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All Journal International Journal of Electrical and Computer Engineering Jurnal Ilmu Komputer dan Informasi Jurnal Teknik ITS Jurnal Buana Informatika TELKOMNIKA (Telecommunication Computing Electronics and Control) JUTI: Jurnal Ilmiah Teknologi Informasi Annual Research Seminar Register: Jurnal Ilmiah Teknologi Sistem Informasi Jurnal Ilmiah FIFO IKRA-ITH Informatika : Jurnal Komputer dan Informatika Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) Journal of Computer Science and Informatics Engineering (J-Cosine) Sewagati Nusantara Journal of Computers and its Applications
Dini Adni Navastara, Dini Adni
Institut Teknologi Sepuluh Nopember
Arts Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Education Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Library & Information Science Public Health Social Sciences Other

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Document Matching for Contradiction Detection in Low-Resource Legislative Texts With Self-Training and Augmentation Using Transformer Model Navastara, Dini Adni; Abdillah, Surya; Benito, Davian; Adillion, Ilham Gurat; Purwitasari, Diana
Jurnal Nasional Pendidikan Teknik Informatika: JANAPATI Vol. 14 No. 2 (2025)
Publisher : Prodi Pendidikan Teknik Informatika Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/janapati.v14i2.95954

Abstract

Detecting contradictions within low-resource legislative texts presents significant challenges due to limited labeled data, complex legal language, and the vast number of verses contained within legal documents. These contradictions can lead to legal ambiguities and disputes if not addressed effectively. To tackle this problem, this study proposes a comprehensive system that combines document matching with contradiction detection. Legal documents are first clustered based on contextual similarity, enabling a more targeted analysis of potentially contradictory verses. Among several clustering approaches tested, keyword similarity-based clustering using KeyBERT produced the highest MatchingScore of 0.6111. To overcome the scarcity of labeled data, we employed a multi-step strategy involving manual annotation, generative AI-based data augmentation, and self-training techniques. The contradiction detection model was developed using the XLM-RoBERTa architecture, trained on TPU V2 with a batch size of 64. The model achieved strong performance, with 0.978 recall, 0.9356 precision, 0.982 accuracy, and a 0.9566 F1-score, completing each epoch in 82 seconds. This integrated approach significantly reduces the complexity of contradiction detection in legislative documents while ensuring high accuracy and robustness.
PENGUKURAN KETEBALAN TULANG KORTIKAL PADA CITRA PANORAMA GIGI BERBASIS MODEL Dini Adni Navastara; Irna Dwi Anggraeni; Agus Zainal Arifin
JUTI: Jurnal Ilmiah Teknologi Informasi Vol 15, No. 1, Januari 2017
Publisher : Department of Informatics, Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j24068535.v15i1.a641

Abstract

Pengukuran ketebalan tulang kortikal pada citra panorama gigi merupakan salah satu cara yang dapat digunakan untuk mendiagnosa osteoporosis. Ketebalan tulang kortikal pada gigi merupakan predictor penting untuk mengetahui kualitas kepadatan tulang. Namun, pengukuran ketebalan tulang kortikal pada citra panorama gigi masih dilakukan secara manual oleh ahli medis. Penelitian ini mengusulkan sebuah sistem otomatis untuk mengukur ketebalan tulang kortikal pada citra panorama gigi berbasis model profil. Pengukuran ketebalan tulang kortikal terdiri dari 5 tahapan yaitu ekstraksi fitur menggunakan multiscale line operator dan gradient orientation analysis pada citra Region Of Interest (ROI), segmentasi tulang kortikal, deteksi centerline pada tulang kortikal, pemodelan profil tulang kortikal, dan estimasi tebal tulang kortikal. Metode ini dievaluasi menggunakan 30 citra panorama gigi. Berdasarkan hasil uji coba, rata-rata akurasi segmentasi tulang kortikal pada ROI paling kiri, ROI kiri-tengah, ROI kanan-tengah, dan ROI paling kanan secara berurut-turut sebesar 95.41%, 89.96%, 95.12%, dan 93.50%. Persentase rata-rata selisih ketebalan tulang kortikal antara sistem dan ground truth menggunakan uji-t dengan 95% confidence interval sebesar 96.65%.
Co-Authors Abdillah, Surya Adi Guna, I Gusti Agung Socrates Adillion, Ilham Gurat Adnan Erlangga Raharja Agus Zainal Arifin Agus Zainal Arifin Ahmad Syauqi Ahmad Syauqi Akwila Feliciano Akwila Feliciano Akwila Feliciano Pradiptatmaka Alam Ar Raad Stone Anny Yuniarti Arif Fadllullah Arif Mudi Priyatno Arya Yudhi Wijaya Atika Faradina Randa Awik Puji Dyah Nurhayati Ayu Kardina Sukmawati Baskoro Nugroho Benito, Davian Chastine Fatichah Daniel Sugianto Deni Sutaji Dewi Hidayati Diana Purwitasari Didit Prasetyo Dimas Fanny Hebrasianto Permadi Dinar Winia Mahandhira Edwin Setiawan Eha Renwi Astuti Evan Tanuwijaya Evelyn Sierra F.X. Arunanto Fahmi Syuhada Fandy Kuncoro Adianto Fandy Kuncoro Adianto Fiqey Indriati Eka Sari Gulpi Qorik Oktagalu Pratamasunu Hadziq Fabroyir Hafiz Nuzal Djufri Handayani Tjandrasa Hari Ginardi Hidayat, Husnul I Gusti Agung Socrates Adi Guna Imagine Clara Arabella Irna Dwi Anggraeni Kevin Christian Hadinata Kevin Christian Hadinata Khairiyyah Nur Aisyah Lissa Rosdiana Lissa Rosdiana Lophita Y Napitupulu Maulana, Hendra Maulana, Hendra Muhammad Farih Muhammad Fikri Sunandar Muhammad Iqbal Izzul Haq Nainik Suciati Nanik Suciati Noor Nailis Sa’adah Nova Maulidina Ashuri Novi Nur Putriwijaya Nurlita Abdulgani R. V. Hari Ginardi Rangga Kusuma Dinata Rangga Kusuma Dinata Rarasmaya Indraswari Rizka Wakhidatus Sholikah Rizqi Okta Ekoputris Safhira Maharani Safhira Maharani Safitri, Julia Salim Bin Usman Salim Bin Usman Santoso, Bagus Jati Shabrina Syifa Ghaissani Sherly Rosa Anggraeni Sherly Rosa Anggraeni Siti Rochimah Syadza Anggraini Wawan Gunawan Wijayanti Nurul Khotimah Yulia Niza Yulia Niza Zakiya Azizah Cahyaningtyas Zakiya Azizah Cahyaningtyas