Analisa dan Desain Maximum Power Point Tracking untuk Generator Induksi pada Aplikasi Sepeda Listrik

Currently, vehicle transportation is becoming a very important tool in helping humans. Therefore an increase in the volume of the vehicle is very high every year. With so many current vehicles could damage the air cleaner in nature, caused by the combustion of residual Fuel (Fuel oil) the vehicles that emit fumes. Therefore it needs an alternative environment-friendly vehicles with electric bicycles. Electric vehicles currently using permanent magnet motors as their motive. On this research the drivers of electric vehicles use the induction generator who has the advantages i.e. a constant round of and excitation does not need another motor as a driving force. This vehicle is also installed on a system power savings, power saving methods is improved significantly. One of them is MPPT (Maximum Power Point Tracking) used in induction generators. MPPT design is modelled using Matlab/Simulink software. The Matlab simulation has manage to obtain 55.04 volts of MPPT. While the generator output voltage without using the MPPT is amount of 40.84 volts. By using perturb and observe algorithm, it is possible to search the optimum power of generator

Merdeka Belajar Merdeka Mengajar

Kebijakan pemerintah “Merdeka Belajar, Kampus Merdeka” tentu menimbulkan respon tersendiri bagi para dosen selaku akademisi bagaimana menyikapi, merencanakan, menyusun dan mengimplementasikan sistem dan model belajar yang paling sesuai dengan kebijakan tersebut. Dan di buku Antologi inilah akan dijumpai berbagai pandangan, pemikiran, dan juga mungkin gambaran usulan untuk mengimplementasikan kebijakan MBKM di era informasi teknologi yang sangat cepat berubah dewasa ini. Sebagai pendidik profesional, para dosen tentu memiliki kiat dan cara tersendiri untuk bisa menghasilkan output lulusan peserta didik yang benar-benar sesuai dengan tujuan dan target kebijakan MBKM tersebu

An Automated Platform for Gathering and Managing Open-Source Cyber Threat Intelligence

The community has begun paying more attention to source OSCTI Cyber Threat Intelligence to stay informed about the rapidly changing cyber threat landscape. Numerous reports from the OSCTI frequently provide Information about dangers. However, current OSCTI gathering and management tools have mainly concentrated on individual minor compromise indicators, despite the urgent need for high-quality OSCTI. The relationship between higher-level notions (including the strategies, methods, and processes) and the connections between them, which hold crucial Information about dangerous behaviors and are crucial to revealing the full dangerous situation, have been disregarded. Therefore, we present SecurityKG, an automated OSCTI collection and administration system. SecurityKG collects OSCTI to extract high-fidelity knowledge about threat behaviours to address the void. Using a mixture of AI and NLP approaches, a security know-how graph is then constructed from a wide variety of sources. To facilitate knowledge graph exploration, SecurityKG provides a user interface (UI) that supports multiple forms of interactivity