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All Journal PROSIDING SEMINAR NASIONAL Jurnal Penelitian Fisika dan Aplikasinya (JPFA) Berkala Fisika Indonesia : Jurnal Ilmiah Fisika, Pembelajaran dan Aplikasinya Journal of Aceh Physics Society Science and Technology Indonesia Journal of Geoscience, Engineering, Environment, and Technology Indonesian Journal of Science and Education Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Journal of Physics: Theories and Applications Prosiding SNFA (Seminar Nasional Fisika dan Aplikasinya) Al-MARSHAD: Jurnal Astronomi Islam dan Ilmu-Ilmu Berkaitan SELAPARANG: Jurnal Pengabdian Masyarakat Berkemajuan JPF : JURNAL PENDIDIKAN FISIKA Surya Abdimas Jurnal Pendidikan Fisika dan Teknologi Indonesian Review of Physics (IRiP) DIFFRACTION: Journal for Physics Education and Applied Physics Indonesian Journal of Electrical Engineering and Computer Science Buletin KKN Pendidikan Jurnal Pengabdian Kepada Masyarakat Patikala Jurnal Ilmiah Kampus Mengajar Engineering Science Letter Journal of Novel Engineering Science and Technology Bincang Sains dan Teknologi Journal of Social and Community Development Indonesian Journal on Learning and Advanced Education (IJOLAE)
Azmi Khusnani
IKIP Muhammadiyah Maumere
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Journal : Journal of Novel Engineering Science and Technology

 1 
AI Big Data System to Predict Air Quality for Environmental Toxicology Monitoring Jufriansah, Adi; Khusnani, Azmi; Pramudya, Yudhiakto; Sya’bania, Nursina; Leto, Kristina Theresia; Hikmatiar, Hamzarudin; Saputra, Sabarudin
Journal of Novel Engineering Science and Technology Vol. 2 No. 01 (2023): Journal of Novel Engineering Science and Technology
Publisher : The Indonesian Institute of Science and Technology Research

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56741/jnest.v2i01.314

Abstract

Pollutants in the air have a detrimental effect on both human existence and the environment. Because it is closely linked to climate change and the effects of global warming, research on air quality is currently receiving attention from a variety of disciplines. The science of forecasting air quality has evolved over time, and the actions of different gases (hazardous elements) and other components directly affect the health of the ecosystem. This study aims to present the development of a prediction system based on artificial intelligence models using a database of air quality sensors.This study develops a prediction model using machine learning (ML) and a Decision Tree (DT) algorithm that can enable decision harmonization across different industries with high accuracy. Based on pollutant levels and the classification outcomes from each cluster's analysis, statistical forecasting findings with a model accuracy of 0.95 have been achieved. This may act as a guiding factor in the development of air quality policies that address global consequences, international rescue efforts, and the preservation of the gap in air quality index standardization.
Comparison of K-Means Algorithm and DBSCAN on Aftershock Activity in the Flores Sea: Seismic Activity 2019-2022 Aprianti, Anyela; Jufriansah, Adi; Donuata, Pujianti Bejahida; Khusnani, Azmi; Ayuba, John
Journal of Novel Engineering Science and Technology Vol. 2 No. 03 (2023): Journal of Novel Engineering Science and Technology
Publisher : The Indonesian Institute of Science and Technology Research

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56741/jnest.v2i03.393

Abstract

This study seeks to determine whether the clustering method can be used to analyze Flores Sea earthquake activity. In this investigation, the BMKG Repo serves as the source for real earthquake vibration data collection. The stages of this research include preparing the data in CSV format and then preparing the data to eliminate useless data by identifying missing data. On the basis of the research data, it was determined that the K-Means and DBSCAN methods are used to determine the clustering method for analyzing earthquake activity. In addition, the data is depicted using a graphical Elbow method so that we can determine the number of clusters of aftershocks in the Flores Sea. The results of the visualization of aftershocks that followed earthquakes in the Flores Sea between 2019 and 2022 revealed three distinct groups of earthquake source depths: 33 to 70 kilometers, 150 to 300 kilometers, and 500 to 800 kilometers. In terms of the shilhoute index parameter, the K-Means algorithm is preferable to the DBSCAN algorithm when clustering results are used to analyze earthquake activity.
The Effect of Attenuation on Simulation of Tsunami Wave Propagation Using FDM Ahdiany, Dian; Khusnani, Azmi; Jufriansah, Adi; Prasetyo, Erwin
Journal of Novel Engineering Science and Technology Vol. 3 No. 01 (2024): Journal of Novel Engineering Science and Technology
Publisher : The Indonesian Institute of Science and Technology Research

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56741/jnest.v3i01.395

Abstract

This study seeks to investigate the shape of the surface of tsunami waves using the finite difference method and the effect of the damping function on the simulation of tsunami wave propagation using Matlab-based visualisation. The effect of attenuation on the propagation of tsunami waves is measured by the variation in energy. The results of the investigation indicate that tsunami waves have a transverse wave form, with waves propagating in a perpendicular direction. In the meantime, the analysis of the damping function reveals a decrease in the value of energy; this indicates that if the damping function is provided, it will have the effect of reducing the wave energy and propagation speed of tsunami waves. This modelling clearly and realistically illustrates the results of wave movement visualisation and provides insight for disaster mitigation and coastal protection.
Suspension Bridge Estimation Method using the Fokker-Planck Model Jufriansah, Adi; Lazwardi, Ahmad; Pramudya, Yudhiakto; Nurrahman, Arip; Khusnani, Azmi; Yohakim, Yoman Ribeta Ratu
Journal of Novel Engineering Science and Technology Vol. 3 No. 02 (2024): Journal of Novel Engineering Science and Technology
Publisher : The Indonesian Institute of Science and Technology Research

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56741/jnest.v3i02.536

Abstract

The failure of the suspension bridge has been known since the beginning of the bridge collapse. Most of these failures form the basis of current engineering knowledge. One of the factors of failure is human-made factors related to the calculation of the bridge estimate. This paper presents an indirect estimation method using numerical simulation using finite elements by analyzing the Fokker-Planck model when dynamic excitation is associated with bridge loads. The results show that the Fokker-Planck model's homogeneous form can take into account the solution for the bridge analysis approach. It leads to a stable state when giving mass variations to the model. The indirect estimation method using finite elements can estimate the cable tension with controllable weak damping. It can be concluded that the method in this study is more accurate and convenient for the application technique.
LLMs Solution to Fake News, Disinformation, and Hoaxes: Llama 3 [70B]-based Hoax Detection and Counteraction System Jufriansah, Adi; Pramudya, Yudhiakto; Khusnani, Azmi; Malahina, Edwin Ariesto Umbu
Journal of Novel Engineering Science and Technology Vol. 4 No. 02 (2025): Journal of Novel Engineering Science and Technology
Publisher : The Indonesian Institute of Science and Technology Research

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56741/jnest.v4i02.952

Abstract

In the digital age, hoaxes or false information are a significant challenge, as they can harm public comprehension, form inaccurate opinions, and endanger the health and safety of individuals. Artificial intelligence technology, particularly large language models (LLMs) like Llama 3, provides an innovative solution to these challenges. A sophisticated generative model with superior natural language processing capabilities, Llama 3 enables the effective detection and clarification of hoaxes. A dataset that is seven times larger than its antecedent, Llama 2, is utilized to train this model. The dataset has a token capacity of up to 128K and a context length of up to 8 K. By utilizing these capabilities, Llama 3 is capable of comprehending context, offering responses that are grounded in scientific data, and reducing response errors. Educational chatbots, interactive web platforms, and mobile applications that are based on Llama 3 can be implemented. This model effectively identifies and clarifies false information regarding cosmic rays that are purportedly hazardous through the presentation of pertinent scientific facts, as demonstrated by case studies. Llama 3's capabilities encompass its capacity to modify parameters to generate valid and pertinent responses. This renders it a critical instrument for bolstering community resilience to the dissemination of falsehoods, as well as digital literacy and awareness. Llama 3, which is open source, facilitates global collaboration in the development of a more secure and trustworthy information ecosystem.
Co-Authors Ade Anggraini Adi Jufriansah Afriyanto, Mulya Ahdiany, Dian Alip, Isma Anggraini, Ade Aprianti, Anyela Arief Hermanto Arief Hermanto Arief Hermanto Ayuba, John Colomeischi Aurora Adina Danurdara Setiamukti Deti, Maria Dian Ahdiany Donuata, Pujianti Bejahida Dwi Sulistyaningsih Edwin Ariesto Umbu Malahina Endang Sulastri Erwin Prasetyo Fiqry, Rizalul Fitri Nur Hikmah Fitri, Moh. Hamzarudin Hikmatiar Hastuti, Isnaini Budi hikmatiar, hamzarudin Irfan Miftahul Fauzi Ishafit Ishafit Isma Alip Junita, Ode Kartika Dewi Rahmawati Konsenius Wiran Wae Lazwardi, Ahmad Leto, Kristina Theresia M Taufiqurrahman Ma'rup Margiono Margiono Margiono, Margiono Moh. Toifur Mohammad Fitri Mohammad Fitri Muhammad Ihsan Mulya Afriyanto Mulya Afriyanto Niehlah, Anis Rohadatul Nurdin H. Abd. Rahman S. Nurrahman, Arip Nursina Sya'bania Ota Welly Jenni Thalo Ota Welly Jenni Thalo PRASETYO, ERWIN Rahmawati Husein, Rahmawati Rini Fatmawati Rita Rahmaniati Sabarudin Saputra Saharul Saharul Saharul Saharul, Saharul Sahlan Sahlan Samana, Fazaki Ramadhani Anwar Sanlan, Sahlan Servia Sina, Tuti Asmianti Sri Slamet Suherman, Suherman Suherman, Suherman Suwandi Wahab, Dedi Sya’bania, Nursina Tria Puspita Sari Wae, Konsenius Wiran Wahab, Dedi Suwandi Wahyuningsih Wahyuningsih Wahyuningsih, Wahyuningsih Yanto Yanto Yohakim, Yoman Ribeta Ratu Yoman Ribeta Ratu Yohakim Yudhiakto Pramudya Yudhiakto Pramudya Yudhiakto Pramudya Yunita Kristianti Subandi Z. Zulfakriza Zulfakriza, Zulfakriza