Aciek Ida Wuryandari
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Design and Analysis of Hybrid Vessel Monitoring System based on DTN and Internet Collaboration Basuki, Akbari Indra; Wuryandari, Aciek Ida
INKOM Journal Vol 9, No 2 (2015)
Publisher : Pusat Penelitian Informatika - LIPI

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (882.245 KB) | DOI: 10.14203/j.inkom.426

Abstract

In this paper, we propose hybrid Vessel Monitoring System (VMS) design as alternative for current VMS scheme by collaborating internet connection and Disruption-Tolerant-Networks (DTN). The hybrid solution combines offline VMS that use radio networks and online VMS that utilizing satellite-based internet. Hybrid VMS aims to provide a more flexible VMS design and able to speed up delivery process of offline vessel’s data. The concept is both type of vessels must install a standard radio networks for data forwarding. The proposed method to speed up data delivery is by forwarding VMS data from one vessel to another using DTN forwarding scheme. Data can be forwarded to another offline vessel that will return to harbor earlier or to online vessels which have internet connection. Performance measurement is done through simulation analysis using ONE simulator. It aims to measure the speed up data delivery using hybrid VMS implementation compare to a pure offline VMS implementation. Simulation result show that hybrid VMS able to speed up data delivery for offline vessel data in 1.5 up to 2 times faster compare to a pure offline VMS implementation. Hybrid VMS also has advantages in flexible implementation by easily switching between online and offline VMS scheme, according to fisherman financial situation. Spray-and-Wait routing is the most suitable routing algorithm for hybrid VMS according to the efficiency ratio.
AN INTRODUCTION TO KNOWLEDGE-GROWING SYSTEM: A NOVEL FIELD IN ARTIFICIAL INTELLIGENCE Sumari, Arwin Datumaya Wahyudi; Ahmad, Adang Suwandi; Wuryandari, Aciek Ida; Sembiring, Jaka; Widjajati, Farida Agustini
JUTI: Jurnal Ilmiah Teknologi Informasi Vol 8, No 2, Juli 2010
Publisher : Teknik Informatika, ITS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j24068535.v8i2.a313

Abstract

The essential matter of Artificial Intelligence (AI) is how to build an entity that mimics human intelligence in the way of learning of a phenomenon in a real life to gain knowledge of it and uses the knowledge to solve problems related to it. Based on the findings of intelligenct characteristic displayed by the human brain in growing and generating new knowledge by fusing information perceived by sensory organs, we develop brain-inspired Knowledge-Growing System (KGS) that is, a system that is capable of growing its knowledge along with the accretion of information as the time passes. The essential matter of KGS is knowledge-growing method which is based on a new algorithm called Observation Multi-time A3S (OMA3S) information-inferencing fusion method. In this paper we deliver the development of KGS along with some examples of KGS application to a real-life problem. Based on the state-of-the-art of AI and approaches to construct OMA3S method as KG method as well as validations to assess the system performance, we state that brain-inspired KGS is a novel field in AI.