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Journal : Jurnal Nasional Teknik Elektro dan Teknologi Informasi

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Pengelompokan Artikel Berbahasa Indonesia Berdasarkan Struktur Laten Menggunakan Pendekatan Self Organizing Map Akhmad Zaini; M. Aziz Muslim; Wijono
Jurnal Nasional Teknik Elektro dan Teknologi Informasi Vol 6 No 3: Agustus 2017
Publisher : Departemen Teknik Elektro dan Teknologi Informasi, Fakultas Teknik, Universitas Gadjah Mada

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Abstract

Document grouping is a necessity among a large number of articles published on internet. Several attempts have been done to improve this grouping process, while majority of the efforts are based on word appearance. In order to improve its quality, the grouping of documents need to be based on topic similarity between documents, instead of the frequency of word appearance. The topic similarity could be known from its latency, since the similarity of the word interpretation are often used in the same context. In the unsupervised learning process, SOM is often used, in which this approach simplifies the mapping of multi-dimension data. This research result shows that implementation of the latent structure decreases characteristic dimension by 32% of the word appearance, hence makes this approach more time efficient than others. The latent structure, however, when implemented on SOM Algorithm, is capable to obtain good quality result compared to word appearance frequency approach. It is then proven by 5% precision improvement, recall improvement of 3%, and another 4% from F-measure. While the achievement is not quite significant, the quality improvement is able to put the dominance of grouping process, compared to the original classification defined by the content provider.
Metode Flyback pada Pembangkitan Tegangan Tinggi untuk Aplikasi Plasma Electrolytic Oxidation Kumala Mahda Habsari; Wijono; D.J. Djoko H.S.
Jurnal Nasional Teknik Elektro dan Teknologi Informasi Vol 6 No 3: Agustus 2017
Publisher : Departemen Teknik Elektro dan Teknologi Informasi, Fakultas Teknik, Universitas Gadjah Mada

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Abstract

Flyback is one of high voltage generation methods using a low voltage source. This method has a simple circuit, which consists of two main components for generating the high voltage. In this study, flyback method is used to generate high voltage on plasma electrolytic oxidation (PEO) application. PEO is a process that combine electrochemical oxidation process and high voltage spark. This application needs high voltage to produce plasma. The plasma is used to form a new surface coating on metal. Flyback circuit is succesfully simulated on LTSpice IV. Voltage value and waveform on simulation has been observed and compared with the real one. The measured and observed part is IGB gate, output voltage of transformer before diode, and load voltage after diode. Flyback effect and waveform on simulation has the similiar result with the real one. A 10 volt input voltage can produce output voltage on the average of 1 kilovolt. Therefore, flyback simulation is able to represent flyback ability on real circuit for generating high voltage which can be used on high voltage generation for PEO application.
Pengaruh Phrase Detection dengan POS-Tagger terhadap Akurasi Klasifikasi Sentimen menggunakan SVM Hermawan Arief Putranto; Onny Setyawati; Wijono
Jurnal Nasional Teknik Elektro dan Teknologi Informasi Vol 5 No 4: November 2016
Publisher : Departemen Teknik Elektro dan Teknologi Informasi, Fakultas Teknik, Universitas Gadjah Mada

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Abstract

Sentiment analysis or opinion mining, which is one of the application of Natural Language Processing (NLP), aims to find a method to facilitate human in communicating with a computer using their common language. To simplify the process of understanding human language, there are three important stages that must be carried out by a computer, which are tokenizing, stemming and filtering. The tokenizing that breaks down the sentence into a single word will make the computer assume all words (token) are the same. If there is a phrase formed from one of unimportant words, which is happened to be in the stoplist, the phrase will be deleted. Solution for the aforementioned problem is tokenizing based on phrase detection using Hidden Markov Model (HMM) POS-Tagger to improve classification performance using Support Vector Machine (SVM).With this approach, computer will be able to distinguish a phrase from others, then store the phrase into a single entity. There is an increase in accuracy by approximately 6% on Dataset I and 3% on Dataset II in the classification process using phrase detection, due to reduction of missing features that usually occurs in the filtering process. In addition, the detection of the phrase-based approach also produces the most optimal classification model, as seen from the ROC value that reaches 0.897.
Co-Authors Akhmad Zaini D.J. Djoko H.S. Dary Rafi Brafianto Habsari, Kumala Mahda Hermawan Arief Putranto M. Aziz Muslim Onny Setyawati Tri Nurwati