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"Penelitian ini bertujuan untuk mengeksplorasi peran Business Intelligence dan dashboard interaktif dalam penyajian skor penilaian Lestari Awards 2024. Dengan meningkatnya kompleksitas data dalam program penghargaan tersebut, diperlukan suatu sistem yang dapat menyajikan data secara transparan, efisien, dan mudah dipahami. Melalui pendekatan kualitatif deskriptif, penelitian ini menggabungkan data sekunder berupa skor penilaian dan data primer dari wawancara dengan Head of Data KG Media untuk memahami penggunaan business intelligence dan dashboard dalam mendukung pengambilan keputusan. Hasil penelitian menunjukkan bahwa dashboard interaktif berbasis BI dapat menyajikan data secara visual dan dinamis, mempercepat proses evaluasi, dan meningkatkan transparansi. Penggunaan Tableau sebagai platform visualisasi data terbukti efektif dalam membantu tim internal KG Media dalam menilai kinerja perusahaan secara lebih cepat dan akurat. Penyesuaian desain berdasarkan umpan balik pengguna juga berperan penting dalam meningkatkan efektivitas dashboard. Secara keseluruhan, penelitian ini mengungkapkan bagaimana Business Intelligence dan dashboard interaktif dapat meningkatkan transparansi dan efisiensi dalam proses pengambilan keputusan pada Lestari Awards 2024, serta menunjukkan kontribusi teknologi dalam penyajian data yang lebih efektif.

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This study aims to explore the role of Business Intelligence (BI) and interactive dashboards in presenting the evaluation scores for the Lestari Awards 2024. As data complexity within the award program increases, there is a growing need for a system that can present information in a transparent, efficient, and easily understandable manner. Using a descriptive qualitative approach, the research combines secondary data in the form of evaluation scores with primary data from interviews with the Head of Data at KG Media to understand how BI and dashboards support decision-making. The findings reveal that BI-based interactive dashboards can present data visually and dynamically, accelerate the evaluation process, and enhance transparency. The use of Tableau as a data visualization platform proved effective in helping KG Media's internal team assess company performance more quickly and accurately. User feedback also played a crucial role in refining the dashboard design to improve its effectiveness. Overall, this study highlights how Business Intelligence and interactive dashboards can improve transparency and efficiency in the decision-making process for the Lestari Awards 2024, demonstrating the valuable contribution of technology in delivering more effective data presentation."

"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."

"PT. XYZ merupakan perusahaan yang bergerak di bidang perawatan pesawat terbang atau yang disebut dengan Maintenance Repair Overhaul (MRO). Dashboards Business Intelligence (BI) hadir di tengah bisnis perusahaan dan diharapkan mampu memberikan data-data yang akurat secara cepat guna aktivitas pengambilan keputusan strategis oleh para pemimpin perusahaan. Ekspektasi awal dari kedua proyek tersebut mampu memberikan data-data dashboards yang cepat dan tentunya akurat. Realita menunjukan data yang digunakan untuk mendukung kegiatan analisa strategis dan pengambilan keputusan tidak cepat selama kurang lebih 3 tahun dashboards BI diimplementasikan. Salah satu penyebabnya adalah rendahnya penggunaan dashboards BI yang menjadi fokus masalah penelitian. Tujuan penelitian yaitu mendapatkan faktor-faktor penyebab penggunaan dashboard rendah pada PT. XYZ dan apa saja perbaikan untuk meningkatkan penggunaan dashboard yang rendah. Penelitian dilakukan dengan menggunakan mixed method antara kuesioner user testing dan heuristic evaluation serta inspeksi yang dilakukan oleh responden. Hasil dari kedua metode adalah mendapatkan faktor-faktor yang memengaruhi penggunaan dashboards rendah. Faktor-faktor tersebut di antaranya adalah flexibility, efficiency, error detected, error control and help, user`s satisfaction, dan application`s behaviour yang akan memberikan rekomendasi bagi PT XYZ berdasarkan hasil inspeksi dari ahli-ahli BI. Saran penelitian didapatkan berdasarkan hasil inspeksi beberapa ahli BI yang mengacu pada seluruh faktor perbaikan dashboard BI PT XYZ. Adapun saran penelitian di antaranya adalah meningkatkan efisiensi, kecepatan akses, fleksibilitas,, user`s satisfaction melalui dashboard yang user friendly, dan perbaikan beberapa error serta penambahan fitur help sebagai informasipenggunaan dashboard. Seluruh saran penelitian menjadi rekomendasi peneliti dan telah divalidasi oleh beberapa ahli BI.

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ABSTRACTPT. XYZ is a company that engaged in aircraft maintenance or what is called Maintenance Repair Overhaul (MRO). Dashboards Business Intelligence (BI) is present in the middle of the company`s business and is expected to be able to provide accurate data quickly for strategic decision making activities by company leaders. Initial expectations of the two projects were able to provide fast and certainly accurate dashboards data. Reality shows the data used to support strategic analysis and decision making activities is not fast for approximately 3 years BI dashboards are implemented. One reason is the low usability ofBI dashboards which is the focus of research problems. The research objective is to get the factors causing the low dashboard usability at PT. XYZ and any improvements to improve dashboard usability is low. The study was conducted using a mixed method between the user testing questionnaire and heuristic evaluation and inspection conducted by respondents. The results of the two methods are the factors that influence the usability of low dashboards. These factors include flexibility, efficiency, error detected, error control and help, user satisfaction, and application`s behavior that will provide recommendations for PT XYZ based on the results of inspections from BI experts. Research suggestions were obtained based on the results of inspections from several BI experts which referred to all factors of BI XYZ dashboard improvement. The research recommendations include increasing efficiency, speed of access, flexibility, user`s satisfaction through a user friendly dashboard, and fixing some errors and adding help features as a dashboard usage information. All research recommendations are recommended by researchers and have been validated by several BI experts."

"Corporations and governmental agencies of all sizes are embracing a new generation of enterprise-scale business intelligence (BI) and data warehousing (DW), and very often appoint a single senior-level individual to serve as the enterprise BI/DW program manager. This book is the essential guide to the incremental and iterative build-out of a successful enterprise-scale BI/DW program comprised of multiple underlying projects, and what the enterprise program Manager must successfully accomplish to orchestrate the many moving parts in the quest for true enterprise-scale business intelligence and data warehousing.Author Alan Simon has served as an enterprise business intelligence and data warehousing program management advisor to many of his clients, and spent an entire year with a single client as the adjunct consulting director for a $10 million enterprise data warehousing (EDW) initiative. He brings a wealth of knowledge about best practices, risk management, organizational culture alignment, and other Critical Success Factors (CSFs) to the discipline of enterprise-scale business intelligence and data warehousing."

"Privasi data menjadi perhatian krusial dalam lanskap bisnis saat ini, terutama dengan Big Data dan Analytics (BD&A) serta kecerdasan buatan (AI). Diulas melalui empat artikel, lanskap analitika bisnis yang terus berkembang membahas aspek sejarah, tantangan implementasi, dan perannya yang transformatif. Sambil menyoroti manfaat BD&A dan AI, esai menekankan kebutuhan mendesak akan kesadaran dan langkah-langkah proaktif untuk mengatasi isu privasi data. Esai ini menekankan dampak negatif dari pengumpulan data yang luas dan menganjurkan perlindungan informasi pribadi melalui regulasi yang ketat. Diskusinya menekankan kesiapan organisasi dan pengembangan kepemimpinan untuk mengatasi tantangan dalam adopsi BD&A sambil memastikan perlindungan data yang sensitif. Esai ini menyimpulkan dengan mengajak untuk lebih mendalami privasi data melalui studi kasus di masa depan untuk mengurangi risiko dalam penanganan informasi rahasia di lingkungan digital yang dinamis.

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Data privacy is a critical concern in today's business landscape, particularly with Big Data and Analytics (BD&A) and artificial intelligence (AI). Explored through four articles, the evolving business analytics landscape addresses historical aspects, implementation challenges, and its transformative role. While highlighting the benefits of BD&A and AI, the essay emphasizes the urgent need for awareness and proactive measures to address data privacy issues. It underscores the drawbacks of extensive data collection and advocates for safeguarding personal information through stringent regulations. The discussion stresses organizational readiness and leadership development to navigate challenges in BD&A adoption while ensuring sensitive data protection. The essay concludes by calling for deeper exploration of data privacy in future case studies to mitigate risks in handling confidential information in the dynamic digital environment."

"[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."