[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123169-en":3,"doc-seo-123169-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},123169,7971461740909,"Levi","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Precision Document Transaction Type Classifier Using Machine Learning Techniques","This paper develops a precision document transaction type classifier using machine learning to identify transaction categories in line with the Ease of Doing Business Law (RA 11032), supporting streamlined government services and improved delivery. Existing government documents are converted into a dataset and used to train and evaluate Naïve Bayes, Bi-LSTM, and BERT models. Results show the BERT model as the most accurate, efficient, and precise. Software is built with Agile methodology and quality is assessed using ISO/IEC 25010:2011, yielding a general high mean score of 4.25 (Excellent) across reliability, efficiency, and overall performance metrics.","Data Science : Journal of Computing and Applied Informatics Vol.9, No.1 (2025) 57-75  \nData Science : Journal Of Computing And Applied Informatics  \nJournal homepage: [https://jocai.usu.ac.id](https://jocai.usu.ac.id)  \nPrecision Document Transaction Type Classifier Using Machine Learning Techniques  \nJay Carlou C. Sabado *1, Sheena I. Sapuay-Guillen2  \n1 City Government of San Fernando, La Union, City of San Fernando, La Union, 2500, Philippines  \n2Don Mariano Marcos Memorial State University-MLUC, City of San Fernando, La Union, 2500, Philippines  \n*[Corresponding Author: j](Corresponding Author: jsabado0012@student.dmmmsu.edu.ph)[sabado0012@student.dmmmsu.edu.ph](Corresponding Author: jsabado0012@student.dmmmsu.edu.ph)  \n\n| ARTICLE INFO |\n| --- |\n| Article history:\u003Cbr>Received 10 November 2024 Revised 07 Oktober 2024\u003Cbr>Accepted 20 Desember 2024 Available online 31 January 2025\u003Cbr>E-ISSN: 2580-829X\u003Cbr>P-ISSN: 2580-6769 |\n| How to cite:\u003Cbr>Jay Carlou C. Sabado and Dr. Sheena I. Sapuay-Guillen,“Precision Document Transaction Type Classifier Using Machine Learning Techniques,” Journal of Computing and Applied Informatics, vol. V9, no. 1, Jan. 2025, doi: 10.32734/jocai.v9.i1- 19945 |\n\nThis work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International.  \n[http://doi.org/10.32734/jocai.v9.i1-19945](http://doi.org/10.32734/jocai.v9.i1-19945)  \n\n| ABSTRACT |\n| --- |\n| This paper aimed to develop a Precision Document Transaction Type Classifier using machine learning to identify transaction types, aligning with the Ease of Doing Business Law (RA 11032), which aims to streamline government services and improve service delivery. With the use of existing government documents, adataset was created and processed for the training and evaluation of models, including Naïve Bayes, Bidirectional Long Short-Term Memory (Bi-LSTM), and Bidirectional Encoder Representations from Transformer (BERT) . The BERT Model was the most accurate, efficient, and precise among other models. For the development of the software application Agile Methodology was used to ensure iterative progress and adaptability during the development phase. For the software quality evaluation, it was assessed using ISO/IEC 25010:2011, achieving a general high score mean of 4.25 corresponding to a descriptive equivalent of Excellent covering various software quality metrics demonstrating reliability, efficiency and overall performance.\u003Cbr>Keyword: Precision Document Transaction Type Classifier, Agile Methodology, Software Quality, Machine Learning, Ease of Doing Business Law, Republic Act 11032 |\n| ABSTRAK |\n\nPenelitian ini bertujuan untuk mengembangkan Pengklasifikasi Jenis Transaksi Dokumen Presisi menggunakan pembelajaran mesin untuk mengidentifikasi jenis transaksi, sejalan dengan Undang-Undang Kemudahan Berusaha (RA 11032), yang bertujuan untuk merampingkan layanan pemerintah dan meningkatkanpemberian layanan. Dengan menggunakan dokumen pemerintah yang ada, sebuah dataset dibuat dan diproses untuk pelatihan dan evaluasi model, termasuk Naïve Bayes, Bidirectional Long Short-Term Memory (Bi-LSTM), dan Bidirectional Encoder Representations from Transformer (BERT) . Model BERT adalah yang paling akurat, efisien, dan tepat di antara model-model lainnya. Untuk pengembangan aplikasi perangkat lunak, Metodologi Agile digunakan untuk memastikan kemajuan berulang dan kemampuan beradaptasi selama fasepengembangan. Untuk evaluasi kualitas perangkat lunak, evaluasi dilakukandengan menggunakan ISO/IEC 25010: 2011, mencapai rata-rata skor tinggi secaraumum sebesar 4,25 yang sesuai dengan deskriptif yang setara dengan Sangat Baik yang mencakup berbagai metrik kualitas perangkat lunak yang menunjukkan keandalan, efisiensi, dan kinerja secara keseluruhan.  \nKeyword: Rprime RSA, Extended Tiny Encryption Algorithm, Kriptografi, Kriptografi Hibrida, Pesan Cepat  \n1. Introduction  \nElectronic governance or e-governance was defined as a modern approach to delivering governme","cbCaisuRvGoBB9ne","https://ap.wps.com/l/cbCaisuRvGoBB9ne","pdf",484449,1,19,"English","en",105,"# Article Info\n## Article history and citation\n# Abstract\n## Keywords\n# Introduction","[{\"question\":\"What is the main goal of the precision document transaction type classifier?\",\"answer\":\"To classify transaction types from documents using machine learning, supporting government service streamlining under RA 11032.\"},{\"question\":\"Which machine learning models are evaluated in the paper?\",\"answer\":\"Naïve Bayes, Bidirectional Long Short-Term Memory (Bi-LSTM), and BERT are trained and evaluated using a prepared dataset.\"},{\"question\":\"Why is Agile methodology used and how is software quality measured?\",\"answer\":\"Agile methodology supports iterative, adaptable development. Software quality is evaluated using ISO/IEC 25010:2011, achieving a high mean score of 4.25 (Excellent).\"}]","Precision Document Transaction Type Classifier Using Machine Learning Techniques | PDF",1785815012,48,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"precision-document-transaction-type-classifier-using-machine-learning-techniques","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/precision-document-transaction-type-classifier-using-machine-learning-techniques/123169/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What is the main goal of the precision document transaction type classifier?","Question",{"text":75,"@type":76},"To classify transaction types from documents using machine learning, supporting government service streamlining under RA 11032.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which machine learning models are evaluated in the paper?",{"text":80,"@type":76},"Naïve Bayes, Bidirectional Long Short-Term Memory (Bi-LSTM), and BERT are trained and evaluated using a prepared dataset.",{"name":82,"@type":73,"acceptedAnswer":83},"Why is Agile methodology used and how is software quality measured?",{"text":84,"@type":76},"Agile methodology supports iterative, adaptable development. 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