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All Journal Jurnal Ilmu Komputer dan Informasi Nusantara Journal of Computers and its Applications
Khairiyyah Nur Aisyah
Institut Teknologi Sepuluh Nopember
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Engineering Other

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MULTI-CLASS REGION MERGING FOR INTERACTIVE IMAGE SEGMENTATION USING HIERARCHICAL CLUSTERING ANALYSIS Khairiyyah Nur Aisyah; Syadza Anggraini; Novi Nur Putriwijaya; Agus Zainal Arifin; Rarasmaya Indraswari; Dini Adni Navastara
Jurnal Ilmu Komputer dan Informasi Vol 12, No 2 (2019): Jurnal Ilmu Komputer dan Informasi (Journal of Computer Science and Information
Publisher : Faculty of Computer Science - Universitas Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (893.612 KB) | DOI: 10.21609/jiki.v12i2.757

Abstract

In interactive image segmentation, distance calculation between regions and sequence of region merging is being an important thing that needs to be considered to obtain accurate segmentation results. Region merging without regard to label in Hierarchical Clustering Analysis causes the possibility of two different labels merged into a cluster and resulting errors in segmentation. This study proposes a new multi-class region merging strategy for interactive image segmentation using the Hierarchical Clustering Analysis. Marking is given to regions that are considered as objects and background, which are then referred as classes. A different label for each class is given to prevent any classes with different label merged into a cluster. Based on experiment, the mean value of ME and RAE for the results of segmentation using the proposed method are 0.035 and 0.083, respectively. Experimental results show that giving the label on each class is effectively used in multi-class region merging.
PERINGKASAN TEKS MULTI-DOKUMEN BERDASARKAN METODE SENTENCE EXTRACTION DAN WORD SENSE DISAMBIGUATION Khairiyyah Nur Aisyah; Syadza Anggraini; Agus Zainal Arifin
NJCA (Nusantara Journal of Computers and Its Applications) Vol 4, No 1 (2019): Juni 2019
Publisher : Computer Society of Nahdlatul Ulama (CSNU) Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36564/njca.v4i1.89

Abstract

Memahami makna utama yang terkandung dalam beberapa dokumen tentu tidak mudah dan membutuhkan waktu yang cukup lama. Menanggapi masalah tersebut, penelitian terkait peringkasan teks dokumen secara otomatis menjadi perhatian khusus dalam beberapa tahun terakhir. Penelitian ini mengusulkan metode peringkasan teks multi-dokumen yang dapat meningkatkan relevansi antar kalimat dengan menggunakan metode sentence extraction  dan word sense disambiguation. Metode sentence extraction yang digunakan didasarkan pada sentence distribution dan part of speech (POS) tagging. Berdasarkan pengujian peringkasan teks dengan metode yang diusulkan, nilai rata-rata ROUGE-1 adalah 0,712, 0,163, 0,247 pada recall, precision,  f-measure secara berurutan. Sedangkan hasil pengujian peringksan teks multi-dokumen tanpa menggunakan word sense disambiguation mendapatkan nilai rata-rata ROUGE-1 sebesar 0,685, 0,139, 0,216 pada recall, precision, f-measure secara berurutan. Hasil penelitian menunjukkan bahwa penggunaan metode sentence extraction dan word sense disambiguation pada peringkasan teks multi-dokumen dapat meningkatkan kualitas hasil peringkasan teks.
Co-Authors Agus Zainal Arifin Dini Adni Navastara, Dini Adni Novi Nur Putriwijaya Rarasmaya Indraswari Syadza Anggraini