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All Journal International Journal of Electrical and Computer Engineering Seminar Nasional Aplikasi Teknologi Informasi (SNATI) Jurnal Ilmu Komputer dan Informasi Jurnal INKOM Jurnal Simetris TELKOMNIKA (Telecommunication Computing Electronics and Control) JSI: Jurnal Sistem Informasi (E-Journal) JUTI: Jurnal Ilmiah Teknologi Informasi Journal of ICT Research and Applications Jurnal Edukasi dan Penelitian Informatika (JEPIN) JTERA (Jurnal Teknologi Rekayasa) Jurnal Infomedia GUIDENA: Jurnal Ilmu Pendidikan, Psikologi, Bimbingan dan Konseling Jurnal Sistem Cerdas Jurnal Linguistik Komputasional Indonesian Journal of Electrical Engineering and Computer Science Jurnal Sistem Informasi BACA: Jurnal Dokumentasi dan Informasi
Ayu Purwarianti
Institut Teknologi Bandung
Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Education Electrical & Electronics Engineering Industrial & Manufacturing Engineering Library & Information Science Mechanical Engineering Social Sciences Other

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Journal : Journal of ICT Research and Applications

 1 
Rhetorical Sentences Classification Based on Section Class and Title of Paper for Experimental Technical Papers Afrida Helen; Ayu Purwarianti; Dwi H. Widyantoro
Journal of ICT Research and Applications Vol. 9 No. 3 (2015)
Publisher : LPPM ITB

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5614/itbj.ict.res.appl.2015.9.3.5

Abstract

Rhetorical sentence classification is an interesting approach for making extractive summaries but this technique still needs to be developed because the performance of automatic rhetorical sentence classification is still poor. Rhetorical sentences are sentences that contain rhetorical words or phrases. Rhetorical sentences not only appear in the contents of a paper but also in the title. In this study, features related to section class and title class that have been proposed in a previous research were further developed. Our method uses different techniques to reach automatic section class extraction for which we introduce new, format-based features. Furthermore, we propose automatic rhetoric phrase extraction from the title. The corpus we used was a collection of technical-experimental scientific papers. Our method uses the Support Vector Machine (SVM) algorithm and the Naïve Bayesian algorithm for classification. The four categories used were: Problem, Method, Data, and Result. It was hypothesized that these features would be able to improve classification accuracy compared to previous methods. The F-measure for these categories reached up to 14%. 
Improvement of Fuzzy Geographically Weighted Clustering-Ant Colony Optimization Performance using Context-Based Clustering and CUDA Parallel Programming Nila Nurmala; Ayu Purwarianti
Journal of ICT Research and Applications Vol. 11 No. 1 (2017)
Publisher : LPPM ITB

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5614/itbj.ict.res.appl.2017.11.1.2

Abstract

Geo-demographic analysis (GDA) is the study of population characteristics by geographical area. Fuzzy Geographically Weighted Clustering (FGWC) is an effective algorithm used in GDA. Improvement of FGWC has been done by integrating a metaheuristic algorithm, Ant Colony Optimization (ACO), as a global optimization tool to increase the clustering accuracy in the initial stage of the FGWC algorithm. However, using ACO in FGWC increases the time to run the algorithm compared to the standard FGWC algorithm. In this paper, context-based clustering and CUDA parallel programming are proposed to improve the performance of the improved algorithm (FGWC-ACO). Context-based clustering is a method that focuses on the grouping of data based on certain conditions, while CUDA parallel programming is a method that uses the graphical processing unit (GPU) as a parallel processing tool. The Indonesian Population Census 2010 was used as the experimental dataset. It was shown that the proposed methods were able to improve the performance of FGWC-ACO without reducing the clustering quality of the original method. The clustering quality was evaluated using the clustering validity index.
Efficient Utilization of Dependency Pattern and Sequential Covering for Aspect Extraction Rule Learning Fariska Zakhralativa Ruskanda; Dwi Hendratmo Widyantoro; Ayu Purwarianti
Journal of ICT Research and Applications Vol. 14 No. 1 (2020)
Publisher : LPPM ITB

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5614/itbj.ict.res.appl.2020.14.1.4

Abstract

The use of dependency rules for aspect extraction tasks in aspect-based sentiment analysis is a promising approach. One problem with this approach is incomplete rules. This paper presents an aspect extraction rule learning method that combines dependency rules with the Sequential Covering algorithm. Sequential Covering is known for its characteristics in constructing rules that increase positive examples covered and decrease negative ones. This property is vital to make sure that the rule set used has high performance, but not inevitably high coverage, which is a characteristic of the aspect extraction task. To test the new method, four datasets were used from four product domains and three baselines: Double Propagation, Aspectator, and a previous work by the authors. The results show that the proposed approach performed better than the three baseline methods for the F-measure metric, with the highest F-measure value at 0.633.
Enhancing Natural Language Inference Performance with Knowledge Graph for COVID-19 Automated Fact-Checking in Indonesian Language Muharram, Arief Purnama; Purwarianti, Ayu
Journal of ICT Research and Applications Vol. 19 No. 1 (2025)
Publisher : DRPM - ITB

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5614/itbj.ict.res.appl.2025.19.1.2

Abstract

Automated fact-checking is a key strategy to overcome the spread of COVID-19 misinformation on the internet. These systems typically leverage deep learning approaches through natural language inference (NLI) to verify the truthfulness of information based on supporting evidence. However, one challenge that arises in deep learning is performance stagnation due to a lack of knowledge during training. This study proposes using a knowledge graph (KG) as external knowledge to enhance NLI performance for automated COVID-19 fact-checking in the Indonesian language. The proposed model architecture comprises three modules: a fact module, an NLI module, and a classifier module. The fact module processes information from the KG, while the NLI module handles semantic relationships between the given premise and hypothesis. The representation vectors from both modules are concatenated and fed into the classifier module to produce the final result. The model was trained using the generated Indonesian COVID-19 fact-checking dataset and the COVID-19 KG Bahasa Indonesia. Our study demonstrates that incorporating KGs can significantly improve NLI performance in fact-checking, achieving a maximum accuracy of 0.8616. This suggests that KGs are a valuable component for enhancing NLI performance in automated fact-checking.
Co-Authors Adhitia, Rama Adhitya, Maulana Krisna Adi Pancoro Afrida Helen Alit Kesatria Mendala Aminuddin, Amir Andri Fachrur Rozie Andria Arisal Antoni, Jefry Arief Rahman Arry Akhmad Arman Arry Akhmad Arman Aswin Juari Dessi Puji Lestari Devi Munandar Diana Permata Sari Diana Sari Dianadewi Riswantini Dwi H. Widyantoro Dwi Hendratmo Widyantoro Ekasari Nugraheni Hadi Saputra Halim, Ismail Syababun Hari Bagus Firdaud Haryono Putro, Ibnu Prastowo Hendradjaja, Bayu Herawati, Neng Ayu Herry Sujaini Herry Sujaini Hibatullah, Muhammad Helmi Imamah Imamah Intan Jelita Saragih Iping S Suwardi Iping Supriana Iping Supriana Iping Supriana Iping Supriana Iping Supriana Iping Supriana Iping Supriana Irfan Afif Iskandar, Yanti Rubiyanti Kridanto Surendro Krishna Aurelio Noviandri KUSPRIYANTO Lisa Madlberger Martalia, Anastasia Mia Mengko, Tati Rajab Miranti Jatnika Riski Mochammad Ibrahim Muh Habibi Haidir Muharram, Arief Purnama Neng Ayu Herawati Nila Nurmala Novi Yusliani Nugraha Priya Utama Prathama, Ubaidillah Ariq Purnomo Husnul Khotimah Putra, Helmy Satria Martha Rama Adhitia Rama Adhitia Rama Adhitia Rifky Raymond, Rifky Ririn Dwi Agustin Ruskanda, Fariska Zakhralativa Samuel Cahyawijaya Saputra, Neva Supanto Supanto Titin Pramiyati Titin Pramiyati Titin Pramiyati Titin Pramiyati Tupamahu, Meldrin Untari Novia Wisesty Utama, Nugraha Priya Wiwin Suwarningsih Wiwin Suwarningsih wiwin suwarningsih Wiwin Suwarningsih Wiwin Suwarningsih Yoga Hanggara Yuhana, Umi Laili Zakiy Firdaus Alfikri Zakiy Firdaus Alfikri