriani
Journal of Scientific Insights Vol. 1 No. 2 (2024): August
Publisher : Science Tech Group

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69930/jsi.v1i2.180

Abstract

This study analyzes various aspects and challenges of telemedicine technology implementation based on findings from 10 related journals. The evolution of telemedicine has transformed healthcare services, becoming more efficient, especially during the COVID-19 pandemic. Telemedicine is applied in areas such as remote consultations, telemonitoring, teletherapy, and telepharmacy, with emerging technologies like AI, VR, and blockchain showing great potential for further enhancement. Key factors influencing the acceptance of telemedicine by healthcare professionals include perceived usefulness, attitude, compatibility, and ease of use. While telemedicine offers significant benefits, challenges such as privacy issues, regulatory barriers, and accessibility remain. Ethical and legal concerns, including the quality of care, data security, and continuity of services, are also highlighted as major issues. This study recommends understanding end-user needs and establishing better standards for future telemedicine implementation.
Co-Authors Anggriani, Dwi Asdah, Atikah Nurul Atnang, Muhammad Awi Eka Purnama Sari, Eka Purnama Fajar , Nurhikmah Fajar, Nurhikmah Hasbi, Nur Hikmah , Nur Juswan Kiki Konasara, Zakiyah Muammar Reza Pahlawan Muhammad Atnang Mustamin, Syaiful Bachri Mutia, Yesi Nurul Nur Azaliah MAR, Nur Azaliah Nurrahma Nurul Hidayatullah, Nurul Safitri, Nur Anita Syamsi Saktilawati, Wa Ode Samsidar Sari, Sri Kurniyan Siti Fatmah, Siti Surahmawanti, Mita Syaiful Bachri Mustamin Usman Mulbar