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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Automata
Annisa Zahra
Universitas Islam Indonesia
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Energy Engineering

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Kajian Literatur Named Entity Recognition pada Domain Wisata Annisa Zahra; Ahmad Fathan Hidayatullah; Septia Rani
AUTOMATA Vol. 2 No. 1 (2021)
Publisher : AUTOMATA

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Abstrak—Saat merencanakan perjalanan wisata, pencarian destinasi wisata merupakan hal yang umumnya dilakukan. Proses tersebut seringkali dilakukan menggunakan bantuan mesin pencari, yaitu dengan membaca artikel yang tersedia di internet dan ditulis oleh orang lain. Pada proses pencarian informasi tersebut, terkadang dibutuhkan waktu yang tidak sedikit karena perlu membaca artikel-artikel yang tersedia untuk memperoleh informasi yang relevan. Named Entity Recognition (NER) dapat digunakan dalam mendeteksi entitas nama pada suatu teks sehingga dapat membantu pengguna dalam menemukan informasi yang diinginkan. Makalah ini mengkaji sebanyak 8 literatur mengenai NER pada domain wisata yang didapat dari hasil pencarian pada Google Scholar dengan kata kunci “Tourism Named Entity Recognition”. Dari kajian literatur yang telah dilakukan, diperoleh informasi bahwa model NER yang paling banyak digunakan pada domain wisata adalah Bidirectional Encoder Representations from Transformers (BERT). Model BERT bertujuan untuk melakukan pelatihan representasi kata menggunakan konverter dua arah dengan menyesuaikan konteks pada sisi kiri dan kanan semua lapisan. Sehingga, penggunaan BERT dapat membantu mencegah terjadinya ambiguitas pada suatu kata yang mengakibatkan kesalahan pengenalan entitas. Hasil penelitian ini diharapkan dapat membantu dalam pengembangan NER pada domain wisata selanjutnya.
A Study on Visual Understanding Image Captioning using Different Word Embeddings and CNN-Based Feature Extractions Dhomas Hatta Fudholi; Annisa Zahra; Royan Abida N. Nayoan
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 7, No. 1, February 2022
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v7i1.1394

Abstract

Image captioning is a task that can provide a description of an image in natural language. Image captioning can be used for a variety of applications, such as image indexing and virtual assistants. In this research, we compared the performance of three different word embeddings, namely, GloVe, Word2Vec, FastText and six CNN-based feature extraction architectures such as, Inception V3, InceptionResNet V2, ResNet152 V2, EfficientNet B3 V1, EfficientNet B7 V1, and NASNetLarge which then will be combined with LSTM as the decoder to perform image captioning. We used ten different household objects (bed, cell phone, chair, couch, oven, potted plant, refrigerator, sink, table, and tv) that were obtained from MSCOCO dataset to develop the model. Then, we created five new captions in Bahasa Indonesia for the selected images. The captions might contain details about the name, the location, the color, the size, and the characteristics of an object and its surrounding area. In our 18 experimental models, we used different combination of the word embedding and CNN-based feature extraction architecture, along with LSTM to train the model. As the result, models that used the combination of Word2Vec + NASNetLarge performed better in generating Indonesian captions than the other models based on BLEU-4 metric.
Bidirectional long-short term memory and conditional random field for tourism named entity recognition Annisa Zahra; Ahmad Fathan Hidayatullah; Septia Rani
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 11, No 4: December 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v11.i4.pp%p

Abstract

The common thing to do when planning a trip is to search for a tourist destination. This process is often done using search engines and reading articles on the internet. However, it takes much time to search for such information, as to obtain relevant information, we have to read some available articles. Named entity recognition (NER) can detect named entities in a text to help users find the desired information. This study aims to create a NER model that will help to detect tourist attractions in an article. The articles used for the dataset are English articles obtained from the internet. We built our NER model using bidirectional long-short term memory (BiLSTM) and conditional random fields (CRF), with Word2Vec as a feature. Our proposed model achieved the best with an average F1-Score of 75.25% compared to all scenarios tested.
Co-Authors Ahmad Fathan Hidayatullah, Ahmad Fathan Dhomas Hatta Fudholi Royan Abida N. Nayoan Septia Rani