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All Journal Jurnal Ilmu Komputer dan Informasi Jurnal Buana Informatika Jurnal Ilmiah Kursor Jurnal ULTIMATICS Journal of Computer Science and Informatics Engineering (J-Cosine) Jurnal Linguistik Komputasional Specta Journal of Technology
Agus Zainal Arifin
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Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Environmental Science

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Journal : Jurnal Linguistik Komputasional

 1 
Pembobotan Kata berdasarkan Kluster untuk Peringkasan Otomatis Multi Dokumen Lukman Hakim; Fadli Husein Wattiheluw; Agus Zainal Arifin; Aminul Wahib
Jurnal Linguistik Komputasional Vol 1 No 2 (2018): Vol. 1, No. 2
Publisher : Indonesia Association of Computational Linguistics (INACL)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (575.381 KB) | DOI: 10.26418/jlk.v1i2.7

Abstract

Multi-document summarization is a technique for getting information. The information consists of several lines of sentences that aim to describe the contents of the entire document relevantly. Several algorithms with various criteria have been carried out. In general, these criteria are the preprocessing, cluster, and representative sentence selection to produce summaries that have high relevance. In some conditions, the cluster stage is one of the important stages to produce summarization. Existing research cannot determine the number of clusters to be formed. Therefore, we propose clustering techniques using cluster hierarchy. This technique measures the similarity between sentences using cosine similarity. These sentences are clustered based on their similarity values. Clusters that have the highest level of similarity with other clusters will be merged into one cluster. This merger process will continue until one cluster remains. Experimental results on the 2004 Document Understanding Document (DUC) dataset and using two scenarios that use 132, 135, 137 and 140 clusters resulting in fluctuating values. The smaller the number of clusters does not guarantee an increase in the value of ROUGE-1. The method proposed using the same number of clusters has a lower ROUGE-1 value than the previous method. This is because in cluster 140 the similarity values in each cluster experienced a decrease in similarity values.
Metode Pembobotan Berbasis Topik dan Kelas untuk Berita Online Berbahasa Indonesia Maryamah Maryamah; Made Agus Putra Subali; Lailly Qolby; Agus Zainal Arifin; Ali Fauzi
Jurnal Linguistik Komputasional Vol 1 No 1 (2018): Vol. 1, No. 1
Publisher : Indonesia Association of Computational Linguistics (INACL)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (840.487 KB) | DOI: 10.26418/jlk.v1i1.4

Abstract

Clustering of news documents manually depends on the ability and accuracy of the human so that it can lead to errors in the grouping process of documents. Therefore, it is necessary to group the news document automatically. In this clustering, we need a weighting method that includes TF.IDF.ICF. In this paper we propose a new weighting algorithm is TF.IDF.ICF.ITF to automatically clustering documents automatically through statistical data patterns so that errors in manual grouping of documents can be reduced and more efficient. K-Means ++ is an algorithm for classification and is the development of the K-Means algorithm in the initial cluster initialization stage which is easy to implement and has more stable results. K-Means ++ classifies documents at the weighting stages of Inverse Class Frequency (ICF). ICF is developed from the use of class-based weighting for the term weighting term in the document. The terms that often appear in many classes will have a small but informative value. The proposed weighting is calculated. Testing is done by using a certain query on some number of best features, the results obtained by TF.IDF.ICF.ITF method gives less optimal results.
Pembobotan Kata berdasarkan Kluster untuk Peringkasan Otomatis Multi Dokumen Fatra Nonggala Putra; Ari Effendi; Agus Zainal Arifin
Jurnal Linguistik Komputasional Vol 1 No 1 (2018): Vol. 1, No. 1
Publisher : Indonesia Association of Computational Linguistics (INACL)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (892.663 KB) | DOI: 10.26418/jlk.v1i1.5

Abstract

Peringkasan Otomatis Multi Dokumen menggunakan Hirarki Kluster Lukman Hakim; Fadli Husein Wattiheluw; Agus Zainal Arifin; Aminul Wahib
Jurnal Linguistik Komputasional Vol 1 No 2 (2018): Vol. 1, No. 2
Publisher : Indonesia Association of Computational Linguistics (INACL)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26418/jlk.v1i2.86

Abstract

Multi-document summarization is a technique for getting information. The information consists of several lines of sentences that aim to describe the contents of the entire document relevantly. Several algorithms with various criteria have been carried out. In general, these criteria are the preprocessing, cluster, and representative sentence selection to produce summaries that have high relevance. In some conditions, the cluster stage is one of the important stages to produce summarization. Existing research cannot determine the number of clusters to be formed. Therefore, we propose clustering techniques using cluster hierarchy. This technique measures the similarity between sentences using cosine similarity. These sentences are clustered based on their similarity values. Clusters that have the highest level of similarity with other clusters will be merged into one cluster. This merger process will continue until one cluster remains. Experimental results on the 2004 Document Understanding Document (DUC) dataset and using two scenarios that use 132, 135, 137 and 140 clusters resulting in fluctuating values. The smaller the number of clusters does not guarantee an increase in the value of ROUGE-1. The method proposed using the same number of clusters has a lower ROUGE-1 value than the previous method. This is because in cluster 140 the similarity values ​​in each cluster experienced a decrease in similarity values.
Co-Authors Ahmad Mustofa Hadi Ahmad Mustofa Hadi Ahmad Reza Musthafa Akbar Noto Ponco Bimantoro Alhaji Sheku Sankoh Ali Fauzi Alif Akbar Fitrawan Aminul Wahib Andi Baso Kaswar Andi Baso Kaswar Anny Yuniarti Ari Effendi Arif Fadllullah Arya Yudhi Wijaya Aryo Harto Aryo Harto Biandina Meidyani Christian Sri Kusuma Aditya Christian Sri Kusuma Aditya Darlis Herumurti Diana Purwitasari Dini Adni Navastara Dyah S. Rahayu Evy Kamilah Ratnasari Fadli Husein Wattiheluw Fahmi Syuhada Syuhada Fajrin, Ahmad Miftah FATRA NONGGALA PUTRA Fitrah Maharani Humaira Galang Amanda Dwi P. Go Frendi Gunawan Gregorius Edwadr Gulpi Qorik Oktagalu Pratamasunu I Made Widiartha Ivanda Zevi Amalia Jayanti Yusmah Sari Khadijah F. Hayati Khadijah Fahmi Hayati Holle Khoirul Umam Lailly Qolby Lailly S. Qolby Luh Putu Ratna Sundari Lukman Hakim Lukman Hakim Made Agus Putra Subali Mamluatul Hani’ah Maryamah Faisol Maryamah Maryamah Muhammad Imron Rosadi Munjiah Nur Saadah Mutmainnah Muchtar Naser Jawas Pasnur Pasnur Putu Praba Santika Randy Cahya Wihandika Rarasmaya Indraswari Ratri Enggar Pawening Resti Ludviani Rigga Widar Atmagi Riska Wakhidatus Sholikah Saiful Bahri Musa Saiful Bahri Musa Saprina Mamase Siti Mutrofin Sonny Christiano Gosaria Tegar Palyus Fiqar Wincoko Putro, Fidi Yuita Arum Sari