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"ABSTRAKPemberitahuan dokumen impor yang dilakukan secara self-assessment perlu dilakukan penelitian kembali oleh pemeriksa dokumen, dikarenakan ada kemungkinan terjadinya kesalahan pemberitahuan baik yang disengaja maupun tidak disengaja. Meskipun demikian, penelitian kembali belum berjalan dengan optimal. Penelitian ini melakukan pendekatan business intelligence untuk menjawab permasalahan tersebut dengan memberikan kemampuan analisis kepada pemeriksa dokumen. Pendekatan tersebut difokuskan pada pengembangan data warehouse dengan metodologi Kimball. Hasil dari penelitian ini adalah rancangan data warehouse yang dapat dimanfaatkan untuk kebutuhan dashboard, OLAP, dan data mining untuk melakukan pemodelan pemberitahuan dokumen impor dengan menggunakan algoritme decision tree, support vector machine, dan neural network.

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ABSTRACTThe customs declaration that carried out by self-assessment needs to be re-examined by the document examiner. There is a possibility that customs declaration have an error to define even on purpose or not. However, the condition of re-examination by document examiners has not run optimally. This study approached business intelligence to answer these problems by providing analysis capabilities to document examiners. The approach was focused on developing a data warehouse with Kimballs methodology. The result of this study is the design of a data warehouse that can be used for the needs of dashboards, OLAP, and data mining to create a model of customs declaration using several algorithms, such as decision tree, support vector machine, and neural network."

"[ABSTRAKPenelitian ini mengangkat isu masalah aksesibilitas informasi Pajak Daerah dan penetapan target penerimaan. Isu aksesibilitas disebabkan oleh kompleksitas prosesdalam mengumpulkan dan mengkonsolidasi data dari beberapa sumber yangtersebar pada unit-unit pelayanan. Di sisi lain Dinas Pelayanan Pajak (DPP) harusmenetapkan target penerimaan berdasarkan data tahun sebelumnya denganmenggunakan metode tertentu. Tujuan penelitian ini untuk menjawab permasalahantersebut dengan melakukan perancangan data warehouse, mengimplementasikandalam bentuk prototipe, memproses cube untuk kepentingan analisis multi dimensional, membuat business intelligence dashboard, dan data mining untuk proyeksi penerimaan Pajak Daerah di masa mendatang. Metodologi yangdigunakan untuk merancang data warehouse adalah metodologi yang dikemukakanoleh Ralph Kimball. Hasil dari penelitian ini adalah rancangan dan implementasiprototipe data warehouse, business intelligence dashboard, dan proyeksi penerimaan Pajak Daerah di masa mendatang yang dapat menjawab kebutuhan informasi DPP.

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ABSTRACTThis research addresses both local taxes information accessibility and revenue target setting issues. The accessibility issue arise from the complexity of compilingprocess since these data have to be gathered and consolidated from several sourcesacross many tax offices. Simultaneously the Local Tax Authority (Dinas PelayananPajak-DPP) has to set annual revenue target which usually derived from time seriesdata by implementing a certain revenue forecasting method. The purposes of thisresearch is to solve the accessibility issue and provide a scientific forecastingmethod by designing data warehouse, implementing its prototype, processing thecubes for multi dimensional analysis, providing a business intelligence dashboard,and mining the data which used in the forecasting process. This research uses datawarehouse design methodology provided by Ralph Kimball. The outcomes of thisresearch are data warehouse design and prototype, business intelligence dashboard, and local taxes revenue forecasting method to provide the information as needed by DPP. ;This research addresses both local taxes information accessibility and revenue target setting issues. The accessibility issue arise from the complexity of compilingprocess since these data have to be gathered and consolidated from several sourcesacross many tax offices. Simultaneously the Local Tax Authority (Dinas PelayananPajak-DPP) has to set annual revenue target which usually derived from time seriesdata by implementing a certain revenue forecasting method. The purposes of thisresearch is to solve the accessibility issue and provide a scientific forecastingmethod by designing data warehouse, implementing its prototype, processing thecubes for multi dimensional analysis, providing a business intelligence dashboard,and mining the data which used in the forecasting process. This research uses datawarehouse design methodology provided by Ralph Kimball. The outcomes of thisresearch are data warehouse design and prototype, business intelligence dashboard, and local taxes revenue forecasting method to provide the information as needed by DPP. , This research addresses both local taxes information accessibility and revenue target setting issues. The accessibility issue arise from the complexity of compilingprocess since these data have to be gathered and consolidated from several sourcesacross many tax offices. Simultaneously the Local Tax Authority (Dinas PelayananPajak-DPP) has to set annual revenue target which usually derived from time seriesdata by implementing a certain revenue forecasting method. The purposes of thisresearch is to solve the accessibility issue and provide a scientific forecastingmethod by designing data warehouse, implementing its prototype, processing thecubes for multi dimensional analysis, providing a business intelligence dashboard,and mining the data which used in the forecasting process. This research uses datawarehouse design methodology provided by Ralph Kimball. The outcomes of thisresearch are data warehouse design and prototype, business intelligence dashboard, and local taxes revenue forecasting method to provide the information as needed by DPP. ]"

"Learn about the emergence and evolution of IT in the enterprise, see how machine learning is transforming business intelligence, and discover various cognitive artificial intelligence solutions that complement and extend machine learning. In this book, author Rohit Kumar explores the challenges when these concepts intersect in IT systems by presenting detailed descriptions and business scenarios. He starts with the basics of how artificial intelligence started and how cognitive computing developed out of it. He'll explain every aspect of machine learning in detail, the reasons for changing business models to adopt it, and why your business needs it. Along the way you'll become comfortable with the intricacies of natural language processing, predictive analytics, and cognitive computing. Each technique is covered in detail so you can confidently integrate it into your enterprise as it is needed. This practical guide gives you a roadmap for transformin g your business with cognitive computing, giving you the ability to work confidently in an ever-changing enterprise environment. You will: See the history of AI and how machine learning and cognitive computing evolved Discover why cognitive computing is so important and why your business needs it Master the details of modern AI as it applies to enterprises Map the path ahead in terms of your IT-business integration Avoid common road blocks in the process of adopting cognitive computing in your business."

"The volume contains 6 research papers, which have been carefully reviewed and selected from 12 submissions, plus the 3 keynotes presented at the workshop. The topics cover all stages of the business intelligence cycle, including capturing of real-time data, handling of temporal or uncertain data, performance issues, event management, and the optimization of complex ETL workflows."