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Dedi Purwanto Indra Kusuma
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Journal of Engineering and Technological Science
Published by Lembaga Penelitian dan Pendidikan (LPP) Kalibra
ISSN : -     EISSN : 3110259X     DOI : https://doi.org/10.70716/jets
Core Subject : Science, Engineering,
Aerospace Engineering Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mechanical Engineering Transportation
Journal of Engineering and Technological Science (JETS) is a peer-reviewed open-access journal published by Lembaga Penelitian dan Pendidikan (LPP) Kalibra with registered number of e-ISSN 3110-259X, dedicated to publishing high-quality research and innovation in the field of engineering and technological science. JETS provides a forum for researchers, academics, professionals, practitioners and students to instill and share knowledge in the form of empirical and theoretical papers, case studies, literature reviews and book reviews related to scientific research in the field of technology and engineering, and or related to it with various themes Engineering such as mechanical engineering, electrical and electronic engineering, civil engineering, chemical engineering, industrial engineering, as well as informatics and computer engineering, robotics, industrial automation, artificial intelligence, Internet of Things (IoT). This journal will process all manuscript receipts in a double anonymous review by Bestari partners.
Arjuna Subject : Teknik (semua kategori) - Teknik (Lain-Lain)
Articles 5 Documents
 1 
Search results for , issue "Vol. 1 No. 2: Journal of Engineering and Technological Science, November 2025" : 5 Documents clear
Analisis Efisiensi Energi pada Sistem Penerangan Gedung Bertingkat Ahmad Rizky Ramadhan
Journal of Engineering and Technological Science Vol. 1 No. 2: Journal of Engineering and Technological Science, November 2025
Publisher : Lembaga Penelitian dan Pendidikan (LPP) Kalibra

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70716/jets.v1i2.130

Abstract

The increasing electrical energy consumption in high-rise buildings highlights the urgent need for efficient and environmentally friendly lighting systems. This study aims to analyze the energy efficiency level of lighting systems in high-rise buildings through the implementation of energy-saving LED lamps and automated sensor systems. The research methodology involved an energy usage survey, light intensity measurements, and a comparative analysis of energy consumption before and after the implementation of the automation system. The results showed a reduction in energy consumption of up to 35% without compromising users’ visual comfort. The implementation of light and motion sensors also improved operational efficiency and significantly reduced monthly electricity costs. In conclusion, the application of energy-efficient lighting systems has a positive impact on energy sustainability and contributes to reducing carbon emissions in high-rise buildings.
Optimasi Jaringan Distribusi Air Bersih Menggunakan Algoritma Genetika dan Data Spasial Siti Rahmawati
Journal of Engineering and Technological Science Vol. 1 No. 2: Journal of Engineering and Technological Science, November 2025
Publisher : Lembaga Penelitian dan Pendidikan (LPP) Kalibra

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70716/jets.v1i2.131

Abstract

The optimization of clean water distribution networks is an essential issue in urban infrastructure management, particularly in developing countries where water demand increases rapidly while resources remain limited. This research proposes an optimization approach for water distribution networks using Genetic Algorithm (GA) combined with spatial data analysis in a Geographic Information System (GIS) environment. The study integrates hydraulic modeling with spatial parameters, including elevation, population density, and pipeline topology, to improve pressure balance and minimize energy consumption. The optimization process is performed using EPANET coupled with a GA-based optimization engine developed in MATLAB. The results show that the proposed method reduces total head loss by 28% and decreases pumping energy costs by 15% compared to conventional design approaches. Spatial data integration enhances the precision of network analysis and provides more realistic representation of topographic conditions. The findings demonstrate that GA combined with spatial data can effectively support decision-making for sustainable and cost-efficient water distribution planning.
Pengaruh Desain Aerodinamika Terhadap Efisiensi Bahan Bakar pada Kendaraan Bermotor Listrik Raka Pratama
Journal of Engineering and Technological Science Vol. 1 No. 2: Journal of Engineering and Technological Science, November 2025
Publisher : Lembaga Penelitian dan Pendidikan (LPP) Kalibra

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70716/jets.v1i2.132

Abstract

The aerodynamic design of electric vehicles (EVs) significantly influences their overall energy efficiency, particularly in reducing air drag and improving battery range. This study aims to analyze the effect of aerodynamic body design on the energy consumption of electric motor vehicles using computational fluid dynamics (CFD) simulations. Three different vehicle models with varying drag coefficients (Cd = 0.22, 0.28, and 0.35) were evaluated under identical speed and wind tunnel conditions. The results indicate that reducing the drag coefficient by 0.1 can increase the vehicle’s travel range by approximately 8–12%, depending on speed and battery capacity. Streamlined body shapes and underbody smoothing were found to be the most effective design features in minimizing drag. This study highlights the critical role of aerodynamic optimization in improving the sustainability and performance of electric vehicles, offering design guidelines for future EV development.
Prediksi Kemacetan Lalu Lintas Urban Menggunakan Model Pembelajaran Mesin dan Data Mobilitas Real-time Ahmad Fikri
Journal of Engineering and Technological Science Vol. 1 No. 2: Journal of Engineering and Technological Science, November 2025
Publisher : Lembaga Penelitian dan Pendidikan (LPP) Kalibra

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70716/jets.v1i2.133

Abstract

Urban traffic congestion is a persistent challenge in rapidly growing cities, leading to increased travel times, fuel consumption, and pollutant emissions. This study aims to develop a machine-learning-based prediction model for urban traffic congestion by leveraging real-time mobility data obtained from vehicle probes and sensor networks. The proposed framework integrates supervised learning techniques including gradient boosting, random forest, and recurrent neural networks to forecast congestion levels with a lead time of 15 to 60 minutes. A dataset collected from a metropolitan region over the course of six months (including vehicle speeds, volumes, occupancy, and external factors such as weather and special events) was used for model training and validation. The results show that the best-performing model (gradient boosting) achieved an accuracy of 87% and a root mean squared error (RMSE) reduction of 23% compared to a baseline regression model. The findings suggest that real-time mobility data combined with advanced machine learning methods can significantly enhance congestion prediction performance, enabling urban traffic managers to implement proactive interventions. The study contributes to the field of intelligent transportation systems by providing a practical modelling approach and highlighting the importance of multi-source data integration. Future work should explore deployment in heterogeneous networks and test scalability across multiple cities.
Sistem Kendali Otonom pada Kendaraan Listrik Menggunakan Sensor Fusion dan Kalman Filter Ahmad Rizal
Journal of Engineering and Technological Science Vol. 1 No. 2: Journal of Engineering and Technological Science, November 2025
Publisher : Lembaga Penelitian dan Pendidikan (LPP) Kalibra

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70716/jets.v1i2.134

Abstract

The development of autonomous electric vehicles requires highly accurate and reliable control systems to ensure safety and efficiency. This research presents an autonomous control system for electric vehicles using sensor fusion integrated with the Kalman Filter algorithm. The system combines data from multiple sensors, including LiDAR, IMU, and GPS, to improve localization accuracy and environmental awareness. A simulation-based experiment was conducted using MATLAB/Simulink and Robot Operating System (ROS) environments. The results show that the Kalman Filter reduces localization error by 37% compared to single-sensor systems, while the sensor fusion approach improves object detection stability under dynamic conditions. The proposed system demonstrates improved path tracking accuracy and smoother control response. These findings highlight the effectiveness of sensor fusion and Kalman Filter implementation in enhancing autonomous vehicle navigation performance.

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