PENGEMBANGAN BAHAN AJAR SIMULTANEOUS LOCALIZATION AND MAPPING (SLAM) BERBASIS LIGHT DETECTION AND RANGING (LIDAR) SEBAGAI PENUNJANG MATERI PEMBELAJARAN ROBOTIK PADA MAHASISWA TEKNIK ELEKTRO

DEVELOPMENT OF SIMULTANEOUS LOCALIZATION AND MAPPING (SLAM) TEACHING MATERIALS BASED ON LIGHT DETECTION AND RANGING (LIDAR) AS A SUPPORT FOR ROBOTICS LEARNING MATERIALS FOR ELECTRICAL ENGINEERING STUDENTS

Perkembangan teknologi robotika di era modern terus mengalami peningkatan pesat dan telah banyak dimanfaatkan di berbagai sektor. Komponen penting dalam robot yakni sistem navigasi yang berguna agar robot dapat bergerak secara autonomus tanpa harus dikontrol. Salah satu teknologi sistem navigasi pada robot adalah metode navigasi Simultaneous Localization and Mapping (SLAM) berbasis sensor Light Detection and Ranging (LIDAR) yang memungkinkan robot melakukan pemetaan atau survei ukuran ruangan secara real – time dengan cepat dan akurat. Teknologi ini telah diaplikasikan luas dalam berbagai jenis robot, namun bahan ajar yang komprehensif masih terbatas. Penelitian ini bertujuan untuk mengetahui hasil kepraktisan dan keefektifan dari bahan ajar yang telah dikembangkan oleh peneliti. Harapan dari bahan ajar yang dikembangkan dapat meningkatkan pemahaman mahasiswa terhadap sistem navigasi SLAM berbasis LIDAR serta mendorong minat dalam bidang robotika. Penggunaan metode Research and Development (R&D) berbasis model ADDIE diterapkan pada penelitian ini. Model ini memungkinkan peneliti untuk secara sistematis mengembangkan bahan ajar mulai dari tahap Analysis, Design, Development, Implementation, dan Evaluation. Metode eksperimen yang digunakan untuk penelitian ini yaitu One-Shot Case Study. Hasil penelitian menunjukkan bahwa bahan ajar yang dikembangkan memenuhi kriteria sangat praktis dengan rata - rata persentase respon mahasiswa terhadap bahan ajar sebesar 90,1% serta bahan ajar sangat efektif terhadap hasil belajar mahasiswa dalam aspek kognitif maupun psikomotorik. Bahan ajar Simultaneous Localization and Mapping (SLAM) berbasis Light Detection and Ranging (LIDAR) terbukti efektif dan memiliki potensi besar dalam pembelajaran robotika. Untuk optimalisasi, disarankan integrasi simulasi real - time, pemanfaatan platform open source, dan pengembangan modul praktikum bertahap.

The development of robotics technology in the modern era continues to increase rapidly and has been widely used in various sectors. An important component in a robot is a navigation system that is useful so that the robot can move autonomously without having to be controlled. One of the navigation system technologies on robots is the Simultaneous Localization and Mapping (SLAM) navigation method based on Light Detection and Ranging (LIDAR) sensors that allows robots to map or survey room size in real time quickly and accurately. This technology has been widely applied in various types of robots, but comprehensive teaching materials are still limited. This study aims to determine the results of the practicality and effectiveness of the teaching materials that have been developed by researchers. It is hoped that the teaching materials developed can increase students' understanding of LIDAR-based SLAM navigation systems and encourage interest in the field of robotics. The use of the ADDIE model-based Research and Development (R&D) method was applied in this study. This model allows researchers to systematically develop teaching materials starting from the Analysis, Design, Development, Implementation, and Evaluation stages. The experimental method used for this study is the One-Shot Case Study. The results of the study showed that the teaching materials developed met the criteria of being very practical with an average percentage of student responses to teaching materials of 90.1% and teaching materials were very effective on student learning outcomes in cognitive and psychomotor aspects. Simultaneous Localization and Mapping (SLAM) teaching materials based on Light Detection and Ranging (LIDAR) have proven to be effective and have great potential in robotics learning. For optimization, it is recommended to integrate real-time simulations, use of open source platforms, and develop gradual practicum modules.