Jurnal SINTA 2
Optimasi Suffix Tree Clustering dengan Wordnet dan Named Entity Recognition untuk Pengelompokan Dokumen
Jurnal Teknologi Informasi dan Ilmu Komputer (JTIIK) · Vol. 4 No. 4 (2017) · pp. 263–267
- ISSN
- 2528-6579, 2355-7699
- Published
- 2017-12-31
- Publisher
- Fakultas Ilmu Komputer, Universitas Brawijaya
- Language
- en
Abstract
AbstrakSemakin meningkatnya jumlah dokumen teks di dunia digital mempengaruhi banyaknya jumlah informasi dan menyebabkan kesulitan dalam proses temu kembali informasi (information retreival). Clustering dokumen merupakan suatu bidang text mining yang penting dan dapat digunakan untuk mengefisienkan dalam pengelolaan teks serta peringkasan teks. Namun beberapa permasalahan muncul dalam clustering dokumen teks terutama dalam dokumen berita seperti ambiguitas dalam content, overlapping cluster, dan struktur unik yang terdapat dalam dokumen berita. Penelitian ini mengusulkan metode baru yaitu optimasi Suffix Tree Clustering (STC) dengan WordNet dan Named Entity Recognition (NER) untuk pengelompokan dokumen. Metode ini memiliki beberapa tahap, yaitu prepocessing dokumen dengan mengekstraksi named entity serta melakukan deteksi sinonim berdasarkan WordNet. Tahap kedua adalah pembobotan term dengan tfidf dan nerfidf. Tahap ketiga adalah melakukan clustering dokumen dengan menggunakan Suffix Tree Clustering. Berdasarkan pengujian didapatkan rata-rata nilai precision sebesar 79.83%, recall 77.25%, dan f-measure78.30 %.Kata kunci: Clustering dokumen, Named Entity Recognition, Suffix Tree Clustering, WordNetAbstractThe increasingnumber oftext documentsin the internet, influence on the number of information and lead to difficulties in the process of information retrieval. Documents clustering is main field of text mining and can be used to stream line the management of text and summarization of text. However, some problems a risein documents clustering, especially in news documents such as ambiguity in the content, overlapping clusters, and theuniquestructure ofthe news thatcontained inthe document. Inthisresearch, we proposea newmethodfor documents clustering, optimization Suffix Tree Clustering (STC) with WordNet and Named Entity Recognition (NER). In this method there are several step, step one is prepocessing documents with named entity extraction and synonym detection based on WordNet. Step two is term weighting with tfidf and nerfidf. For the last step is document clustering using Suffix Tree Clustering. Based on testingwe obtained 79.83% for precision, 77.25% for recall, and78.30% for F-measureKeywords: Documents Clustering, Named Entity Recognition, Suffix Tree Clustering, WordNet
Keywords
Collaboration network of the authors
40 authors · 90 co-authorship links · 18 across different organisations · showing the 40 most connected
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
- Universitas Muhammadiyah Malang
- National Research and Innovation Agency
- Universitas Muhammadiyah Gresik
- Politeknik Negeri Malang
- Different organisations
- Same organisation
- Affiliation unknown
- Thicker line = more joint articles · bigger circle = more articles
Show as table
| Co-author | Organisation | Joint articles |
|---|---|---|
| Aditya Rachmadi | University of Brawijaya | 5 |
| Satrio Agung Wicaksono | University of Brawijaya | 5 |
| Diana Purwitasari | Institut Teknologi Sepuluh Nopember | 4 |
| Faizatul Amalia | University of Brawijaya | 4 |
| Yusi Tyroni Mursityo | University of Brawijaya | 3 |
| Mochamad Chandra Saputra | University of Brawijaya | 3 |
| Yufis Azhar | Universitas Muhammadiyah Malang | 3 |
| Farid Sukmana | Universitas Muhammadiyah Gresik | 2 |
| Cindy Felita Nur Alimah | University of Brawijaya | 2 |
| Meilita Dwi Aryati | University of Brawijaya | 2 |
| Chastine Fatichah | Institut Teknologi Sepuluh Nopember | 1 |
| Wahyu Nur Hidayat | Politeknik Negeri Malang | 1 |
| Raden Arief Setyawan | University of Brawijaya | 1 |
| Issa Arwani | University of Brawijaya | 1 |
| Kartika Utami | University of Brawijaya | 1 |
| Rahmana Ilmi Hakim | University of Brawijaya | 1 |
| Nurur Rohmah | University of Brawijaya | 1 |
| Ridho Fadhlurrahman | University of Brawijaya | 1 |
| Savira Fahrunisa | University of Brawijaya | 1 |
| Azri Putri Rahmatika | University of Brawijaya | 1 |
| Dinar Indah Dwi Utami | University of Brawijaya | 1 |
| Safira Widya Hapsari | University of Brawijaya | 1 |
| Muhammad Rosyid Mubarok | University of Brawijaya | 1 |
| Anisah Ikhsawiyanthi | University of Brawijaya | 1 |
| Pratama Aprilianto | University of Brawijaya | 1 |
| Hanifah Muslimah Az-Zahra | University of Brawijaya | 1 |
| Alfansya Achmad Mulyadi | University of Brawijaya | 1 |
| Azka Azkiyah | University of Brawijaya | 1 |
| Allisa Majid | University of Brawijaya | 1 |
| Ahmad Afif Supianto | University of Brawijaya | 1 |
| Abdul Harris Wicaksono | University of Brawijaya | 1 |
| Didik Krisnandi | National Research and Innovation Agency | 1 |
| Ana Heryana | National Research and Innovation Agency | 1 |
| Yafie Arbian Cipto | University of Brawijaya | 1 |
| Fatmawati Fatmawati | University of Brawijaya | 1 |
| Dian Eka Ratnawati | University of Brawijaya | 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.