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All Journal Jurnal Antropologi: Isu-Isu Sosial budaya Jurnal The Messenger GANDRUNG: Jurnal Pengabdian Kepada Masyarakat ALTRUISTIK : Jurnal Konseling dan Psikologi Pendidikan Komunika: Jurnal Dakwah dan Komunikasi IBDA': Jurnal Kajian Islam dan Budaya Al-Ma`mun: Jurnal Kajian Kepustakawanan dan Informasi Solidaritas: Jurnal Pengabdian Kartika: Jurnal Studi Keislaman Surya Informatika Journal of Computing Theories and Applications Hujjah: Jurnal Ilmiah Komunikasi dan Penyiaran Islam Lentera: Jurnal Ilmu Dakwah dan Komunikasi Transaction on Informatics and Data Science Jurnal empower : jurnal pengembangan masyarakat Islam
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Department of Informatics, Faculty of Science and Technology, Universitas Islam Negeri Prof. K.H. Saifuddin Zuhri Purwokerto
Religion Arts Humanities Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Engineering Languange, Linguistic, Communication & Media Library & Information Science Physics 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 Ajeng Ayu Suryani; Ummi Athiyah; Yohani Setiya Rafika Nur; Warto
Transactions on Informatics and Data Science Vol. 1 No. 1 (2024)
Publisher : Department of Informatics, Faculty of Science and Technology, UIN Prof. K.H. Saifuddin Zuhri, Purbalingga, Indonesia

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 Nuruddin Safriandono Agus Sriyanto Ahsin, Kahfie Ahmad Ajeng Ayu Suryani Aris Saefulloh Arnold Adimabua Ojugo Asmiran, Atika Andrian Daud Yefkanius Nassa De Rosal Ignatius Moses Setiadi Devi Alviana Dian Kristiawan Nugroho Eli Ermawati Ersa Balqis, Mardiani Etika Kartikadarma Fathan Fathan Fauziah, Nike Mutiara Fita Sheila Gomiasti Gilman Habibi Haniati, Umi Hidayati, Okta Nurul Imam Bukhori Jutono Gondohanindijo Layinah, Ayuni Mokhamad Zainal Anwar Mudzrikatun Chabibah Muhammad Syahrul Wafda Muslikh, Ahmad Rofiqul Nike Mutiara Fauziah Nur Kholis Nurul Ngatiqoh Rahman Latif Alfian, Rahman Latif Rindha Widyaningsih Subowo, Edy Syahroni Wahyu Iriananda Ummi Athiyah Yohani Setiya Rafika Nur