Sugianela, Yuna
Faculty of Computer Science - Universitas Indonesia

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CHARACTER IMAGE SEGMENTATION OF JAVANESE SCRIPT USING CONNECTED COMPONENT METHOD Sugianela, Yuna; Suciati, Nanik
Jurnal Ilmu Komputer dan Informasi Vol 12, No 2 (2019): Jurnal Ilmu Komputer dan Informasi (Journal of Computer Science and Informatio
Publisher : Faculty of Computer Science - Universitas Indonesia

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Abstract

Automation of Javanese script translation is needed to make it easier for people to understand the meaning of ancient Javanese script. By using Javanese script image as input, the translation system generally consists of character segmentation, character recognition, and combining the recognized characters as a meaningful word. The segmentation which obtains region of interest of each character, is an important process in the translation system. In the previous research, segmentation using projection profile method can separate each character well. The method can overcome characters overlapping, but it still produces truncated characters. In this study, we proposed a new segmentation to reduce the truncated character. The first step of the proposed method is pre-processing that consists of converting input into binary image and cleaning noises. The next step is to determine the connected component labels, which further perform as candidate of characters. Some of the candidates are still represented by more than one labels, so that we need a process to merge the connected component labels that have centroid distance less than threshold. We evaluate the proposed method using Intersection over Union (IoU). The evaluation shows the best accuracy 93,26%.
EEG CLASSIFICATION FOR EPILEPSY BASED ON WAVELET PACKET DECOMPOSITION AND RANDOM FOREST Sugianela, Yuna; Sutino, Qonita Luthfia; Herumurti, Darlis
Jurnal Ilmu Komputer dan Informasi Vol 11, No 1 (2018): Jurnal Ilmu Komputer dan Informasi (Journal of Computer Science and Informatio
Publisher : Faculty of Computer Science - Universitas Indonesia

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Abstract

EEG (electroencephalogram) can detect epileptic seizures by neurophysiologists in clinical practice with visually scan long recordings. Epilepsy seizure is a condition of brain disorder with chronic noncommunicable that affects people of all ages. The challenge of study is how to develop a method for signal processing that extract the subtle information of EEG and use it for automating the detection of epileptic with high accuration, so we can use it for monitoring and treatment the epileptic patient. In this study we developed a method to classify the EEG signal based on Wavelet Packet Decomposition that decompose the EEG signal and Random Forest for seizure detetion. The result of study shows that Random Forest classification has the best performance than KNN, ANN, and SVM. The best combination of statisctical features is standard deviation, maximum and minimum value, and bandpower. WPD is has best decomposition in 5th level.
Rancang Bangun Pixel Art Converter Menggunakan Segmentasi berbasis K-means Clustering Sugianela, Yuna; Suciati, Nanik; Bagus A.R, Maulidan
Jurnal Teknik ITS Vol 6, No 2 (2017)
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat (LPPM), ITS

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Abstract

Pixel art merupakan jenis aset grafis yang digunakan pada game. Untuk mengefisiensi pekerjaan pada industri game, diperlukan sebuah converter citra raster biasa menjadi pixel art. Tahapan dalam membangun aplikasi pixel art converter adalah mengubah warna menjadi aturan yang berlaku pada pixel art, lalu membuat tepi gambar menjadi jaggy yang merupakan ciri khas grafis pixel art. Algoritma segmentasi yang digunakan merupakan segmentasi berbasis k-means clustering. Segmentasi ini berguna untuk mengubah warna citra menjadi lebih sederhana. Citra hasil dari segmentasi k-means kemudian diolah menjadi citra yang memiliki tepian bergerigi atau jaggy yang merupakan ciri khas utama dari pixel art. Aplikasi dinyatakan telah memenuhi kebutuhan Tim Desain Maulidan Games. Kualitas citra hasil pixel art converter dipengaruhi oleh ukuran citra input, parameter nilai K untuk k-means clustering serta skala grid untuk membuat tepian jaggy. Nilai optimal yang digunakan untuk membuat pixel art yang baik yaitu, untuk citra gradient color nilai optimal nilai K untuk K-means adalah 25, sedangkan untuk citra flat color menggunakan nilai K 16, nilai skala grid untuk membuat tepian adalah 80, ukuran citra optimal adalah 100 x 100 piksel.
Javanese Document Image Recognition using Multiclass Support Vector Machine Sugianela, Yuna; Suciati, Nanik
CommIT (Communication and Information Technology) Journal Vol 13, No 1 (2019): CommIT Vol. 13 No. 1 Tahun 2019 (In Press)
Publisher : Bina Nusantara University

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Abstract

Some ancient documents in Indonesia are written in the Javanese script. Those documents contain the knowledge of history and culture of Indonesia, especially about Java. However, only a few people understand the Javanese script. Thus, the automation system is needed to translate the document written in the Javanese script. In this study, the researchers use the classification method to recognize the Javanese script written in the document. The method used is the Multiclass Support Vector Machine (SVM) using One Against One (OAO) strategy. The researchers use seven variations of Javanese script from the different document for this study. There are 31 classes and 182 data for training and testing data. The result shows good performance in the evaluation. The recognition system successfully resolves the problem of color variation from the dataset. The accuracy of the study is 81.3%.