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Journal of Scientific Insights
Published by CV. Science Tech Group
ISSN : -     EISSN : 30628571     DOI : -
Core Subject : Health, Education, Social, Engineering,
Education Engineering Public Health Social Sciences Other
Journal of Scientific Insights (JSI) is an international, peer-reviewed, open-access journal dedicated to publishing high-quality research across a broad spectrum of disciplines. Emphasizing interdisciplinary collaboration, JSI welcomes original contributions that bridge science, engineering, technology, and other fields—such as health, education, social sciences, and economics—to address complex real-world problems. The journal particularly encourages work that applies innovative scientific and technological perspectives in support of the United Nations Sustainable Development Goals (SDGs).
Arjuna Subject : Teknik (semua kategori) - Teknik (Lain-Lain)
Articles 69 Documents
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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.
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.
Leveraging Public Transport Route Innovations as Technological Inspiration for Advancing Islamic Education Stiya Mulyani, Pamungkas; Syam , Robingun Suyud El
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.v1i3.194

Abstract

The problems and challenges that exist in Islamic education, both in learning and its application, are multidimensional, including the decline in children's innovative abilities due to the influence of digital products. This article aims to review unique writing on public transport routes: inspiration for innovation in Islamic education. This paper is set in qualitative library research, with inductive analysis. The results of the discussion show that the unique writing on public transport routes is intentional, the result of innovation from the owners of these vehicles. This fact can be used as inspiration for Islamic education, that innovative processes can be obtained from various sources and then contextualized into the learning process. Research implications for the direction of interdisciplinary research to build Islamic education based on contextual teaching and learning. The research hopes to contribute to Islamic education.  
The Impact of Job Equivalency Policies and Work Systems on Employee Performance in the Gorontalo City Government: An Information Systems Perspective Tangguda, Kharisma; Gobel , Lisda Van; Nggilu, Rukiah
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.v1i3.198

Abstract

The purpose of this study was to determine and analyze the influence of job equivalency policy on employee performance, the influence of work system on employee performance and the influence of job equivalency policy and work system simultaneously on employee performance at the Gorontalo City Government. This study used a survey research type with a quantitative approach, a sample of 73 respondents who were employees of the Gorontalo City Government, data analysis using multiple regression analysis with the help of the SPSS version 26 application. The results of the study showed that: (1) partially the job equivalency policy has a negative and significant effect on employee performance. If the job equivalency policy continues to be implemented, employee performance will continue to decline. (2) partially the work system has a positive and significant effect. a good work system continues to be improved, then employee performance will increase. (3) the job equalization policy has a positive and significant effect. the job equalization policy and work system are improved together, then performance will increase. Gorontalo City Government needs to re-evaluate the job equalization policy because it has a negative effect on improving employee performance. Evaluation in terms of human resource management according to competency and competency development through functional training for officials affected by the equalization. There is also a need to be capacity mapping and mapping of human resource needs. In addition, employees in the Gorontalo City Government environment need to continue to improve the work system so that they can improve employee performance.
Digitalized Regional Development Planning Model for North Gorontalo Regency: A Technology-Enhanced Approach to Participatory and Technocratic Planning Indah, Nurmala Suci; Rachman, Ellys; Gobel , Lisda Van
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.v1i3.199

Abstract

The objectives of this study are: (1) To find out and analyze the regional development planning model by the Regional Planning Research and Development Agency of North Gorontalo Regency seen from the technocratic planning model, participatory planning, and top-down and bottom- up planning, (2) To find out and analyze the inhibiting factors of the regional development planning model by the Regional Planning Research and Development Agency of North Gorontalo Regency. This study uses a qualitative approach with a descriptive research type. The data consists of primary data obtained from interviews with informants and observations and secondary data from document studies. The results of the study indicate that the regional development planning model of technocratic planning, namely regional development planning is formulated based on the results of analysis by competent regional apparatus and academics and has a function as an implementing team for the preparation of the RKPD, Participatory planning, namely BAPPPEDA coordinating with other agencies and presenting community leaders in accommodating community aspirations and proposals fairly in supporting regional development in North Gorontalo Regency, while top-down and bottom-up planning, namely planning through musrenbangda by synchronizing government work plans and program proposals from the community. Furthermore, the inhibiting factors of the planning model due to limited human resources that are not yet sufficient and inadequate, community program proposals have not been fully realized due to lack of community knowledge in development planning, lack of consistency in top-down and bottom- up planning due to lack of awareness and community involvement in the planning process.
Implementation of the Health Bailout Assistance Policy at the North Gorontalo District Health Service Rambulangi, Stenly Manuel; Gobel, Lisda Van; Koton , Yosef P.
Journal of Scientific Insights Vol. 2 No. 1 (2025): February
Publisher : Science Tech Group

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69930/jsi.v2i1.200

Abstract

The objectives of this research are: (1) To understand and analyze the implementation of the health bailout policy at the North Gorontalo District Health Service in terms of the degree of change desired, (who) implements the program, and the resources produced, (2) To know and analyze the inhibiting factors implementation of the health bailout policy at the North Gorontalo District Health Service. This research uses a qualitative approach with a descriptive research type.  The data consists of primary data from interviews with informants and observations and secondary data from document studies. The results of the research show that the implementation of the health bailout policy from the aspect of the degree of change desired, namely being a solution for the community and also beneficial for recipients of aid so that they get health insurance and provide the changes they want to achieve, the aspect of implementing the program, namely implementing the health bailout policy, is The Health Service manages the Regional Government of North Gorontalo Regency. In contrast, the resource aspect used is the facilities and infrastructure or health facilities available for health services at RSUD, but these health facilities are still inadequate. Furthermore, factors inhibiting policy implementation are limited human resources, which are insufficient and less competent, lack of government outreach to the community, and unclear population identification numbers, integrated data on social welfare for underprivileged communities, which causes the data to be invalid.
Honey Price Classification using K-Nearest Neighbor Machine Learning Budi Aribowo; Aprilia Tri Purwandari; Tsabitah, Nimah; Reudinta Zesha; Dwi Astharini
Journal of Scientific Insights Vol. 2 No. 1 (2025): February
Publisher : Science Tech Group

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69930/jsi.v2i1.222

Abstract

The global honey market faces significant challenges due to price inconsistencies, which often do not correlate with the actual quality of the honey. This research aims to develop a honey price classification model based on quality using the K-Nearest Neighbor (K-NN) machine learning method. The contribution in this research is a machine learning model that can classify honey prices validated by measured variables of honey concentration. Data was collected on 14 types of honey, focusing on price per 100 ml, pcategorized into 'cheap' and 'expensive' classifications. Data processing includes statistical testing using T-Test to determine the significance of price differences, followed by applying the K-NN algorithm for classification. Model performance is evaluated using metrics such as accuracy, the Receiver Operating Characteristic (ROC) curve, a graph used to evaluate the performance of binary classification models, and the Area Under the Curve (AUC). The results show that the K-NN model achieves an optimal accuracy of 100% and an AUC of 1.00 when the K parameter is set to 3, indicating excellent classification ability. It is hoped that these findings will increase market transparency, set fairer price standards, and help consumers and producers in making decisions in purchasing honey.
The Overview of Smoking on Clotting Time Results Among Students of Politeknik Piksi Ganesha Sudrajat, Agus; Alwi , Ryo Gerald Valentio
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.v1i4.229

Abstract

The World Health Organization (WHO) in 2019 stated that smoking is one of the biggest challenges in global health, as it causes approximately 6 million deaths worldwide each year. One of the effects of smoking is an increase in plasma homocysteine levels. This study is an analytical study with a cross-sectional design using quota sampling technique. The research sample consisted of students from Politeknik Piksi Ganesha aged 20-25 years who were active smokers. The study used the Lee-White method to measure blood clotting time. According to the results, it can be concluded that the blood clotting time among smoking students at Politeknik Piksi Ganesha tends to be shorter, with 18 out of 30 samples (60%) showing a shortened clotting time, while the remaining 12 samples (40%) exhibited normal clotting times. This study emphasizes that smoking is a major factor that can affect the body's hemostatic system, potentially leading to prolonged clotting time.
Revolutionizing Automotive Engineering with Artificial Neural Networks: Applications, Challenges, and Future Directions H. Abdelati, Mohamed; Ebram F.F. Mokbel; Hilal A. Abdelwali; Al-Hussein Matar; M. Rabie
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.v1i4.232

Abstract

Artificial neural networks (ANNs) have emerged as the technology that provides solutions to key issues arising in the field of automobile engineering regarding autonomous driving, predictive maintenance, energy control, and vehicle protection. This paper aims to present various uses of ANNs in car industry concerning data handling for continuous decision-making and adaptation. Convolutional Neural Networks (CNNs), Long Short-Term Memory networks (LSTMs), and Generative Adversarial Networks (GANs) are all explored in relation to their ANN specific relevance to automobiles. The identified limitation also responds to issues associated with the integration of ANN such as data dependency, the computational load required, and questions related to the ethical use of AI decision making. This paper compares ANN techniques in an automotive context, explaining where they excel and where they could use improvement in terms of the tasks they are applied to. The strategies for phased implementation of the ANN framework, the performance evaluation for each stage of implementation, and the optimization methodologies are discussed below. Future direction highlights the future development of transformers, energy efficient models and raising concerns of ethical regulatory frameworks with regards to ANN driven systems. Thus, by such barriers overcoming, ANNs have a potential to significantly influence the further development of automotive engineering and make automobiles safer, more efficient and environmentally friendly. This study advances the discussion around intelligent mobility and provides the foundation on which future research in the field can build from.

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