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All Journal IJCCS (Indonesian Journal of Computing and Cybernetics Systems) TELKOMNIKA (Telecommunication Computing Electronics and Control) PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic Jurnal Ilmiah Kursor Journal of ICT Research and Applications International Journal of Advances in Intelligent Informatics Proceeding of the Electrical Engineering Computer Science and Informatics JUMANJI (Jurnal Masyarakat Informatika Unjani) Jurnal Manajemen Informatika
Masayu Leylia Khodra
Institut Teknologi Bandung
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering

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

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (154.731 KB) | DOI: 10.26874/jumanji.v2i1.44

Abstract

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.
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.
Neural Network Pruning in Unsupervised Aspect Detection based on Aspect Embedding Muhammad Haris Maulana; Masayu Leylia Khodra
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 16, No 4 (2022): October
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.72981

Abstract

 Aspect detection systems for online reviews, especially based on unsupervised models, are considered better strategically to process online reviews, generally a very large collection of unstructured data.  Aspect embedding-based deep learning models are designed for this problem however they still rely on redundant word embedding and they are sensitive to initialization which may have a significant impact on model performance. In this research, a pruning approach is used to reduce the redundancy of deep learning model connections and is expected to produce a model with similar or better performance. This research includes several experiments and comparisons of the results of pruning the model network weights based on the general neural network pruning strategy and the lottery ticket hypothesis. The result of this research is that pruning of the unsupervised aspect detection model, in general, can produce smaller submodels with similar performance even with a significant amount of weights pruned. Our sparse model with 80% of its total weight pruned has a similar performance to the original model. Our current pruning implementation, however, has not been able to produce sparse models with better performance.
Sentiment Analysis of Sentence-Level using Dependency Embedding and Pre-trained BERT Model Fariska Zakhralativa Ruskanda; Stefanus Stanley Yoga Setiawan; Nadya Aditama; Masayu Leylia Khodra
PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic Vol 11 No 1 (2023): March 2023
Publisher : LPPM Universitas Islam 45 Bekasi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33558/piksel.v11i1.6938

Abstract

Sentiment analysis is a valuable field of research in NLP with many applications. Dependency tree is one of the language features that can be utilized in this field. Dependency embedding, as one of the semantic representations of a sentence, has shown to provide more significant results compared to other embeddings, which makes it a potential way to improve the performance of sentiment analysis tasks. This study aimed to investigate the effect of dependency embedding on sentence-level sentiment analysis through experimental research. The study replaced the Vocabulary Graph embedding in the VGCN-BERT sentiment classification system architecture with several dependency embedding representations, including word vector, context vector, average of word and context vectors, weighting on word and context vectors, and merging of word and context vectors. The experiments were conducted on two datasets, SST-2 and CoLA, with more than 19 thousand labeled sentiment sentences. The results indicated that dependency embedding can enhance the performance of sentiment analysis at the sentence level.
The Evaluation of DyHATR Performance for Dynamic Heterogeneous Graphs Nasy`an Taufiq Al Ghifari; Gusti Ayu Putri Saptawati; Masayu Leylia Khodra; Benhard Sitohang
Journal of ICT Research and Applications Vol. 17 No. 2 (2023)
Publisher : DRPM - ITB

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5614/itbj.ict.res.appl.2023.17.2.7

Abstract

Dynamic heterogeneous graphs can represent real-world networks. Predicting links in these graphs is more complicated than in static graphs. Until now, research interest of link prediction has focused on static heterogeneous graphs or dynamically homogeneous graphs. A link prediction technique combining temporal RNN and hierarchical attention has recently emerged, called DyHATR. This method is claimed to be able to work on dynamic heterogeneous graphs by testing them on four publicly available data sets (Twitter, Math-Overflow, Ecomm, and Alibaba). However, after further analysis, it turned out that the four data sets did not meet the criteria of dynamic heterogeneous graphs. In the present work, we evaluated the performance of DyHATR on dynamic heterogeneous graphs. We conducted experiments with DyHATR based on the Yelp data set represented as a dynamic heterogeneous graph consisting of homogeneous subgraphs. The results show that DyHATR can be applied to identify link prediction on dynamic heterogeneous graphs by simultaneously capturing heterogeneous information and evolutionary patterns, and then considering them to carry out link predicition. Compared to the baseline method, the accuracy achieved by DyHATR is competitive, although the results can still be improved.
Co-Authors Agus Nursikuwagus Ary Setijadi Prihatmanto Bambang Riyanto Trilaksono Benhard Sitohang Carmadi Machbub Dwi Hendratmo Widyantoro Dwi Hendratmo Widyantoro Dwi Hendratmo Widyantoro Dwi Hendratmo Widyantoro E. Aminudin Aziz E. Aminudin Aziz Ema Rachmawati Ghoziyah Haitan Rachman Gusti Ayu Putri Saptawati Iping Supriana Iping Supriana Suwardi Ivana Clairine Irsan Jan Wira Gotama Putra Jan Wira Gotama Putra Mohammad Dimas Muhammad Haris Maulana Nadya Aditama Nasy`an Taufiq Al Ghifari Ridwan Ilyas Rinaldi Munir Ruskanda, Fariska Zakhralativa Stefanus Stanley Yoga Setiawan Stefanus Thobi Sinaga Supriana, Iping Wahyudi Wahyudi