Judul: Implementasi Algoritme Ant Colony Optimization pada Swarm UAV Crazyflie untuk Menyelesaikan Masalah Multiple Travelling Salesman Problem
Abstrak: Penggunaan teknologi swarm robotics saat ini semakin berkembang, salah satunya adalah penggunaan swarm UAV. Salah satu permasalahan yang kerap muncul adalah Multiple Travelling Salesman Problem (MTSP), yakni suatu masalah pencarian rute pada sejumlah swarm UAV yang terlibat untuk mengunjungi sejumlah target tertentu tepat satu kali dan kembali ke posisi awal dengan biaya perjalanan minimum. Ant Colony Optimization (ACO) merupakan salah satu pendekatan yang dapat digunakan untuk menyelesaikan permasalahan MTSP, namun belum banyak penelitian yang mengimplementasikan algoritme ACO pada swarm UAV di lingkungan nyata. Penelitian ini bertujuan untuk mengimplementasikan algoritme ACO pada swarm UAV berjenis Crazyflie untuk menyelesaikan masalah MTSP. Pergerakan swarm UAV akan disimulasikan pada simulator Crazyswarm, lalu diimplementasikan secara real menggunakan Crazyflie. Hasil penelitian yang dilakukan menunjukkan bahwa algoritme ACO yang diimplementasikan pada Crazyflie telah berhasil mengunjungi target-target yang diharapkan dengan meminimumkan jarak tempuh. Selain itu, hasil penelitian juga menunjukkan error posisi antara hasil simulasi dan hasil aktual yang cukup rendah, yakni kurang dari 5 cm secara rata-rata pada setiap sumbu koordinat x, y, dan z., The use of swarm robotics technology is currently growing and one of them is the use of UAV swarm. One of the problems that often arises is the Multiple Traveling Salesman Problem (MTSP), which is a route-finding problem for a number of UAV swarms involved to visit a certain number of targets exactly once and return to the initial position with minimum travel costs. Ant Colony Optimization (ACO) is one approach that can be employed to solve MTSP problems, but not many studies have implemented the ACO algorithm on UAV swarms in the real environment. This study aims to implement the ACO algorithm on the Crazyflie type UAV swarm to solve the MTSP problem. The movement of the UAV swarm will be simulated on the Crazyswarm simulator, then implemented in real-time using Crazyflie. The result of our study showed that the ACO algorithm implemented on Crazyflie has succeeded in visiting the expected targets by minimizing the distance. Simulation results also showed that we obtain a relatively low error between the simulation results and the actual results, which is less than 5 cm on average on each x, y, and z coordinates.
Keyword: Swarm UAV, Crazyflie, ACO Algorithm, MTSP

Judul: Crazyswarm and Lighthouse Positioning System Configuration to Support Multi-UAV Algorithm Implementation
Abstrak: Sistem multi-UAV saat ini banyak digunakan dalam berbagai bidang, termasuk pertanian. Salah satu algoritma terpenting untuk mengimplementasikan sistem multi-UAV adalah algoritma koordinasi dan algoritma pengalokasian tugas (task allocation). Sebelum algoritma-algoritma tersebut dapat diimplementasikan, sistem multi-UAV perlu dikonfigurasi terlebih dahulu agar UAV dapat berkoordinasi satu sama lainnya dan juga dengan central station yang mengendalikannya. Penelitian ini bertujuan untuk mengonfigurasi sistem Multi-UAV Crazyflie dan perangkat lunak Crazyswarm dalam lingkungan laboratorium. Setelah sistem Multi-UAV berhasil dikonfigurasi, sistem tersebut diuji dengan algoritma modifikasi Ant Colony Optimization (ACO) dan K-Means. Pengujian dilakukan dengan menerbangkan swarm Crazyflie ke posisi yang diperoleh oleh algoritma berdasarkan skenario yang telah ditentukan. Setelah mencapai posisi yang ditentukan, masing-masing Crazyflie akan terbang hover selama 5 detik di atas target. Posisi nyata Crazyflie ketika terbang hover ini kemudian dibandingkan dengan posisi luaran algoritma. Saat pengujiannya, Crazyswarm tidak bisa mendapatkan seluruh data posisi Crazyflie jika hanya menggunakan satu buah Crazyradio. Oleh karena itu, tiga buah Crazyradio serta tiga channel komunikasi berbeda digunakan supaya Crazyswarm dapat mengambil data posisi seluruh Crazyflie. Hasil pengujian menunjukkan bahwa nilai rata-rata error posisi UAV adalah -0.084 ± 0.033 cm untuk sumbu X, -0.251 ± 0.2 cm untuk sumbu Y, dan 0.01 ± 0.09 cm untuk sumbu Z., Multi-UAV systems are now widely used in various fields, including agriculture. One of the most important algorithms for implementing multi-UAV systems is the coordination and task allocation algorithms. Before these algorithms can be implemented, multi-UAV system needs to be configured in order to coordinate with each other and also with the central station that controls the system. This research aims to configure the Crazyflie Multi-UAV system and Crazyswarm software in a laboratory environment. After the Multi-UAV system was successfully configured, it is then tested with a modified Ant Colony Optimization (ACO) and K-Means algorithm. The testing was conducted by flying the Crazyflie swarm to the position obtained by the algorithm based on a predefined scenario. After reaching the specified position, each Crazyflie will hover for 5 seconds above the target. The real position of the Crazyflie when hovering is then compared with the algorithm's output position. While testing, Crazyswarm could not obtain all Crazyflie position data when using only one Crazyradio. Therefore, three Crazyradio and three different communication channels were used in order for Crazyswarm to retrieve the position data of all Crazyflies. The test result shows that the average value of UAV position error is -0.084 ± 0.033 cm for X axis, -0.251 ± 0.2 cm for Y axis, and 0.01 ± 0.09 cm for Z axis.
Keyword: ACO, configuration, Crazyflie, Crazyswarm, multi-UAV

Judul: Karakteristik Kimia Lignin Sepuluh Jenis Kayu.
Abstrak: Lignin is a chemical component of wood which was concerned in relation to pulping process. The content and reactivity of lignin affect the easiness of delignification in the pulping process. This research aimed to analyze the chemical characteristics of lignin in the ten wood species; which were expressed as total lignin, Klason lignin, acid-soluble lignin, and syringil/guiacyl ratio. Total lignin was defined as a sum of Klason and acid-soluble lignin. Lignin content varied widely between wood species. Klason lignin was 20.51-34.3% and acid-soluble lignin was 0.31-2.8%. Wood species having lower lignin content tend to have higher acid-soluble lignin and syringil/guaiacyl ratios. Consequently, there was clear and positive correlation between acid-soluble lignin and syringil/guaiacyl ratio (R2=0.92)
Keyword: acid soluble lignin, delignification, Klason lignin, monomers lignin, reactivity