Isye Arieshanti

Classification of non-proliferative diabetic retinopathy based on hard exudates using soft margin SVM

Diabetic retinopathy is a retinal disease caused by diabetes mellitus. Severity of diabetic retinopathy may lead to blindness. Therefore, early detection of diabetic retinopathy is very important. One of diabetic retinopathy symptoms is the existence of hard exudates. In this study, hard exudates in retinal fundus images are employed to classify the moderate and severe non-proliferative diabetic retinopathy. The hard exudates are segmented using mathematical morphology and the extracted features are classified by using soft margin SVM. The classification model achieves accuracy of 90.54% for 75 training data and 74 testing data of retinal images.

Enriching English into Sundanese and Javanese translation list using pivot language

This paper discusses the problem of sparse translation of English into Sundanese and Javanese that were found in Translator-Gator. Translator-Gator is a language game created by the United Nation Global Pulse, to support the research initiatives in Indonesia. Thousands of keyword were generated and translated from English into some Indonesian local languages using the crowd resource. Unfortunately, many English words are still has no translation in Javanese as well as Sundanese. To overcome this problem we propose a technique to fill the un-translated English words in Javanese and Sundanese using Indonesian translation as a pivot language. Evaluation was made by manually investigated whether each phrase results a proper translation. Experiment shows that our technique results relatively low translation accuracy. Limited coverage of phrase translation list and ambiguous words are identified as causes of translations errors in our technique.

K-medoids algorithm on Indonesian Twitter feeds for clustering trending issue as important terms in news summarization

News summary could be a solution for information access need. However, it is challenging because of the number of news is growth rapidly. The information integration of several news has some difficulties because sentences that compose news summary could be come from various issues. Short text or Twitter Feeds called tweets could be used to recognize those issues. More weight value are given to the issue terms. Hence, the issue terms will exists within the news summary. This paper focuses on the usage of K-Medoids algorithm for tweet clustering. The data in this study is Twitter feeds in Indonesian. The result experiment shows the effect of re-tweet occurrences and also its influence in the summary result.

Analysis of SELDI-TOF-MS Using ε-Support Vector Regression for Ovarian Cancer Identification

The analysis of protein expression profile using SELDI-TOF-MS can assist early detection of ovarian cancer. The chance to save patient’s life is greater when ovarian cancer is detected at an early stage. However, the analysis of protein expression profile is challenging because it has very high dimensional features and noisy characteristic. In order to tackle these limitations, the e-Support Vector Regression model to identify ovarian cancer is proposed. We can show that the performance of the model to discriminate the protein expression profile with cancer disease from the normal ones can reach accuracy 99%, specificity 99% and sensitivity 100%. This result shows that the model is promising for SELDI-TOFMS analysis in Ovarian Cancer identification.