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All Journal JST ( Jurnal Sains Terapan ) Jurnal Sistem Teknik Industri Journal of Scientific Insights Jurnal Teknologi dan Sains Modern Media Elektrik INTENS Jurnal Inovasi Sains Teknologi dan Bisnis
Fajar, Nurhikmah
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Agriculture, Biological Sciences & Forestry Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Engineering Industrial & Manufacturing Engineering 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.
Smart Sensors and Intelligent Analysis: A Literature Review on More Effective Early Warning Systems with IoT and Machine Learning Mustamin, Syaiful Bachri; Atnang , Muhammad; Sahriani , Sahriani; Fajar, Nurhikmah; Sari, Sri Kurnian; Pahlawan , Muammar Reza; Amrullah, Mujahidin
Journal of Scientific Insights Vol. 1 No. 4 (2024): December
Publisher : Science Tech Group

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69930/jsi.v1i4.182

Abstract

The IoT system described in the article "LoRaWAN-Based IoT System Implementation for Long-Range Outdoor Air Quality Monitoring" monitors air quality in real-time and transmits data through a LoRaWAN network to a public IoT platform. It measures seven key air quality parameters: nitrogen dioxide (NO₂), sulfur dioxide (SO₂), carbon dioxide (CO₂), carbon monoxide (CO), PM2.5, temperature, and humidity. These parameters were chosen for their significant effects on air quality and human health. NO₂ and SO₂ come from fossil fuel combustion and can cause respiratory issues and acid rain. CO₂ contributes to climate change, while CO is toxic and harmful to health. PM2.5 particles can lead to respiratory and cardiovascular problems. The system uses sensors connected to an Arduino microcontroller to collect data, which is transmitted through a LoRa Shield to a LoRaWAN gateway. Data is then sent to The Things Network (TTN), integrated with ThingSpeak, and displayed on a web dashboard. Additionally, it is synchronized with the Virtuino smartphone app for mobile monitoring. The system has been validated by comparing its data to Aeroqual air quality monitors, demonstrating reliable real-time monitoring and transmission of air quality information over the internet.
The Application of Machine Learning and Intelligent Sensors for Real-Time Air Quality Monitoring: A Literature Review Mustamin, Syaiful Bachri; Atnang, Muhammad; Sahriani, Sahriani; Fajar, Nurhikmah; Sari, Sri Kurnian; Pahlawan , Muammar Reza; Amrullah, Mujahidin
Journal of Scientific Insights Vol. 1 No. 3 (2024): October
Publisher : Science Tech Group

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69930/jsi.v1i3.183

Abstract

Air pollution is a global issue that has major consequences for human health and the environment. Accurate air quality prediction plays an important role in mitigating and preventing the negative impacts of air pollution. The thirteen sources analyzed in this literature study show a growing trend in the use of machine learning for air quality prediction, driven by the limitations of traditional methods and machine learning capabilities in efficiently processing complex data. This literature study examines a variety of commonly used machine learning models, such as Support Vector Regression (SVR), Random Forest, Gradient Boosting, and Long Short-Term Memory (LSTM), and evaluates their performance based on metrics such as RMSE, MAE, and R². The sources also highlight the importance of understanding the factors that affect air quality, including concentrations of various pollutants (PM2.5, PM10, NO2, CO, SO2, and ozone), meteorological data (temperature, humidity, wind speed, air pressure, precipitation, and temperature inversion), traffic data, and spatial-temporal variations. The integration of the Internet of Things (IoT) and machine learning is the main focus in the development of real-time air quality monitoring systems. IoT sensors enable the collection of real-time air quality and meteorological data, which are then processed using machine learning models to generate predictions. This literature study identifies several challenges in air quality prediction, such as data limitations, the complexity of air pollution dynamics, and ethical & privacy considerations. However, machine learning offers great potential to improve the accuracy of air quality predictions and monitoring, thus contributing to a healthier and more sustainable environment.
Co-Authors Akhyar Muchtar, Akhyar Amrullah, Mujahidin Andi Fitriati Anggriani, Dwi Atnang , Muhammad Atnang, Muhammad Bakhtiar, Sri Muslihah Bohari, Nurul Aulia Eka Purnama Sari, Eka Purnama Fachry Abda El Rahman Kiki Konasara, Zakiyah Muhammad Arif Wibisono, Muhammad Arif Mustamin, Syaiful Bachri Nur Azaliah MAR, Nur Azaliah Nur Azhary Iriawan Eka Putra Nurul Hidayatullah, Nurul Pahlawan , Muammar Reza Ramli, Agus Salim Sahriani Sahriani , Sahriani Sahriani, Sahriani Samsidar Sari, Sri Kurnian Siti Fatmah, Siti Surahmawanti, Mita Yun Angraeni Saputri