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All Journal Perfecting a Video Game with Game Metrics Proceeding of the Electrical Engineering Computer Science and Informatics International Journal of Advances in Intelligent Informatics Proceeding of the Electrical Engineering Computer Science and Informatics JUMANJI (Jurnal Masyarakat Informatika Unjani)

Masayu Leylia Khodra

Institut Teknologi Bandung

Author-ID : 298323

Computer Science & IT Electrical & Electronics Engineering

Published : 5 Documents Claim Missing Document

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Articles

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

We present hierarchical multi-feature classification (HMC) system for multiclass fruit recognition problem. Our approach to HMC exploits the advantages of combining multimodal features Â andÂ the Â fruit Â hierarchy Â property. Â In Â 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.

Analisis Pembangunan Korpus Berpasangan Untuk Pembangkitan Parafrasa Pada Makalah Ilmiah Ilyas, Ridwan; Widyantoro, Dwi Hendratmo; Khodra, Masayu Leylia
JUMANJI (Jurnal Masyarakat Informatika Unjani) Vol 2 No 1 (2018): Jurnal Masyarakat Informatika Unjani
Publisher : Jurusan Informatika Universitas Jenderal Achmad Yani

Pembangunan mesin yang dapat membangkitkan kalimat baru dengan tingkat semantik yang tinggi namun secara penulisan berbeda (parafrasa) membutuhkan sumberdaya bahasa berupa korpus parallel. Proses pembangunan korpus memerlukan analisis awal sesuai dengan domain dari mesin yang akan dibuat. Pada penelitian ini dilakukan analis dalam pembangunan korpus berpasangan pada makalah ilmiah. Kalimat-kalimat pada makalah ilmiah memiliki karakteristik yang berbeda dengan domain lain seperti berita atau media sosial. Dari hasil proses ekstraksi awal didapatkan 590.402 kalimat isi dan 23.584 kalimat abstrak. Hasil dari penelitian ini dapat menjadi kandidat korpus yang dilakukan dengan proses terkomputerisasi.

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

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

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

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.

Co-Authors Dwi Hendratmo Widyantoro Ema Rachmawati Iping Supriana Ivana Clairine Irsan Jan Wira Gotama Putra Rachmawati, Ema Ridwan Ilyas, Ridwan Supriana, Iping

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