Suchergebnisse

Quick-and-accurate decision is a primary requirement for achieving a mission's successfulness in many organizations whether they are militaries or civilians. The larger and more complex the scope of a mission the longer the time will be needed to perform decision-making. A complex mission such as retaining a sustainable mission of aircrafts deployed in certain remote bases will require a 24-hour of standby on-site or off-site maintenance teams. The most common problem arisen from humans in such situations is the fatigue that can occur anytime to any of them during a continuing mission and of course, it can affect the continuity of the mission and delay or disappoint the mission achievement. In this paper we address the concept of the utilization of Multiagent Collaborative Computation (MCC) paradigm in supporting the decision-making process in aircrafts maintenance during a sustainable mission in remote bases. The ultimate goal of the MCC-based aircraft maintenance system is comprehensive information regarding the maintenance procedures in order to keep the aircrafts serviceable during the mission implementation. In this paper we also present the technique to obtain the comprehensive information as the basis for decision-making called A3S (Arwin-Adang-Aciek-Sembiring) information-inferencing fusion method

A problem that has been faced by the Radar is if the aircraft flies at low level or near to the surface so its coming in the aerial-surveillance airspace cannot be detected and endangers the air sovereignty. The aircraft can be recognized and identified by carrying out a technique called Visual Aircraft Recognition (VACR) using a binocular. This technique requires military personnel that has capability carrying out the air surveillance from the ground. Surveillance is a time-consuming and tiring task so it can cause fatigue and impact to the results of the recognition and identification. To cope with this problem, we have designed and implemented a novel recognition and identification method using the combination of Naive Bayes Classifier (NBC) and information fusion. By using a dataset that consists of 45 military aircrafts, 35 civilian aircrafts, 40 military helicopters, and 35 civilian helicopters with 80:20 dataset distribution for the training scheme and the validation one, we obtained the recognition accuracy of 87.1%. We also found that the recognition and identification process can be speeded up 1.2 seconds when using information fusion.

Proyek kolaborasi pengembangan Korean Fighter Experiment/Indonesian Fighter Experiment (KFX/IFX) Joint Development antara Indonesia dan Korea Selatan dibangun atas dasar keinginan Korea Selatan dan Indonesia untuk membangun kemandirian industri pertahanan di tengah keberadaan negara-negara besar dalam laju pengembangan alat utama sistem persenjataan dunia. Proyek ini pada prosesnya mengalami hambatan dan tarik ulur di antara kedua negara. Tujuan artikel ini adalah untuk menunjukkan bagaimana hambatan tersebut dapat terjadi. Artikel ini menggunakan kerangka teoretis ekonomi kolaborasi dalam proses akusisi pertahanan secara makro. Pendekatan yang digunakan adalah kualitatif untuk menganalisis secara deskriptif relasi Military-Industrial Complex (MIC) dalam proyek KFX/IFX. Hasil yang dicapai dalam penelitian ini adalah pemetaan relasi antara Indonesia-Korea Selatan-Amerika Serikat sebagai segitiga besi pertahanan internasional dalam proyek KFX/IFX.