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Newton-Maxwell Journal of Physics
Published by Universitas Bengkulu
ISSN : -     EISSN : 27755894     DOI : 10.33369
Core Subject : Science, Engineering,
Astronomy Earth & Planetary Sciences Electrical & Electronics Engineering Mechanical Engineering Physics
Newton-Maxwell Journal of Physics is a scientific journal published by UNIB Press and managed by the Department of Physics, FMIPA University, Bengkulu, with ISSN Number: 2775-5894. This journal is published twice a year, in April and October, as a forum for lecturers, researchers, and students to publish their work. The scope of this scientific journal covers the field of physics in general, such as materials, theory and instrumentation, geophysics, acoustics, computer science, physics and its applications, and other relevant areas.
Arjuna Subject : Fisika dan Astronomi - Fisika dan Astronomi (Lain-Lain)
Articles 66 Documents
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A Systematic Review of the Use of Ordinary Differential Equations in Modeling the Dynamics and Control of Autonomous Vehicles Detrika Mawar Arrum; Akhsan, Hamdi; Nely Andriani; Wardah Amalia
Newton-Maxwell Journal of Physics Vol. 7 No. 1: April 2026
Publisher : UNIB Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33369/nmj.v7i1.46608

Abstract

This systematic review aims to synthesize and critically evaluate the application of ordinary differential equations (ODEs) in modeling and controlling autonomous vehicle dynamics from 2020 to 2025. Following the PRISMA framework, 36 peer-reviewed journal articles and conference proceedings were selected and analyzed to map model types, control strategies, and their practical trade-offs. The review identifies six primary ODE-based modeling approaches kinematic, dynamic, linear, nonlinear, single-track, and two-track each exhibiting distinct balances among accuracy, computational demand, and real-time feasibility. Integration with control methods such as PID, LQR, Model Predictive Control (MPC), and nonlinear techniques reveals context-dependent performance: while MPC and sliding mode control offer high accuracy and robustness, they impose significant computational burdens; in contrast, PID and LQR are lightweight but limited in nonlinear or high-disturbance scenarios. Critical gaps persist, including insufficient real-world validation, limited fusion of physics-based ODEs with data-driven learning, scarcity of open dynamic datasets, and unresolved real-time implementation challenges. The study concludes that model–control selection must be mission- and hardware-aware, and calls for stronger empirical validation, standardized benchmarks, and hybrid architectures combining ODE rigor with machine learning adaptability.
Assessing Groundwater Contamination Risks from Dumpsite Leachate in Southwestern Nigeria: An Integrated Geophysical and Hydrochemical Review Ogunkoya, Charles; Alasi, Taiwo Kamorudeen
Newton-Maxwell Journal of Physics Vol. 7 No. 1: April 2026
Publisher : UNIB Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33369/nmj.v7i1.47875

Abstract

Groundwater contamination from dumpsite leachate poses a significant environmental and public health challenge, particularly in developing regions like Southwestern Nigeria, where rapid urbanization and inadequate waste management exacerbate the problem. Leachate, a toxic byproduct of decomposing waste, infiltrates aquifers, posing a threat to water quality. This review synthesizes existing research on groundwater contamination risks from dumpsite leachate, employing geophysical and hydrochemical methods. The study aims to elucidate contamination mechanisms, evaluate methodological synergies, and identify gaps for future research. These methods collectively map leachate plumes and quantify pollutant concentrations, providing a comprehensive assessment of the extent of contamination. Geophysical surveys consistently identify low-resistivity zones (<100 Ωm) that correlate with dumpsite locations and hydrochemical indicators associated with leachate contamination. Hydrochemical data reveal elevated levels of heavy metals: chloride, nitrate, often exceeding both local and international limits. The Basement Complex geology, with fractured aquifers, exacerbates contaminant spread, while seasonal rainfall intensifies leachate mobility. The findings underscore thr critical need for improved waste management and groundwater protection in Nigeria. Persistent heavy metal contamination highlights long-term health risks, even where general water quality parameters appear acceptable. Alternative water sources should mitigate health impacts, while advanced remediation technologies and policy enforcement are essential for sustainable solutions.
Prediksi Trajektori dan Intensitas Siklon Tropis Menggunakan Pendekatan Multi-Task Learning Berbasis Recurrent Neural Network Syahid, Wisnu; Putu Aldi Tusan Pratama; Muhammad Nur Rizqi; Yosik Norman
Newton-Maxwell Journal of Physics Vol. 7 No. 1: April 2026
Publisher : UNIB Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33369/nmj.v7i1.47955

Abstract

The limited ability of Numerical Weather Prediction (NWP) models to capture nonlinear dynamics and atmospheric uncertainty remains a major challenge in improving tropical cyclone forecasts, particularly over the eastern Indian Ocean. This study evaluates a Multi-Task Learning approach based on several Recurrent Neural Network (RNN) variants, namely LSTM, BiLSTM, GRU, and BiGRU, to simultaneously predict three key cyclone components: position (latitude and longitude), wind intensity, and cyclone category. Historical IBTrACS data from 2000 to 2025 with a 3-hour temporal resolution are used as model input, employing 48-hour sequences to forecast cyclone conditions at lead times of 12, 24, 48, and 72 hours. The results show that all models achieve stable convergence during training. At a 12-hour lead time, the BiLSTM model delivers the best performance, with a mean position error of 83.53 km and a Hit Rate of 0.966, outperforming the other models. For longer lead times (24–72 hours), the BiGRU model demonstrates the most stable positional accuracy, exhibiting the lowest error degradation as the forecast horizon increases. In addition, wind intensity predictions remain robust, with a Mean Absolute Error (MAE) below 4.6 knots up to 72 hours. These findings highlight the potential of multi-output RNN-based models to support more adaptive and efficient tropical cyclone forecasting systems.
Analisis Dinamika Labilitas Atmosfer Hujan Es berbasis Satelit, Model ERA-5, dan Observasi (Studi Kasus : Jawa Timur, 8 Oktober 2025 Carundyatama, Daniar Ihza; Anisa Nabilah; Ramadhan, Faiz Ahza; Achmad Zakir; Aditya Mulya
Newton-Maxwell Journal of Physics Vol. 7 No. 1: April 2026
Publisher : UNIB Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33369/nmj.v7i1.47973

Abstract

Hujan es adalah fenomena cuaca langka yang terjadi di daerah tropis dengan proses fenomena ketika tetesan air di dalam awan konvektif mengalami pembekuan akibat suhu yang sangat rendah pada ketinggian troposfer atas, kemudian jatuh ke permukaan sebelum sempat mencair. Penelitian ini menganalisis kondisi atmosfer yang memicu terjadinya hujan es di Jawa Timur, khususnya Mojokerto, dan daerah sekitarnya pada tanggal 8 Oktober 2025. Analisis ini dilakukan melalui pendekatan multi-data, termasuk citra satelit dari Himawari-9, reanalisis ERA5, pengamatan radiosonde Stasiun Meteorologi Kelas I Juanda. Analisis satelit menunjukkan perkembangan cepat awan konvektif dengan suhu puncak awan di bawah -60 °C, yang menandakan adanya awan cumulonimbus yang sangat dalam. Atmosfer ditandai dengan kelembaban tinggi di lapisan bawah, geseran angin vertikal yang kuat, serta konvergensi di lapisan bawah dan divergensi di lapisan atas. Indeks labilitas seperti K Index dan Total Totals Index menunjukkan bahwa atmosfer sangat kondusif untuk konveksi yang kuat. Analisis penampang vertikal ERA5 menunjukkan adanya aliran naik yang signifikan dan peningkatan kandungan es awan di lapisan tengah. Di sisi lain, diagram Skew-T menunjukkan CAPE rendah hingga sedang dengan CIN yang cukup besar, menunjukkan bahwa pemicu dinamis diperlukan untuk memicu konveksi. Penelitian ini diharapkan dapat meningkatkan pemahaman tentang mekanisme hujan es di daerah tropis.
Eksplorasi Potensi Limbah Sayur Lokal Indonesia sebagai Sumber Karbon untuk Elektroda Padat Bebas Perekat untuk Penyimpan Energi Elektrokimia: Eksplorasi Potensi Limbah Sayur Lokal Indonesia sebagai Sumber Karbon untuk Elektroda Padat Bebas Perekat Apriwandi, Apriwandi; Sinta Mustika, Widya; Julnaidi, Julnaidi; Taslim, Rika; Taer, Erman
Newton-Maxwell Journal of Physics Vol. 7 No. 1: April 2026
Publisher : UNIB Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33369/nmj.v7i1.48093

Abstract

Penelitian ini bertujuan mengeksplorasi potensi limbah sayur lokal Indonesia berbasis daun lobak a untuk elektroda karbon padat bebas perekat pada superkapasitor dengan penekanan pada kinerja volumetrik. Elektroda karbon disintesis melalui pra-karbonisasi biomassa, pencetakan bertekanan tinggi, dan pirolisis hingga 800°C tanpa penambahan bahan pengikat, dengan dua variasi sampel, yaitu karbon tanpa perlakuan (DLK) dan karbon dengan impregnasi awal H3PO4 konsentrasi rendah (DLK-P). Karakterisasi dilakukan terhadap perubahan densitas, morfologi, dan komposisi unsur, serta evaluasi sifat elektrokimia menggunakan voltametri siklik. Hasil menunjukkan bahwa impregnasi kimia mampu menghasilkan struktur karbon yang lebih homogen dan mengurangi komponen non-konduktif, tanpa menurunkan stabilitas elektroda monolitik. Kondisi ini berdampak pada peningkatan respons kapasitif dan retensi kapasitansi pada berbagai laju pindai. Elektroda DLK-P mencapai kapasitansi volumetrik sebesar 109 F/cm³, jauh lebih tinggi dibandingkan DLK sebesar 53,4 F/cm³. Temuan ini menegaskan bahwa limbah daun lobak berpotensi dimanfaatkan sebagai bahan baku elektroda karbon padat bebas perekat melalui pendekatan sintesis sederhana dan berkelanjutan.
Prediksi Curah Hujan Jangka Menengah di Provinsi Lampung Menggunakan Artificial Neural Network Berbasis Data CHIRPS dan Indeks Iklim Global Pratama, Alvin
Newton-Maxwell Journal of Physics Vol. 7 No. 1: April 2026
Publisher : UNIB Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33369/nmj.v7i1.48129

Abstract

Informasi curah hujan yang akurat dan andal sangat penting untuk mendukung perencanaan pertanian, pengelolaan sumber daya air, serta mitigasi bencana hidrometeorologi. Provinsi Lampung merupakan wilayah dengan tingkat kerentanan tinggi terhadap variabilitas iklim yang dipengaruhi oleh fenomena iklim global seperti El Niño–Southern Oscillation (ENSO) dan Indian Ocean Dipole (IOD). Penelitian ini bertujuan mengembangkan model prediksi curah hujan bulanan jangka menengah di Provinsi Lampung menggunakan Artificial Neural Network (ANN) berbasis data satelit CHIRPS dan indeks iklim global. Data yang digunakan meliputi curah hujan observasi dari 19 pos hujan, CHIRPS, Oceanic Niño Index (ONI), dan Dipole Mode Index (DMI) selama periode 1991–2020. Tiga konfigurasi model ANN diuji, yaitu kombinasi CHIRPS–DMI, CHIRPS–ONI, serta CHIRPS–DMI–ONI. Hasil evaluasi menunjukkan bahwa data CHIRPS memiliki korelasi kuat terhadap curah hujan observasi dengan nilai rata-rata 0,659 (p < 0,001). Analisis korelasi menunjukkan bahwa IOD memiliki pengaruh lebih dominan terhadap curah hujan Lampung dibandingkan ENSO. Model ANN terbaik diperoleh pada konfigurasi CHIRPS–DMI dengan nilai RMSE terendah sebesar 55,49 mm dan koefisien korelasi tertinggi sebesar 0,671. Model ini menunjukkan performa prediksi yang stabil hingga 12 bulan ke depan. Hasil penelitian ini menunjukkan potensi integrasi data satelit dan indeks iklim global dalam mendukung sistem prediksi curah hujan dan mitigasi risiko iklim di wilayah tropis.

Page 7 of 7 | Total Record : 66

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