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Sentence Extraction Based on Sentence Distribution and Part of Speech Tagging for Multi-Document Summarization Agus Zainal Arifin; Moch Zawaruddin Abdullah; Ahmad Wahyu Rosyadi; Desepta Isna Ulumi; Aminul Wahib; Rizka Wakhidatus Sholikah
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 16, No 2: April 2018
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v16i2.8431

Abstract

Automatic multi-document summarization needs to find representative sentences not only by sentence distribution to select the most important sentence but also by how informative a term is in a sentence. Sentence distribution is suitable for obtaining important sentences by determining frequent and well-spread words in the corpus but ignores the grammatical information that indicates instructive content. The presence or absence of informative content in a sentence can be indicated by grammatical information which is carried by part of speech (POS) labels. In this paper, we propose a new sentence weighting method by incorporating sentence distribution and POS tagging for multi-document summarization. Similarity-based Histogram Clustering (SHC) is used to cluster sentences in the data set. Cluster ordering is based on cluster importance to determine the important clusters. Sentence extraction based on sentence distribution and POS tagging is introduced to extract the representative sentences from the ordered clusters. The results of the experiment on the Document Understanding Conferences (DUC) 2004 are compared with those of the Sentence Distribution Method. Our proposed method achieved better results with an increasing rate of 5.41% on ROUGE-1 and 0.62% on ROUGE-2.
Region Based Image Retrieval Using Ratio of Proportional Overlapping Object Agus Zainal Arifin; Rizka Wakhidatus Sholikah; Dimas Fanny H. P.; Dini Adni Navastara
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 14, No 4: December 2016
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v14i4.4289

Abstract

In Region Based Image Retrieval (RBIR), determination of the relevant block in query region is based on the percentage of image objects that overlap with each sub-blocks. But in some images, the size of relevant objects are small. It may cause the object to be ignored in determining the relevant sub-blocks. Therefore, in this study we proposed a system of RBIR based on the percentage of proportional objects that overlap with sub-blocks. Each sub-blocks is selected as a query region. The color and texture features of the query region will be extracted by using HSV histogram and Local Binary Pattern (LBP), respectively. We also used shape as global feature by applying invariant moment as descriptor. Experimental results show that the proposed method has average precision with 74%.
PENGEMBANGAN PROGRAM CHSE BERBASIS AI DAN KEBIJAKAN STANDAR TEKNOLOGI PARIWISATA DI ERA NEW NORMAL UNTUK MENGONTROL PENGUNJUNG KAWASAN EDUWISATA MOJOKERTO Tony Hanoraga; Banu Prasetyo; Khakim Ghozali; Rizka Wakhidatus Sholikah; Ridho Rahman Hariadi; Juniarun Fathurrohman
ABDIMAS Vol 2 No 01 (2022): Education for Sustainable Development
Publisher : COMMUNITY OF RESEARCH LABORATORY SURABAYA

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

Abstract

Kebijakan publik merupakan modal utama yang dimiliki pemerintah untuk menata kehidupan masyarakat dalam berbagai aspek kehidupan. Dikatakan sebagai modal utama karena hanya melalui kebijakan publiklah pemerintah memiliki kekuatan dan kewenangan hukum untuk menata masyarakat dan sekaligus memaksakan segala ketentuan yang telah ditetapkan. Untuk mencegah penularan COVID-19 di Kawasan Eduwisata Mojokerto di era new normal perlu disusun kebijakan standar teknologi pariwisata untuk mengontrol pengunjung di Kawasan Eduwisata Mojokerto. Salah satu inovasi kebijakan standar teknologi pariwisata yang dapat dilakukan yaitu memanfaatkan teknologi kecerdasan buatan (Artificial Intelligence) untuk mengontrol kepatuhan pengunjung terhadap Protokol CHSE (Cleanliness, Health, Safety, and Environment Sustainability). Luaran yang diharapkan dari ABMAS ini adalah Kabupaten Mojokerto khususnya kawasan Eduwisata Mojokerto memiliki kebijakan standar teknologi pariwisata yang dapat meningkatkan perlindungan wisatawan terhadap penularan COVID-19 yang berkunjung ke kawasan wisata ini sekaligus meningkatkan kepercayaan wisatawan terhadap pelaksanaan Protokol CHSES di kawasan Eduwisata Mojokerto. Luaran lainnya dari kegiatan abmas ini adalah diterapkannya perangkat teknologi untuk mendeteksi suhu, wajah dan kerumunan pengunjung di kawasan Eduwisata Mojokerto.
A Comparative Study of Multi-Label Classification for Document Labeling in Ethical Protocol Review Rizka Wakhidatus Sholikah; Diana Purwitasari; Mohammad Zaenuddin Hamidi
Techno.Com Vol 21, No 2 (2022): Mei 2022
Publisher : LPPM Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33633/tc.v21i2.5994

Abstract

An ethical clearance document ensures that the research will protect the subject in accordance with existing ethical principles. The ethical clearance is issued by the Research Ethics Commission (KEP). KEP will conduct a review of the proposed ethical protocol based on the seven standards contained in a protocol. The review process is done manually by KEP. This process often creates bottlenecks in research due to the large number of protocols that must be reviewed, so that the process to get ethical clearance takes a long time. This can affect the setback in the schedule of the research process. Therefore, in this research, a comparative study was conducted on the problem of multi-label classification to automate the ethical protocol review process. Automation of the labeling process can increase the effectiveness of the review process because it can provide an overview to the reviewer regarding the label of a document before conducting a more in-depth review process. The experiment results show that the use of the traditional machine learning approach produces better performance than the deep learning approach. The machine learning method with the best results is Naïve Bayes+BoW with precision, recall, and F-score values of 0.76, 0.80, and 0.78, respectively.
Penentuan Topik dengan Opinion Mining berbasis Two-Pass Classifier dan Bayesian dalam Peringkasan Teks Twitter Muhammad Mirza Muttaqi; Diana Purwitasari; Rizka Wakhidatus Sholikah
ILKOMNIKA: Journal of Computer Science and Applied Informatics Vol 4 No 3 (2022): Volume 4, Nomor 3, Desember 2022
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28926/ilkomnika.v4i3.499

Abstract

Kolom komentar berkepanjangan pada teks Twitter mempersulit masyarakat yang ingin mengetahui informasi terkini seperti topik tren Covid-19. Peringkasan teks dengan mempertahankan konten melalui pengelompokkan kata untuk deteksi kemiripan hubungan dalam konteks kalimat dapat menghasilkan ringkasan lebih terfokus. Akan tetapi pada klastering teks Twitter sering ditemukan kalimat atau satu tweet yang seharusnya berbeda klaster. Oleh karena itu, perlu dilakukan pemodelan topik dan penentuan topik untuk mempermudah pengguna mencari suatu informasi tertentu. Pada penelitian ini difokuskan pada topik Covid-19. Pemodelan topik digunakan untuk mengatur, mencari, memahami, dan meringkas sebuah teks. Opinion Mining digunakan untuk mengekstrak atau mengklasifikasikan polaritas sentimen. Polaritas sentimen ini berupa “positif” atau “negatif” pada suatu entitas atau aspek. Proses klasifikasi menggunakan metode two pass clasiffier untuk sentimen positif dan negatif, serta Bayesian sebagai metode pelabel entitas – entitas. Setelah itu, label-label tersebut dikelompokkan sehingga terbentuk topik-topik dan beberapa tweet yang mempunyai kemiripan entitas topik dikelompokan ke dalam topik. Dari hasil evaluasi mengunkan TextRank, Okapi BM25 dan PageRank, proses opinion mining menghasilkan nilai yang lebih tinggi dibandingkan dengan Latent Semantic Indexing (LSI), dengan selisih rata-rata 0,53
Abstractive and Extractive Approaches for Summarizing Multi-document Travel Reviews Narandha Arya Ranggianto; Diana Purwitasari; Chastine Fatichah; Rizka Wakhidatus Sholikah
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 7 No 6 (2023): December 2023
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v7i6.5170

Abstract

Travel reviews offer insights into users' experiences at places they have visited, including hotels, restaurants, and tourist attractions. Reviews are a type of multidocument, where one place has several reviews from different users. Automatic summarization can help users get the main information in multi-document. Automatic summarization consists of abstractive and extractive approaches. The abstractive approach has the advantage of producing coherent and concise sentences, while the extractive approach has the advantage of producing an informative summary. However, there are weaknesses in the abstractive approach, which results in inaccurate and less information. On the other hand, the extractive approach produces longer sentences compared to the abstractive approach. Based on the characteristics of both approaches, we combine abstractive and extractive methods to produce a more concise and informative summary than can be achieved using either approach alone. To assess the effectiveness of abstractive and extractive, we use ROUGE based on lexical overlaps and BERTScore based on contextual embeddings which it be compared with a partial approach (abstractive only or extractive only). The experimental results demonstrate that the combination of abstractive and extractive approaches, namely BERT-EXT, leads to improved performance. The ROUGE-1 (unigram), ROUGE-2 (bigram), ROUGE-L (longest subsequence), and BERTScore values are 29.48%, 5.76%, 33.59%, and 54.38%, respectively. Combining abstractive and extractive approach yields higher performance than the partial approach.