Study of Cooperative Control System for Multiple Mobile Robots Using Particle Swarm Optimization

The idea of using multiple mobile robots for tracking targets in an unknown environment can be realized with Particle Swarm Optimization proposed by Kennedy and Eberhart in 1995. The actual implementation of an efficient algorithm like Particle Swarm Optimization (PSO) is required when robots need to avoid the randomly placed obstacles in unknown environment and reach the target point. However, ordinary methods of obstacle avoidance have not proven good results in route planning. PSO is a self-adaptive population-based method in which behavior of the swarm is iteratively generated from the combination of social and cognitive behaviors and is an effective technique for collective robotic search problem. When PSO is used for exploration, this algorithm enables robots to travel on trajectories that lead to total swarm convergence on some target

Buku panduan e-class untuk dosen dan admin

Trend perkembangan teknologi informasi yang semakin pesat dan masuk dalam hampir setiap aktivitas individu dan organisasi, menyebabkan bermunculannya produk-produk teknologi yang dibuat untuk mengakselerasi penyelesaian masalah yang semakin kompleks. Berbagai dampak yang ditimbulkannya menyebabkan dunia pendidikan ikut merespon dan memanfaatkan kemajuan teknologi informasi tersebut. Semoga dengan adanya EClass ini dapat meningkatkan kualitas dan memajukan Pendidikan di Universitas Merdeka Malang di tingkat Nasional maupun Internasional

FORECASTING THE OCCUPANCY RATE OF STAR HOTELS IN BALI USING THE XGBOOST AND SVR METHODS

The hotel occupancy rate indicator has become a concern in recent years as it goes hand in hand with the rapid growth of the global tourism industry. A way to maintain or even improve this indicator is to carry out managerial planning using forecasting methods. The forecasting methods used in this research are XGBoost and SVR. The advantage of this modelling is that it achieves high accuracy and processing speed. Meanwhile, the benefit of SVR is that it will produce good prediction because can overcome overfitting. The steps in this research are exploring data, separating training data and testing data, transforming data, modelling data, forecasting data, and evaluating forecasting results using RMSE, MAE, and MAPE. The results show that MAPE value from both methods is smaller than 10%, which means that both methods can predict the occupancy rate of star hotels in Bali very accurately. Apart from that, the SVR method has smaller values for all model evaluation criteria than the XGBoost method, which means that the SVR method is better than XGBoost for predicting the occupancy rate of star hotels in Bali

Pendeteksi Wajah Secara Real Time pada 2 Degree of Freedom (DOF) Kepala Robot Menggunakan Deep Integral Image Cascade

Abstrak— Teknologi Robot merupakan karya terbaik yang sangat penting bagi kehidupan manusia modern saat ini untuk mempermudah semua pekerjaan manusia. Perkembangan dunia robot saat ini akan difokuskan pada robot yang memiliki fitur mirip manusia. Bahkan diharapkan memiliki kemampuan berinteraksi dan berperilaku seperti manusia yaitu robot humanoid, mekanisme dari gerakan robot humanoid memiliki derajat kebebasan Degree of Freedom (DOF). Layaknya pada manusia robot diberi kemampuan penglihatan untuk mendeteksi adanya objek yang ditangkap secara real time Penelitian kepala robot 2 degree of freedom (DOF) untuk pendeteksi wajah secara real time menggunakan metode Deep Integral Image Cascade untuk deteksi wajahnya. Untuk keakurasian pendeteksi wajah dengan real time pada penelitian ini dengan pengujian akurasi terbesar adalah 95,25% dengan waktu respons pendeteksi tercepat 7 detik dengan waktu terlama 8,55 second rata-rata data citra semuanya tidak terdeteksi dengan benarKata kunci: Raspberry pi, Pendeteksi wajah, Degree of freedom, Haar cascade classifier, Robot kepalaAbstract— Robot technology is the best work that is very important for modern human life today to facilitate all human work. The development of the robot world today will be focused on being a robot that has human-like features. Even expected to have the ability to interact and behave like a humanoid robot, the mechanism of humanoid robot movement has a degree of freedom of Degree of Freedom (DOF). Like in the robot man is given the ability of vision to detect the presence of objects captured in real time robotic head Research 2 degree of freedom (DOF) for face detection in real time using the Deep Integral Image Cascade method to Face Detection.  For the real-time accuracy of the face detector in this research with the greatest precision testing is 95.25% with the fastest detection response time of 7 seconds with the oldest time 8.55 second the average image data everything is not detected with Really.Keywords: Raspberry Pi, face detector, Degree of freedom, Haar Cascade classifier, Robot hea