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"Sejak krisis ekonomi di Asia Tenggara tahun 1997-1998, Indonesia mengubah sistem nilai tukar dari sistem mengambang terkendali (managed floating exchange rate) menjadi sistem mengambang bebas (free floating exchange rate). Dengan penerapan sistem free floating rate, maka nilai tukar rupiah menjadi lebih fluktuatif. Sehubungan dengan tingginya exchange rate pass through di Indonesia dan ITF yang diterapkan di Indonesia, intervensi pada nilai tukar pun diperlukan. Intervensi bank sentral dalam pasar valuta asing tersebut merupakan salah satu tanda suatu negara melakukan fear of floating. Studi ini meneliti apakah benar praktek fear of floating terjadi di Indonesia. Pengujian ini dilakukan dengan menggunakan model OLS yang diadopsi dari model Frankel dan Wei (1994).

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Indonesian government has changed its exchange rate system from managed floating exchange rate to free floating exchange rate since the economic crisis hit most of the south east asian country in 1997-1998. This has led the exchange rate of Indonesian rupiah to became more fluctuatif against other currency. As exchange rate pass through is higher and the application of ITF, the exchange rate intervention by the central bank is needed to secure rupiah against other currency. This method, known as Fear Floating, is the method that used by country which applied the central bank?s intervention to the foreign exchange market. This study, using OLS model which is adapted from Frankel and Wei (1994), reveals the detail of whether Fear of Floating method is applied within Indonesian monetary system."

"This study explores the financing structures, ownership structures, and governance mecha-nisms of five automakers within two business groups (i.e., the Toyota Group in Japan and the Hyundai Motor Group in South Korea). By analyzing the five automakers’ financing structures and ownership structures, this study calls attention to the weakening influence of outsiders who are otherwise supposed to function as professional and systematic supervisors in monitoring corpo-rate management. We assign great importance to each of their governing organizations—which practically take the place of the monitoring function—and attempt to analyze their governing orga-nizations, with a focus on composition, size, and the substantive role of organizational members, inter alia. Furthermore, our findings show that the original role expected of outsiders as members of the governing organization was diminished by the influence of conventional factors (i.e., social or corporate conventions"

"Open Access in Theory and Practice investigates the theory-practice relationship in the domain of open access publication and dissemination of research outputs.Drawing on detailed analysis of the literature and current practice in OA, as well as data collected in detailed interviews with practitioners, policymakers, and researchers, the book discusses what constitutes ‘theory’, and how the role of theory is perceived by both theorists and practitioners. Exploring the ways theory and practice have interacted in the development of OA, the authors discuss what this reveals about the nature of the OA phenomenon itself and the theory-practice relationship.Open Access in Theory and Practice contributes to a better understanding of OA and, as such, should be of great interest to academics, researchers, and students working in the fields of information science, publishing studies, science communication, higher education policy, business, and economics. The book also makes an important contribution to the debate of the relationship between theory and practice in information science, and more widely across different fields of the social sciences and humanities"

"Senjata genggam kelas pistol, shoutgun, dan rifle sering digunakan dalam kegiatan kriminal. Sering kali objek senjata-senjata tersebut yang terekam sulit terdeteksi pada keramaian, dikarenakan pengawasan masih dilakukan dengan mata telanjang. Proses deteksi senjata-senjata tersebut pada rekaman bisa dibantu dengan menggunakaan Deep Learning. Dalam hal ini penulis mengusulkan menggunakan Deep Learning untuk mendeteksi senjata dan menentukan jenis senjata api yang terdeteksi. Penelitian ini bertujuan untuk mengimplementasikan Deep Learning pada robot deteksi senjata api berenis handgun, rifle, dan shotgun. Algoritma Deep Learning yang digunakan yaitu YOLO dan EfficientDet. YOLO merupakan salah satu metode pendeteksian objek tercepat dan akurat, mengungguli algoritma pendeteksian lainnya. Namun, algoritma YOLO memerlukan arsitektur komputer yang berat. Oleh karena itu YOLOv3-tiny dan YOLOv4-tiny, versi YOLOv3 yang lebih ringan, dapat menjadi solusi untuk arsitektur yang lebih kecil. Penulis menggunakan 3 versi YOLO yaitu YOLOv3-tiny, YOLOv4-tiny, dan YOLOv7. YOLOv -tiny memiliki FPS tinggi, yang seharusnya akan menghasilkan kinerja lebih cepat. Karena YOLOv-tiny adalah versi modifikasi dari YOLO versi aslinya, maka akurasinya meningkat, dan YOLOv3 sudah mengungguli SSD dan R-CNN yang lebih cepat. Sedangkan YOLOv7 sebagai versi modifikasi terbaru dari YOLO diuji performanya lebih baik dari YOLO versi yang lainnya atau tidak. Selain itu penulis menggunakan algoritma pendeteksian lainnya yaitu EfficientDet untuk pengujian apakah YOLO mengungguli algoritma pendeteksian lainnya. Tujuan lain dari penelitian ini yaitu untuk mengetahui performa training model Deep Learning terbaik yang akan diimplementasikan pada robot deteksi senjata api yang telah dirancang. Robot yang dirancang menggunakan Single Board Computer (SBC) yaitu Raspberry Pi model 4B yang kemudian didesain hingga berbentuk robot mars rover. Studi ini menemukan bahwa model YOLOv4-tiny adalah model Deep Learning yang diaplikasikan ke robot karena hasil training model ini menggungguli dari pada hasil training model Deep Learning lainnya. Nilai parameter hasil training model YOLOv4-tiny antara lain yaitu: mAP 82%, F1 score 78%, dan Avg. loss 0.74. Dengan demikian, studi ini juga berhasil megimplementasikan Deep Learning berbasis YOLO pada robot deteksi senjata api dengan nilai confidence pendeteksian rata-rata 99%. serta berhasil mengklasifikasi kelas jenis senjata yang terdeteksi.

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Pistol, shoutgun and rifle class handheld weapons are often used in criminal activities. Often the objects of these weapons that are recorded are difficult to detect in the crowd, because monitoring is still carried out with the naked eye. The process of detecting these weapons in recordings can be assisted by using Deep Learning. In this case the author proposes using Deep Learning to detect weapons and determine the type of firearm detected. This research aims to design a weapon detection and weapon type classification tool based on Deep Learning algorithms. The Deep Learning algorithms used are YOLO and EfficientDet. YOLO is one of the fastest and most accurate object detection methods, outperforming other detection algorithms. However, the YOLO algorithm requires heavy computer architecture. Therefore YOLOv3-tiny and YOLOv4-tiny, lighter versions of YOLOv3, can be a solution for smaller architectures. The author uses 3 versions of YOLO, namely YOLOv3-tiny, YOLOv4-tiny, and YOLOv7. YOLOv -tiny has a high FPS, which should result in faster performance. Because YOLOv-tiny is a modified version of the original YOLO, its accuracy is improved, and YOLOv3 already outperforms faster SSDs and R-CNN. Meanwhile, YOLOv7 as the latest modified version of YOLO is tested whether its performance is better than other versions of YOLO or not. Apart from that, the author uses another detection algorithm, namely EfficientDet, to test whether YOLO outperforms other detection algorithms. Another aim of this research is to determine the performance of the best training model that will be applied to the tool that has been designed. The tool designed using a Single Board Computer (SBC), namely the Raspberry Pi model 4B, was then designed to take the form of a Mars rover robot. This study found that the YOLOv4-tiny model is a Deep Learning model that is applied to robots because the training results of this model are superior to the training results of other Deep Learning models. The parameter values resulting from the YOLOv4-tiny model training include: mAP 82%, F1 score 78%, and Avg. loss 0.74. Thus, this study also succeeded in designing a weapon detection and weapon type classification tool based on a Deep Learning algorithm with an average detection confidence value of 99%. and succeeded in classifying the class of weapons detected."