nan Gunawan

Building Synsets for Indonesian WordNet with Monolingual Lexical Resources

This paper presents an approach to build synsets for Indonesian Word Net semi-automatically using monolingual lexical resources available freely in Bahasa Indonesia. Monolingual lexical resources refer to Kamus Besar Bahasa Indoensia or KBBI (monolingual dictionary of Bahasa Indonesia) and Tesaurus Bahasa Indonesia (Indonesian thesaurus). We assume that monolingual resources will play an important role in synsets building, because it will provide more accurate senses specifically for Bahasa. Besides, resources that have been used are produced by Bahasa Indonesia Language Center, which is a government institution that manages Bahasa Indonesia development. However, the synsets retrieved will be considered as a prototype version of Indonesian Word Net synsets.

Acquisition of Hypernymy-Hyponymy Relation between Nouns for WordNet Building

Automatic extraction of hypernym-hyponym pairs has been done in many researches. But none is described as an automatic method to incorporate the result to Word Net or on Word Net building. This paper proposes a method to automatically acquire hypernym-hyponym pairs for Word Net building by utilizing a monolingual dictionary and Lesk Word Sense Disambiguation or Lesk WSD to deliver tagged pairs. This method is implemented on an Indonesian monolingual dictionary and produces 70% accuracy.

GeoJSON Web Service based road assets management system for Surabaya city using mobile GPS

In this research, we have constructed road assets management system for Surabaya, the second biggest city in Indonesia. As reported in this paper, we have managed 607 assets -comprising traffic signs, traffic lights, and road marking- which are spread out at 365 different locations. The availability of mobile apps, which are equipped with camera and Global Positioning System (GPS), allows our proposed system to be implemented for road assets management system in other cities in Indonesia.

Noun phrases extraction using shallow parsing with C4.5 decision tree algorithm for Indonesian Language ontology building

Ontology describes a set of concept or entity and each relation. Ontology as knowledge representation usually has a large structure because it can cover a wide area topics. Ontology building process is divided into two subprocesses, those are term extraction and relation formation. Term extraction in ontology building is done for extracting concept or entity before each relation is obtained. Main objective in this research is to extract noun phrases using shallow parsing algorithm based on C4.5 decision tree as candidate concept or term for ontology building process in Indonesian Text. One of the advantages of using shallow parsing is it can recover syntactic information efficiently and reliably from unrestricted text. For our dataset, we use Indonesian Language online newspapers for one month. Based on our experiments, it concludes that our proposed method can perform well for Indonesian Language noun phrase identification with average F-score 84.63%.

Beyond Lego Mindstorms: Analytical and Experimental Minds

Lego Mindstorms is a robotic tool which perfectly safe to use in the educational environment. Even children and teenager can touch and program it without a fear about the electricity. Because of this, we develop the use of Lego Mindstorms robotic tool to arouse analytical and experimental minds of the students. In this paper we presented the important of thinking and finding ideas and then implementing it to solve the problem given. In fact, there are many things for students to do. Students have to test and change their not-worked solution many times before they find the worked solution and reach the goal. These are what required in constructivist learning and constructionist educational philosophy which is very important and absolutely have a big and positive impact in motivating students to learn and explore the world surrounds them.