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All Journal Jurnal Penelitian Fisika dan Aplikasinya (JPFA) Berkala Fisika Indonesia : Jurnal Ilmiah Fisika, Pembelajaran dan Aplikasinya Berkala Ilmiah Pendidikan Fisika Eduma : Mathematics Education Learning and Teaching 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 BERDIKARI : Jurnal Inovasi dan Penerapan Ipteks Prosiding SNFA (Seminar Nasional Fisika dan Aplikasinya) Al-MARSHAD: Jurnal Astronomi Islam dan Ilmu-Ilmu Berkaitan ORBITA: Jurnal Pendidikan dan Ilmu Fisika SELAPARANG: Jurnal Pengabdian Masyarakat Berkemajuan JPF : JURNAL PENDIDIKAN FISIKA Surya Abdimas Jurnal Ilmu Pendidikan (JIP) STKIP Kusuma Negara Jurnal Pendidikan Fisika dan Teknologi Kappa Journal JPMI (Jurnal Pembelajaran Matematika Inovatif) Indonesian Review of Physics (IRiP) TRIDARMA: Pengabdian Kepada Masyarakat (PkM) DIFFRACTION: Journal for Physics Education and Applied Physics Indonesian Journal of Electrical Engineering and Computer Science Jurnal Simki Pedagogia Jurnal Pengabdian Kepada Masyarakat Patikala Jurnal Ilmiah Kampus Mengajar Engineering Science Letter Journal of Novel Engineering Science and Technology Bincang Sains dan Teknologi Buletin Edukasi Indonesia INPAFI (Inovasi Pembelajaran Fisika) Research in Physics Education International Conference on Education for All Edukasi Elita : Jurnal Inovasi Pendidikan Journal of Social and Community Development Journal on Mathematics Education Frontiers in Sustainable Science and Technology Indonesian Journal on Learning and Advanced Education (IJOLAE)
Adi Jufriansah
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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.
Experimental Study of Gravity Measurement with a Video-Based Laboratory Pendulum with Tracker Software: Comparison of Weighted and Unweighted Tests Wahab, Dedi Suwandi; Hamsa, Berlian; Sina , Tuti Asmianti; Deti, Maria; Anwar, Zaina; Arifin, Anggun Syafira; Nursilawati, Wingki; Servia; Jaudin, Santi Hasan; Jufriansah, Adi
Journal of Novel Engineering Science and Technology Vol. 4 No. 01 (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.v4i01.644

Abstract

This research aims to determine the value of the Earth's gravitational acceleration (g) using the mathematical pendulum swing method with an unweighted linear regression approach, weighted linear regression, and the 4th-order Runge-Kutta numerical method. The data used are the results of measuring the swing period of the pendulum for various lengths of string. The analysis was carried out by calculating the average value of gravitational acceleration using multiple methods. The results show that weighted linear regression provides more consistent and accurate estimates than unweighted linear regression, with a high coefficient of determination (R²) value. The Runge-Kutta numerical method is also used to predict swing periods with a more in-depth mathematical approach, producing values ​​supporting experimental data trends. Overall, this research makes an average value of gravitational acceleration of around 9.11 m/s², close to the expected theoretical value. These findings show that the mathematical pendulum swing method can be used effectively to measure the Earth's gravitational acceleration with sufficient accuracy and provide an essential contribution in the context of physics education regarding the application of basic principles in physics experiments.
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.
Comparison of Multi-Face Detection Performance on Images Using Haarcascade, Dlib, and RetinaFace Saputra, Sabarudin; Jufriansah, Adi; Mu’min, Muhammad Amirul; Akbar, Muhammad; Jayanto, Deni Luvi; Hernita, Ayu
Journal of Novel Engineering Science and Technology Vol. 4 No. 03 (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.v4i03.1134

Abstract

Multi-face detection presents a significant challenge in computer vision, especially in environments with limited hardware resources. This study compares the performance of three multi-face detection methods—Haarcascade, Dlib (HOG and CNN), and RetinaFace—using a subset of the WIDER FACE dataset in a CPU-only environment without GPU acceleration. The experiment was conducted in two stages using a total of 300 images from the WIDER FACE dataset, which reflect real-world variations such as pose, scale, illumination, expression, and occlusion. Performance evaluation was carried out using precision, recall, F1-score, accuracy, and processing time as metrics. The results show that RetinaFace consistently outperforms the other methods, achieving superior metrics in Recall (0.92), F1-score (0.93), and Accuracy (0.88) on Subset A, and leading across all metrics on Subset B. While Dlib-CNN demonstrates high detection performance, it suffers from very slow processing time. In contrast, Haarcascade delivers the fastest processing speed but performs poorly in terms of evaluation metrics. The experiments also reveal that RetinaFace is the most consistent and reliable method based on standard deviation values of precision (0.01), recall (0.11), F1-score (0.07), and accuracy (0.11). Overall, this study contributes valuable insights for selecting efficient face detection methods under constrained resource conditions.
Co-Authors Ade Anggraini Afriyanto, Mulya Agam Akhmad Syaukani, Agam Akhmad Ahdiany, Dian Ahmad Iqbal Hidayat Ajah Saputra Al Farizi, Zakaria Al Sanaani, Ali Essam Alfarizi Alfarizi Alip, Isma Andiyani, Rhavida Anniza Anggraini, Ade Anomeisa, Agnesia Bergita Anwar, Zaina Anwari, Ardiansyah Risko Aprianti, Anyela Arief Hermanto Arief Hermanto Arief Hermanto Arif Jamali Arifin, Anggun Syafira Asmia Fransiska Aurelia Aleny Ayu Hernita Ayuba, John Azmi Khusnani Bogar, Dominika Yonavista Budi Jatmiko Budi Jatmiko Bukangdonu, Feronika Colomeischi Aurora Adina Danar Aswim Danur Dara Setiamukti Deni Luvi Jayanto Deti, Maria Dhema, Magdalena Dian Ahdiany Donuata, Pujianti Bejahida Dwi Prastyo Dwi Sulistyaningsih Dwi Sulisworo Edwin Ariesto Umbu Malahina Eko Purnomo Endang Sulastri Endang Sulastri Erwin Prasetyo Fitri Nur Hikmah Fitri, Moh. Fitria Eka Wulandari Fitriany, Devinda Nur Florentinus Primarius Naraama Koten Fransiska Antonia Sari Hafizhatu Nadia Halili, Siti Hajar Binti Halim Hamsa, Berlian Hamzarudin Hikmatiar Hamzarudin Hikmatiar Harun Joko Prayitno Heri Siswanto hikmatiar, hamzarudin Irfan Miftahul Fauzi Isma Alip Jaudin, Santi Hasan Jayadin Kanisius Goreti Sangi Kartika , Ika Kartika Dewi Rahmawati Khusnani , Azmi Koesoemo Ratih Konsenius Wiran Wae Kurniaji, Ganno Tribuana Lazwardi, Ahmad Leto, Kristina Theresia Ma'rup Mahmud, Randy Saputra Maodjud, Siti Hamia Margiono Margiono Margiono, Margiono Maria Elvina Maria Yosephien Retna Tinon Kawuri Maulana, Mahesa Meda, Helmiana Dua Moh. Toifur Mohammad Fitri Mohammad Fitri Muhamad Taufik Arifin Muhammad Akbar Muhammad Syahriandi Adhantoro Mulya Afriyanto Mulya Afriyanto Murtiningsih, Tenny Mu’min, Muhammad Amirul Naufal Ishartono Ngaga, Elisabeth Jaa Niehlah, Anis Rohadatul Nisa, Kartini Rahman Noly Shofiyah Noor Aida Aflahah Nugroho, Febriyanto Arif Nuniati Nuniati Nurahman, Arip Nurdin H. Abd. Rahman S. Nurlina Nurlina Nurrahman, Arip Nursilawati, Wingki Nursina Sya'bania Ota Welly Jenni Thalo Ota Welly Jenni Thalo Pradita, Rachel Aulia PRASETYO, ERWIN Pribadi, Pandu Purnama , Aventus Puteri Novianty Rahmawati Husein, Rahmawati Rakuasa, Heinrich Ratnasari Diah Utami Razak, Rafiza binti Abdul Rifai , Ahmat Rizky Habibur Rohman Sabarudin Saputra Saharul Saharul Saharul Saharul, Saharul Sahlan Sahlan Sahlan Samana, Fazaki Ramadhani Anwar Servia Sina , Tuti Asmianti Sina, Tuti Asmianti Sofiyudin, Muh Subandi, Yunita Kristianti Suritno - Fayanto Suwandi Wahab, Dedi Sya’bania, Nursina Tade , Ferdi Taha A, Ali Abdulraoof Tanti Diyah Rahmawati Tria Puspita Sari Wae, Konsenius Wiran Wahab, Dedi Suwandi Wahyuningsih Wahyuningsih Wahyuningsih, Wahyuningsih Yanto Yanto Yohakim, Yoman Ribeta Ratu Yoman Ribeta Ratu Yohakim Yomianus Bura Yudhiakto Pramudya Yudhiakto Pramudya Yudhiakto Pramudya Yunita Kristianti Subandi Z. Zulfakriza Zulfakriza, Zulfakriza