Open access Jurnal SINTA 2
Pengenalan Entitas Bernama Menggunakan Bi-LSTM pada Chatbot Bahasa Indonesia
Jurnal Teknologi Informasi dan Ilmu Komputer (JTIIK) · Vol. 11 No. 5 (2024) · pp. 965–970
- ISSN
- 2528-6579, 2355-7699
- Published
- 2024-10-31
- Publisher
- Fakultas Ilmu Komputer, Universitas Brawijaya
- Language
- id
Abstract
Bahasa Indonesia
Institusi publik perlu mengitegrasikan e-government ke dalam struktur pengelolaan mereka. Sistem pelayanan terpadu harus dapat menyelesaikan masalah yang dihadapi oleh pengguna layanan. Apabila sistem pelayanan terpadu hanya mengandalkan manusia, maka sistem pelayanan terpadu dapat terhambat. Chatbot adalah salah satu solusi untuk menggantikan peran manusia dalam sistem pelayanan terpadu. Salah satu komponen pada chatbot adalah pengenalan entitas bernama. Pada penelitian ini, pengenalan entitas bernama dilakukan dalam beberapa tahap. Tahapan-tahapan tersebut antara lain penghilangan noise, pelabelan data, pembuatan kamus kata dan label, encoding urutan dan pemisahan data, inisiasi model, dan pelatihan model. Model yang digunakan yakni bidirectional long-short term memory. Skor F1 terbaik yang didapat dari pengujian adalah 87,44% dengan hyperparameter jumlah layer sebanyak 2, hidden size sebanyak 100, dan learning rate sebesar 0,01. Kemudian, penambahan jumlah layer maupun hidden size kurang berpengaruh terhadap skor F1 yang dihasilkan oleh model. Learning rate memengaruhi seberapa cepat model mencapai solusi optimal. Abstract Public institutions must integrate e-government into their management structures. An integrated service system must be able to solve the problems faced by service users. If the integrated service system only relies on humans, then the integrated service system can be hampered. Chatbot is one of the solutions to replace the human role in an integrated service system. One component of the chatbot is named entity recognition. In this study, the named entity recognition was carried out in several stages. These stages include noise removal, data labeling, word and label dictionary creation, sequence encoding and data separation, model initiation, and model training. The model used is bidirectional long-short term memory. The best F1 score obtained from the test is 87.44% with hyperparameters of the number of layers of 2, hidden size of 100, and learning rate of 0.01. The addition of the number of layers and hidden size has little effect on the F1 score produced by the model. The learning rate affects how fast the model reaches the optimal solution.
English
Institusi publik perlu mengitegrasikan e-government ke dalam struktur pengelolaan mereka. Sistem pelayanan terpadu harus dapat menyelesaikan masalah yang dihadapi oleh pengguna layanan. Apabila sistem pelayanan terpadu hanya mengandalkan manusia, maka sistem pelayanan terpadu dapat terhambat. Chatbot adalah salah satu solusi untuk menggantikan peran manusia dalam sistem pelayanan terpadu. Salah satu komponen pada chatbot adalah pengenalan entitas bernama. Pada penelitian ini, pengenalan entitas bernama dilakukan dalam beberapa tahap. Tahapan-tahapan tersebut antara lain penghilangan noise, pelabelan data, pembuatan kamus kata dan label, encoding urutan dan pemisahan data, inisiasi model, dan pelatihan model. Model yang digunakan yakni bidirectional long-short term memory. Skor F1 terbaik yang didapat dari pengujian adalah 87,44% dengan hyperparameter jumlah layer sebanyak 2, hidden size sebanyak 100, dan learning rate sebesar 0,01. Kemudian, penambahan jumlah layer maupun hidden size kurang berpengaruh terhadap skor F1 yang dihasilkan oleh model. Learning rate memengaruhi seberapa cepat model mencapai solusi optimal. Abstract Public institutions must integrate e-government into their management structures. An integrated service system must be able to solve the problems faced by service users. If the integrated service system only relies on humans, then the integrated service system can be hampered. Chatbot is one of the solutions to replace the human role in an integrated service system. One component of the chatbot is named entity recognition. In this study, the named entity recognition was carried out in several stages. These stages include noise removal, data labeling, word and label dictionary creation, sequence encoding and data separation, model initiation, and model training. The model used is bidirectional long-short term memory. The best F1 score obtained from the test is 87.44% with hyperparameters of the number of layers of 2, hidden size of 100, and learning rate of 0.01. The addition of the number of layers and hidden size has little effect on the F1 score produced by the model. The learning rate affects how fast the model reaches the optimal solution.
Keywords
Collaboration network of the authors
31 authors · 59 co-authorship links · 4 across different organisations
Hover or tap a name to highlight its co-authors. Double-click to open the author's page.
- University of Brawijaya
- Institut Teknologi Sepuluh Nopember
- Affiliation unknown
- Different organisations
- Same organisation
- Affiliation unknown
- Thicker line = more joint articles · bigger circle = more articles
Show as table
| Co-author | Organisation | Joint articles |
|---|---|---|
| Dian Eka Ratnawati | University of Brawijaya | 2 |
| Putra Pandu Adikara | University of Brawijaya | 2 |
| Tri Hadiah Muliawati | Institut Teknologi Sepuluh Nopember | 1 |
| Reddy Alexandro | Institut Teknologi Sepuluh Nopember | 1 |
| Fitra Abdurrachman Bachtiar | University of Brawijaya | 1 |
| Nathanael Victor Darenoh | University of Brawijaya | 1 |
| Chastine Fatichah | Institut Teknologi Sepuluh Nopember | 1 |
| Diana Purwitasari | Institut Teknologi Sepuluh Nopember | 1 |
| Satrio Agung Wicaksono | University of Brawijaya | 1 |
| Rochmat Setiyawan | University of Brawijaya | 1 |
| Budi Darma Setiyawan | University of Brawijaya | 1 |
| Ari Hernawan | University of Brawijaya | 1 |
| Achmad Ridok | University of Brawijaya | 1 |
| Wayan Firdaus Mahmudy | University of Brawijaya | 1 |
| Ghenniy Rachmansyah | University of Brawijaya | 1 |
| Putri Nur Fadila | University of Brawijaya | 1 |
| Muhammad Fathur Rahman Khairul | University of Brawijaya | 1 |
| Zahra Asma Annisa | University of Brawijaya | 1 |
| Faiz Aulia Al Farisi | University of Brawijaya | 1 |
| Aryo Pinandito | University of Brawijaya | 1 |
| Multohadi Hamzaturrazak | University of Brawijaya | 1 |
| Abdul Jabbar | University of Brawijaya | 1 |
| Candra Dewi | University of Brawijaya | 1 |
| Firman Afrialdy | Affiliation unknown | 1 |
| Firman Afrialdy | Affiliation unknown | 1 |
| Candra Dewi | Affiliation unknown | 1 |
| Muh. Arif Rahman | University of Brawijaya | 1 |
| Matyus Garbela Ismanto | Affiliation unknown | 1 |
Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.