Masayu Leylia Khodra
Institut Teknologi Bandung

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Toward a New Approach in Fruit Recognition using Hybrid RGBD Features and Fruit Hierarchy Property Rachmawati, Ema; Supriana, Iping; Khodra, Masayu Leylia
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 (1220.177 KB) | DOI: 10.11591/eecsi.v4.1029

Abstract

We present hierarchical multi-feature classification (HMC) system for multiclass fruit recognition problem. Our approach to HMC exploits the advantages of combining multimodal features  and  the  fruit  hierarchy  property.  In  the construction of hybrid features, we take the advantage of using color feature in the fruit recognition problem and combine it with 3D shape feature of depth channel of RGBD (Red, Green, Blue, Depth) images. Meanwhile, given a set of fruit species and variety, with a preexisting hierarchy among them, we consider the problem of assigning images to one of these fruit variety from the point of view of a hierarchy. We report on computational experiment using this approach. We show that the use of hierarchy structure along with hybrid RGBD features can improve the classification performance.
Hierarchical multi-label news article classification with distributed semantic model based features Ivana Clairine Irsan; Masayu Leylia Khodra
International Journal of Advances in Intelligent Informatics Vol 5, No 1 (2019): March 2019
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v5i1.168

Abstract

Automatic news categorization is essential to automatically handle the classification of multi-label news articles in online portal. This research employs some potential methods to improve performance of hierarchical multi-label classifier for Indonesian news article. First potential method is using Convolutional Neural Network (CNN) to build the top level classifier. The second method could improve the classification performance by calculating the average of the word vectors obtained from distributed semantic model. The third method combines lexical and semantic method to extract documents features, which multiplied word term frequency (lexical) with word vector average (semantic). Model build using Calibrated Label Ranking as multi-label classification method, and trained using Naïve Bayes algorithm has the best F1-measure of 0.7531. Multiplication of word term frequency and the average of word vectors were also used to build this classifiers. This configuration improved multi-label classification performance by 4.25%, compared to the baseline. The distributed semantic model that gave best performance in this experiment obtained from 300-dimension word2vec of Wikipedia’s articles. The multi-label classification model performance is also influenced by news’ released date. The difference period between training and testing data would also decrease models’ performance.
Rhetorical Sentence Classification for Automatic Title Generation in Scientific Article Jan Wira Gotama Putra; Masayu Leylia Khodra
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 15, No 2: June 2017
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v15i2.4061

Abstract

In this paper, we proposed a work onrhetorical corpus construction andsentence classification model experiment that specifically could be incorporated in automatic paper title generation task for scientific article. Rhetorical classification is treated as sequence labeling. Rhetorical sentence classification model is useful in task which considers document’s discourse structure. We performed experiments using two domains of datasets: computer science (CS dataset), and chemistry (GaN dataset). We evaluated the models using 10-fold-cross validation (0.70-0.79 weighted average F-measure) as well as on-the-run (0.30-0.36 error rate at best). We argued that our models performed best when handled using SMOTE filter for imbalanced data
Toward a New Approach in Fruit Recognition using Hybrid RGBD Features and Fruit Hierarchy Property Ema Rachmawati; Iping Supriana; Masayu Leylia Khodra
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 (1220.177 KB) | DOI: 10.11591/eecsi.v4.1029

Abstract

We present hierarchical multi-feature classification (HMC) system for multiclass fruit recognition problem. Our approach to HMC exploits the advantages of combining multimodal features  and  the  fruit  hierarchy  property.  In  the construction of hybrid features, we take the advantage of using color feature in the fruit recognition problem and combine it with 3D shape feature of depth channel of RGBD (Red, Green, Blue, Depth) images. Meanwhile, given a set of fruit species and variety, with a preexisting hierarchy among them, we consider the problem of assigning images to one of these fruit variety from the point of view of a hierarchy. We report on computational experiment using this approach. We show that the use of hierarchy structure along with hybrid RGBD features can improve the classification performance.
Image Captioning menurut Scientific Revolution Kuhn dan Popper Agus Nursikuwagus; Rinaldi Munir; Masayu Leylia Khodra
Jurnal Manajemen Informatika (JAMIKA) Vol 10 No 2 (2020): Jurnal Manajemen Informatika (JAMIKA)
Publisher : Program Studi Manajemen Informatika, Fakultas Teknik dan Ilmu Komputer, Universitas Komputer Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34010/jamika.v10i2.2630

Abstract

Image captioning is one area in artificial intelligence that elaborates between computer vision and natural language processing. The focus on this process is an architecture neural network that includes many layers to solve the identification object on the image and give the caption. This architecture has a task to display the caption from object detection on one image. This paper explains about the connection between scientific revolution and image captioning. We have conducted the methodology by Kuhn's scientific revolution and relate to Popper's philosophy of science. The result of this paper is that an image captioning is truly science because many improvements from many researchers to find an effective method on the deep learning process. On the philosophy of science, if the phenomena can be falsified, then an image captioning is the science.