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All Journal Proceeding of the Electrical Engineering Computer Science and Informatics Jurnal ULTIMATICS
Vinnia Kemala Putri
Universitas Indonesia
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering

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Sketch Plus Colorization Deep Convolutional Neural Networks for Photos Generation from Sketches Vinnia Kemala Putri; Mohamad Ivan Fanany
Proceeding of the Electrical Engineering Computer Science and Informatics Vol 4: EECSI 2017
Publisher : IAES Indonesia Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (4979.984 KB) | DOI: 10.11591/eecsi.v4.1040

Abstract

In this paper, we introduce a method to generate photos from sketches using Deep Convolutional Neural Networks (DCNN). This research proposes a method by combining a network to invert sketches into photos (sketch inversion net) with a network to predict color given grayscale images (colorization net). By using this method, the quality of generated photos is expected to be more similar to the actual photos. We first artificially constructed uncontrolled conditions for the dataset. The dataset, which consists of hand-drawn sketches and their corresponding photos, were pre-processed using several data augmentation techniques to train the models in addressing the issues of rotation, scaling, shape, noise, and positioning. Validation was measured using two types of similarity measurements: pixel- difference based and human visual system (HVS) which mimics human perception in evaluating the quality of an image. The pixel- difference based metric consists of Mean Squared Error (MSE) and Peak Signal-to-Noise Ratio (PSNR) while the HVS consists of Universal Image Quality Index (UIQI) and Structural Similarity (SSIM). Our method gives the best quality of generated photos for all measures (844.04 for MSE, 19.06 for PSNR, 0.47 for UIQI, and 0.66 for SSIM).
Klasifikasi Diabetes Menggunakan Model Pembelajaran Ensemble Blending Vinnia Kemala Putri; Felix Indra Kurniadi
Ultimatics : Jurnal Teknik Informatika Vol 10 No 1 (2018): Ultimatics : Jurnal Teknik Informatika
Publisher : Faculty of Engineering and Informatics, Universitas Multimedia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1436.767 KB) | DOI: 10.31937/ti.v10i1.709

Abstract

Diabetes mellitus is one of the deadliest disease and it is increasing in occurrence through the world. This can be prevented by conducting early diagnosis and treatment. However, in developing countries, less than half of people with diabetes are diagnosed correctly which lead to lose of human lives. In this Big Data era, medical databases have enormous quantities of data about their patients. But this medical data may contain noise and a lot of useless information which may mislead the expert in making a decision for medical diagnosis. Data mining is a technique to that is very effective for medical applications for identifying patterns and extracting useful information for databases. This paper proposed a data mining approach using an ensemble blending method to tackle a diabetes prediction problem in Pima Indian Diabetes Dataset. We proposed a blending ensemble classifier approach using a combination of Decision Tree and Logistic Regression as base classifiers, and Support Vector Machine as a top blender classifier. Our approach reached accuracy of 81% and F1-score of 0.81 proves to be higher when compared with basic classifier without combination. Index Terms—diabetes, ensemble, data mining
Gray Level Co-ocurence Matrix untuk Pengekstrasian Ciri Topeng Cirebon Felix Indra Kurniadi; Vinnia Kemala Putri
Ultimatics : Jurnal Teknik Informatika Vol 12 No 1 (2020): Ultimatics : Jurnal Teknik Informatika
Publisher : Faculty of Engineering and Informatics, Universitas Multimedia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (358.663 KB) | DOI: 10.31937/ti.v12i1.1507

Abstract

Cirebon mask is one of the intangible cultural heritage in Indonesia. It is one of the prominent cultural assets from Cirebon and becoming one of the identity Cirebon culture. However, the current condition people tend to forget the cultural asset and lack of help from the government makes the Cirebon mask become the third-rate assets. Our concern lays on the extinction of this Mask. We want to implement digitation and automatic identification using image processing techniques. In this paper, we applied the Gray Level Co-occurrence Matrix for extracting the features. K-Nearest Neighbour as the classifier. The best accuracy of this research is 40,67%
Co-Authors Felix Indra Kurniadi Felix Indra Kurniadi Mohamad Ivan Fanany