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All Journal Primary: Jurnal Pendidikan Guru Sekolah Dasar International Journal of Education, Science, Technology, and Engineering (IJESTE) Santhet: (Jurnal Sejarah, Pendidikan Dan Humaniora) International Journal of Social Learning (IJSL) Al Ma mun Jurnal Kajian Kepustakawanan dan Informasi Al-Ma`mun: Jurnal Kajian Kepustakawanan dan Informasi Solidaritas: Jurnal Pengabdian Journal of Advanced Technology and Multidiscipline (JATM) KRONIK: Journal of History Education and Historiography Jurnal KALANDRA Journal of Contemporary Issues in Elementary Education Naditira Widya Transaction on Informatics and Data Science Jurnal Istimā, Jurnal Pengabdian kepada Masyarakat: Inklusi Sosial dan Pemberdayaan Masyarakat Komunitas: International Journal of Indonesian Society and Culture
Warto
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Religion Agriculture, Biological Sciences & Forestry Arts Humanities Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Social Sciences Other

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Journal : Transaction on Informatics and Data Science

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Classification of Cavendish Banana Quality using Convolutional Neural Network Suryani, Ajeng Ayu; Athiyah, Ummi; Nur, Yohani Setiya Rafika; Warto
Transactions on Informatics and Data Science Vol. 1 No. 1 (2024)
Publisher : Department of Informatics, Faculty of Da'wah, UIN Saizu Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24090/tids.v1i1.12191

Abstract

Indonesia's agricultural production is divided into two main categories: vegetables and fruits. The vegetable category includes shallots, garlic, chilies, mushrooms, spinach, cabbage, and potatoes. One of the fruit commodities from the fruit horticulture subsector is bananas, which are divided into several types, including ambon, plantains, Cavendish, pipit, and horn bananas. One of the bananas that has a good selling value in Indonesia is the Cavendish banana, but the selling value of the Cavendish banana is determined by the quality of the banana fruit. A classification process is necessary to find out the quality of bananas. We perform classification using one of the deep learning algorithms, namely Convolutional Neural Network. The experiment uses 1047 images, divided into 65% training data, 15% validation data, and 20% testing data by using epochs 20 times with 16 batch sizes, the accurate results obtained are 99%. The results indicate the effectiveness of the confusion matrix in identifying training data and detecting images. It can be concluded that using more training data leads to higher accuracy, as fewer image reading errors occur when fewer images are processed. This classification is expected to be able to classify bananas with good quality like the real condition.
Corpus Development and NER Model for Identification of Legal Entities (Articles, Laws, and Sanctions) in Corruption Court Decisions in Indonesia Subowo, Edy; Bukhori, Imam; warto
Transactions on Informatics and Data Science Vol. 2 No. 1 (2025)
Publisher : Department of Informatics, Faculty of Da'wah, UIN Saizu Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24090/tids.v2i1.13592

Abstract

This study aims to develop an annotated corpus and a deep learning-based Named Entity Recognition (NER) model to identify legal entities in Indonesian corruption court rulings. The corpus was constructed from 450 Supreme Court documents related to the Anti-Corruption Laws (Laws No. 31/1999), collected via web scraping, with semi-automatic annotation (regex) and validation by legal experts. A total of 12,000 entities (Article, Laws, Sanctions) were tagged in IOB format, creating the first specialized dataset for Indonesian corruption laws. The NER model combines the IndoBERT (pre-trained language model) architecture with a CRF layer, fine-tuned to handle legal text complexities such as hierarchical article references (paragraphs, clauses) and amended laws citations (jo.). Evaluation using 10-fold cross-validation revealed that the model achieved an F1-score of 92.3%, outperforming standalone CRF (85.1%) and BiLSTM+CRF (88.7%), particularly in detecting ARTICLE entities (F1: 93.8%). Error analysis highlighted challenges in recognizing SANCTIONS entities (F1: 87.4%) due to sentence structure variability and conjunctions. The model’s implementation could accelerate judicial decision analysis, identify violation patterns, and support sanctions recommendation systems for laws enforcement. This research also provides legal entity annotation guidelines adaptable to other legal domains. Future work should expand to other laws (e.g., ITE Laws, Criminal Code) via transfer learning and integrate knowledge graphs to enhance entity relation detection.
Co-Authors Achmad Buchori Ahsin, Kahfie Ahmad AKHMAD ARIF MUSADAD Atikoh, Ismaul Bambang Priyono Djono Estia Dwi Andini Fina Andrianti Haniati, Umi Hieronymus Purwanta Imam Bukhori Indriyani, Nina Kabib Sholeh Latifatussofiyah, Mamluah Lita Nurmawati Maulana Yusuf Arrasuly Miskawi, Miskawi Muslim Ngatmini Nugraha, Rizhal Ade Rahayu, Ratih Rara Sugiarti Rendi Nugraha Rizkiyah Amelia Sabik Khumaini Samsuri Setiani, Anggi Sholawati Cantika Dewi Subowo, Edy Suryani, Ajeng Ayu Tobing, Sheila Triyogi, Galih Wicaksono Tundjung Wahadi Sutirto Ummi Athiyah Wahyu Rizki Andifani Wijaya, Fiqih Akbar Yohani Setiya Rafika Nur Yuliarni Yuliarni