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Journal : JURNAL TEKNOLOGI TECHNOSCIENTIA

MODEL INTEROPERABILITAS ANTAR APLIKASI E-GOVERNMENT Istiyanto, Jazi Eko; Sutanta, Edhy
JURNAL TEKNOLOGI TECHNOSCIENTIA Technoscientia Vol 4 No 2 Februari 2012
Publisher : Lembaga Penelitian & Pengabdian Kepada Masyarakat (LPPM), IST AKPRIND Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34151/technoscientia.v4i2.501

Abstract

Interoperability between information systems is an urgent problem to be solved in the development of e-Gov in Indonesia. This is caused by the need for increasing the data multisectoral policy-making in order to solve problems involving data from the inter-related sectors. While the state government application in the current environment, are still largely sectoral, in isolation, can not communicate with each other, and heterogeneous. Interoperability between e-Gov applications become important things to be sought the solution to the problem of developing e-Gov in Indonesia are not protracted. This paper is a review of the literature reveals the development of e-Gov in Indonesia, the interoperability problems encountered, and how the model of interoperability between e-Gov built to implement a web services models.
STUDI KOMPARASI ALGORITMA HIERARCHICAL DAN PARTITIONAL UNTUK CLUSTERING DOKUMEN TEKS BERBAHASA INDONESIA Hamzah, Amir; Susanto, Adhi; Soesianto, F; Istiyanto, Jazi Eko
JURNAL TEKNOLOGI TECHNOSCIENTIA Academia Ista Vol 12 No 01 Agustus 2007
Publisher : Lembaga Penelitian & Pengabdian Kepada Masyarakat (LPPM), IST AKPRIND Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34151/technoscientia.v0i0.1972

Abstract

Text document clustering is a technique which has been intensively studied be-cause of its important role in the text-mining and information retrieval. In the vector spa-ce model it is typically known two main clustering approaches,i.e. hierachical algorithm and partitional algorithm. The hierarchical algorithm produces deterministic result known as a dendogram, but its weakness is high complexity in time and memory. On the other hand, partitiaonal algorithm has linear time and memory complexity although its clustering result is not independent from its initial cluster. The aim of this research was to study experimentally to compare the perfor-mances of several techniques of hierarchical algorithms and partitional algorithms applied to text documents written in Bahasa Indonesia. The five similarity techniques i.e. UPGM-A, CSI, IST,SL and CL were chosen from hierarchical, whereas K-Means, Bisecting K-Mean and Buckshot are chosen for partitonal ones. The documents were collected from 200 to 800 Indonesian news text that have been categorized manually and used to test these algorithms using F-measure for clustering performance. This value was derived from Recall and Precision and can be used to measure the performance of the algorithms to correctly classify the collections. Results showed that Bisecting K-Mean as a variant of partitional algorithm performed comparably with the two best hierarchical techniques,i.e. UPGMA and CL but with much lower time complexity.