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All Journal Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Educatio
Sopian, Sani Moch
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Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Energy Engineering

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Peningkatan Kreativitas dan Pemahaman Siswa tentang Energi Terbarukan Melalui Perakitan Mobil Mainan Bertenaga Surya di SMP Baitul Hikmah Ikhsan, Akhmad Fauzi; Nurpalah, Rifki; Nugroho, Ginaldi Ari; Sopian, Sani Moch; Wardani, Sekar Ayu Kusuma
Educatio Vol 20 No 1 (2025): Educatio: Jurnal Ilmu Kependidikan
Publisher : Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/edc.v20i1.29853

Abstract

This activity was organized with the aim of introducing the concept of renewable energy, particularly solar energy, to students of SMP Baitul Hikmah through the assembly of solar-powered toy cars. The program was designed to raise students' awareness of the importance of environmentally friendly energy while also enhancing their critical thinking skills and creativity in applying theory to practice. Throughout the activity, students actively participated in the assembly process and encountered various technical challenges that encouraged them to find solutions independently. As a result, the activity demonstrated an improvement in students' understanding of solar energy as well as the development of their creativity in completing assigned tasks.
Optimizing Autonomous Navigation: Advances in LiDAR-based Object Recognition with Modified Voxel-RCNN Firman; Satyawan, Arief Suryadi; Susilawati, Helfy; Haqiqi, Mokh. Mirza Etnisa; Artemysia, Khaulyca Arva; Sopian, Sani Moch; Wijaya, Beni; Samie, Muhammad Ikbal
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 10, No. 2, May 2025
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v10i2.2199

Abstract

This study aimed to enhance the object recognition capabilities of autonomous vehicles in constrained and dynamic environments. By integrating Light Detection and Ranging (LiDAR) technology with a modified Voxel-RCNN framework, the system detected and classified six object classes: human, wall, car, cyclist, tree, and cart. This integration improved the safety and reliability of autonomous navigation. The methodology included the preparation of a point cloud dataset, conversion into the KITTI format for compatibility with the Voxel-RCNN pipeline, and comprehensive model training. The framework was evaluated using metrics such as precision, recall, F1-score, and mean average precision (mAP). Modifications to the Voxel-RCNN framework were introduced to improve classification accuracy, addressing challenges encountered in complex navigation scenarios. Experimental results demonstrated the robustness of the proposed modifications. Modification 2 consistently outperformed the baseline, with 3D detection scores for the car class in hard scenarios increasing from 4.39 to 10.31. Modification 3 achieved the lowest training loss of 1.68 after 600 epochs, indicating significant improvements in model optimization. However, variability in the real-world performance of Modification 3 highlighted the need for balancing optimized training with practical applicability. Overall, the study found that the training loss decreased up to 29.1% and achieved substantial improvements in detection accuracy under challenging conditions. These findings underscored the potential of the proposed system to advance the safety and intelligence of autonomous vehicles, providing a solid foundation for future research in autonomous navigation and object recognition.
Co-Authors Artemysia, Khaulyca Arva Firman Haqiqi, Mokh. Mirza Etnisa Helfy Susilawati Ikhsan, Akhmad Fauzi Nugroho, Ginaldi Ari Nurpalah, Rifki RR. Ella Evrita Hestiandari Samie, Muhammad Ikbal Satyawan, Arief Suryadi Wardani, Sekar Ayu Kusuma