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"Saat ini masih banyak mahasiswa baik S1, S2 dan S3, dan juga penelititi yang bingung bagaimana cara pengolahan dan analisis data setelah selesai dari tahap pengumpulan data, sering timbul pertayaan, “mau diapakan data di kuesioner inii? Bagaimana cara mengolahnya? Bagaimana cara Analisis datanya? Bila itu permasalahannya maka buku ini tepat digunakan dan merupakan solusi untuk menjawab pertanyaan-pertanyaan tersebut.Pembahasan buku ini dilakukan dengan bahasan yang jelas, lugas dan mudah dipahami dan mengalir seakan-akan membaca buku cerita. Untuk itu bagi mahasiswa dan peneliti yang ingin melakukan analisis data dengan benar, buku ini dapat dijadikan panduan lengkapPembahasan buku ini dilakukan secara sederhana dan komprehensif, berisi tahapan-tahapan yang seharusnya dilakukan pada kegiatan analisis data penelitian, dimulai dengan membahas konsep dasar statistik dan penelitian, dilanjutkan pembahasan bagaimana cara mengolah data yang mudah, bagaimana cara transformasi data yang mudah juga dibahas di bab selanjutnya. Bahasan berikutnya adalah cara menguji kuesioner yang tepat, dan selanjutnya dibahas cara melakukan analisis data yang benar, dimulai dari analisis deskriptif/ univariabel, analisis analitik sederhana misalnya uji T, Uji Anova,Uji Chi Squae dan Uji Korelasi. Buku ini diakhiri dengan pembahasan analisis data analitki yang komplek yaitu multivariabel. Dalam buku ini dibahas dua uji yaitu analisis regresi linier multivariate dan analisis regresi logistic multivariate."

"This textbook on practical data analytics unites fundamental principles, algorithms, and data. Algorithms are the keystone of data analytics and the focal point of this textbook. Clear and intuitive explanations of the mathematical and statistical foundations make the algorithms transparent. But practical data analytics requires more than just the foundations. Problems and data are enormously variable and only the most elementary of algorithms can be used without modification. Programming fluency and experience with real and challenging data is indispensable and so the reader is immersed in Python and R and real data analysis. By the end of the book, the reader will have gained the ability to adapt algorithms to new problems and carry out innovative analyses.This book has three parts:(a) Data Reduction: Begins with the concepts of data reduction, data maps, and information extraction. The second chapter introduces associative statistics, the mathematical foundation of scalable algorithms and distributed computing. Practical aspects of distributed computing is the subject of the Hadoop and MapReduce chapter.(b) Extracting Information from Data: Linear regression and data visualization are the principal topics of Part II. The authors dedicate a chapter to the critical domain of Healthcare Analytics for an extended example of practical data analytics. The algorithms and analytics will be of much interest to practitioners interested in utilizing the large and unwieldly data sets of the Centers for Disease Control and Prevention's Behavioral Risk Factor Surveillance System.(c) Predictive Analytics Two foundational and widely used algorithms, k-nearest neighbors and naive Bayes, are developed in detail. A chapter is dedicated to forecasting. The last chapter focuses on streaming data and uses publicly accessible data streams originating from the Twitter API and the NASDAQ stock market in the tutorials.This book is intended for a one- or two-semester course in data analytics for upper-division undergraduate and graduate students in mathematics, statistics, and computer science. The prerequisites are kept low, and students with one or two courses in probability or statistics, an exposure to vectors and matrices, and a programming course will have no difficulty. The core material of every chapter is accessible to all with these prerequisites. The chapters often expand at the close with innovations of interest to practitioners of data science. Each chapter includes exercises of varying levels of difficulty. The text is eminently suitable for self-study and an exceptional resource for practitioners."

"Museum anak merupakan salah satu wadah nonformal bagi anak untuk mendapatkan informasi. Dalam proses penyampaian informasi tersebut, diperlukan media komunikasi yang tepat berupa elemen visual yang sesuai dengan tahapan perkembangan anak sebagai pengunjung museum. Penerapan elemen visual yang sesuai dengan tahapan perkembangan kognitif anak menjadi faktor utama dalam menarik perhatian anak untuk dapat belajar dan mengolah informasi dengan maksimal. Pada tulisan ini, penulis memfokuskan pembahasan pada elemen visual dalam museum anak berupa warna, bentuk, grafis, dan teks yang baik digunakan untuk anak yang berada pada tahap praoperasional dan operasional konkret. Studi kasus dilakukan pada Museum PP-IPTEK dan ruang pamer Create! Hopscotch for Geniuses oleh teamLab yang merupakan ruang pamer yang menargetkan anak sebagai pengunjung utamanya. Pengambilan data untuk melengkapi kajian ini dilakukan dengan cara observasi dan wawancara langsung ke Museum IPTEK dan melalui laman resmi ruang pamer Create! Hopscotch for Geniuses oleh teamLab. Kedua museum atau ruang pamer tersebut kemudian dikaji berdasarkan literatur kemudian dikomparasikan. Hasil dari analisis menunjukkan bahwa elemen visual berupa warna primer, bentuk geometri dasar, grafis yang menyenangkan serta familiar bagi anak, dan teks dengan ukuran besar dan bentuk bulat merupakan jenis media komunikasi yang sesuai dengan tahapan perkembangan anak tahap praoperasional dan operasional konkret.

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Children's Museum is a non-formal forum for children to get information. In the process of delivering this information, it is necessary to have the right communication media in the form of visual elements that are in accordance with the stages of child development as museum visitors. The application of visual elements in accordance with the stages of children's cognitive development is the main factor in attracting children's attention to be able to learn and process information optimally. In this paper, the author focuses on the discussion of visual elements in the children's museum in the form of colors, shapes, graphics, and text that are good for children who are in the preoperational and concrete operational stages. The case study was conducted at the PP-IPTEK Museum and the Create! Hopscotch for Geniuses by teamLab which is an exhibition that targets children as its main visitors. Data collection to complete this study was carried out by direct observation and interviews to the Science and Technology Museum and through the official website of the Create! Hopscotch for Geniuses by teamLab. The two museums or exhibition rooms are then reviewed based on the literature and then compared. The results of the analysis show that visual elements in the form of primary colors, basic geometric shapes, graphics that are fun and familiar to children, and text with large sizes and round shapes are types of communication media that are suitable for the preoperational and concrete operational stages of child development. "

"Pesatnya perkembangan proses audit menggunakan pendekatan data analytics menawarkan manfaat kompetitif yang signifikan bagi organisasi yang dapat memanfaatkan lingkungan berbasis data, termasuk kantor akuntan publik. Namun pada saat ini belum banyak kantor akuntan publik yang memanfaatkan data analytics. Penelitian ini bertujuan untuk mengevaluasi kesiapan implementasi data analytics dalam audit pada KAP DNA, salah satu kantor akuntan publik terbesar di dunia yang berafiliasi internasional. KAP DNA telah menginisiasi penggunaan data analytics mulai dari tahun 2016. Proses penelitian ini dilakukan dengan metode kualitatif dimana narasumber dipilih berdasarkan snowball sampling sejumlah 5 narasumber yang terdiri dari manajer senior, manajer, dan senior auditor. Alat ukur evaluasi kesiapan menggunakan kriteria Acatech indeks maturitas industri milik Schuh et al. (2017) yang telah dikembangkan di penelitian Gürdür et al. (2019). Kriteria tersebut terdiri dari kesiapan sumber daya, kesiapan sistem informasi, kesiapan budaya, dan kesiapan organisasi. Berdasarkan hasil penelitian, KAP DNA memiliki tingkat kesiapan menengah secara umum. Hal ini disebabkan oleh tantangan seperti kurangnya kemampuan auditor, kekurangan sumber daya manusia tim khusus data analytics, dan perangkat yang belum memadai. KAP DNA direkomendasikan untuk mempertimbangkan menambah pelatihan terkait data analytics kepada auditor di saat low season, merekrut karyawan tambahan di tim khusus data analytics, dan menyediakan perangkat yang memadai.

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The rapid development of the audit process using a data analytics approach offers significant competitive advantages for organizations that can take advantage of a data-driven environment, including public accounting firms. However, currently not many public accounting firms are utilizing data analytics. This study aims to evaluate the readiness to implement data analytics in audits at KAP DNA, one of the world's largest public accounting firms with international affiliations. KAP DNA has initiated the use of data analytics starting in 2016. This research process was carried out using a qualitative method where the informants were selected based on snowball sampling of 5 sources consisting of senior managers, managers, and senior auditors. The measuring instrument for readiness evaluation uses the Acatech industrial maturity index criteria belonging to Schuh et al. (2017) which has been developed in the research of Gürdür et al. (2019). The criteria consist of resource readiness, information system readiness, cultural readiness, and organizational readiness. Based on the research results, KAP DNA has a medium level of readiness in general. This is due to challenges such as lack of auditor capabilities, lack of human resources for dedicated data analytics teams, and inadequate tools. KAP DNA is recommended to consider adding training related to data analytics to auditors during low season, recruiting additional employees in a dedicated data analytics team, and providing adequate tools."

"E-commerce merupakan online platform yang sedang mengalami pertumbuhan pesat dan memberikan kontribusi terhadap perekonomian internet di Indonesia selama lima tahun terakhir. E-commerce menghasilkan ulasan konsumen yang merupakan sumber informasi bagi para pemangku kepentingan. Penelitian ini melakukan analisis big data terhadap 132.085 ulasan konsumen online mengenai ponsel Xiaomi yang ditulis pada tiga situs e-commerce terbesar di Indonesia: Shopee, Bukalapak, dan Blibli dengan text mining, untuk mengidentifikasi distribusi topik, menganalisis jaringan asosiasi semantik, menemukan perbedaan pada ketiga situs, dan menganalisis hubungan antara topik dan skor penilaian ulasan. Hasil penelitian menunjukkan bahwa logistik merupakan topik yang paling banyak didiskusikan pada ketiga situs, sementara kualitas pelayanan lebih banyak didiskusikan pada Consumer-to-Consumer (C2C) daripada Business-to-Consumer (B2C) e-commerce. Atribut ponsel lebih banyak didiskusikan pada Bukalapak dan Blibli, dengan fokus topik sistem dan CPU & perangkat keras. Jaringan ulasan konsumen Bukalapak membentuk scale-free network, sementara jaringan kedua situs lainnya hanya menunjukkan karakteristik dari small-world network. Hasil regresi logistik ordinal menunjukkan bahwa 5 dari 8 topik yang dibahas dalam komentar ulasan memiliki hubungan negatif dengan skor penilaian, serta ulasan bernilai rendah cenderung memiliki komentar yang lebih panjang dan spesifik. Hasil penelitian dapat bermanfaat sebagai wawasan untuk pengembangan bagi para pemangku kepentingan di industri e-commerce.

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E-commerce is a rapidly growing online platform that contributes to Indonesias internet economy during the past five years. E-commerce generates customer reviews as a source of information for stakeholders. This study applies big data analytics toward 132,085 online reviews about Xiaomi mobile phones posted on three major e-commerce websites in Indonesia: Shopee, Bukalapak, and Blibli by text mining, in identifying their distribution of topics, analyzing semantic association network, determining differences between the three websites, also analyzing the relationship between topics and rating score. The findings show that logistics is the most highly discussed topic, while service quality is discussed more in Consumer-to-Consumer (C2C) rather Business-to-Consumer (B2C) e-commerce. Phone attributes are discussed more in Bukalapak and Blibli, focusing on system and CPU & hardware topics. The network of Bukalapaks customer reviews form a scale-free network, and the other two only have the characteristics of a small-world network. The overall results from multilinear regression and ordinal logistic regression show that 5 out of 8 topics reviewed have negative relationships with rating scores, and low-rated reviews tend to have longer and more specific review comments. The findings provide insights for e-commerce stakeholders in supporting further development."

"Praktik tax evasion merupakan praktik illegal dan termasuk pidana perpajakan. Sulitnya dilakukan pendeteksian dikarenakan sifatnya berdasarkan niatan buruk untuk tidak melaporkan penghasilan atau melebihkan beban pengeluaran. Salah satu solusinya adalah dengan menggunakan big data analytics. Oleh karena itu, penelitian ini akan membahas implementasi big data analytics untuk mengidentifikasi praktik tax evasion di Indonesia. Penelitian ini menggunakan pendekatan post-positivist dengan melihat implementasi langsung di DJP dengan melakukan wawancara mendalam dan melakukan uji pustaka. Hasil penelitian ini menunjukkan penggunaan data analytics dalam pemeriksaan pajak, atau biasa disebut sebagai audit analytics, telah dilakukan oleh DJP, salah satunya untuk mendeteksi praktik tax evasion. Metode analytics yang digunakan adalah predictive analytics untuk menguji tingkat kepatuhan wajib pajak. Kemudian, dilakukan prescriptive analytics untuk mendapatkan saran atas wajib pajak yang wajib dilakukan pemeriksaan. Terakhir, descriptive analytics yang berguna untuk menemukan bukti secara komprehensif atas kesalahan wajib pajak. Faktor pendukung diimplementasikan data analytics di DJP dikarenakan dominasi pelaporan digital dan tuntutan perkembangan zaman. Namun, terdapat faktor hambatan juga, yaitu sulitnya proses ETL, adanya ego sektoral antar lembaga, anggaran, dan SDM. Kedepannya, terdapat beberapa langkah strategis yang akan dikembangkan, yaitu optimalisasi kebijakan tax evasion dan tax avoidance, pengembangan sistem CoreTax, optimalisasi MoU atas data, dan improvisasi teknologi.

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The practice of tax evasion is an illegal practice and is a tax crime. Detection is difficult because it is based on bad intentions not to report income or overestimate expenses. One solution is to use big data analytics. Therefore, this study will discuss the implementation of big data analytics to identify tax evasion practices in Indonesia. This study uses a post-positivist approach by looking at the direct implementation of the DGT by conducting in-depth interviews and literature review. The results of this study show that the use of data analytics in tax audits, or commonly referred to as audit analytics, has been carried out by the DGT, one of which is to detect tax evasion practices. The analytical method used is predictive analytics to test the level of taxpayer compliance. Then, prescriptive analytics is carried out to get advice on taxpayers who must be audited. Finally, descriptive analytics are useful for finding comprehensive evidence of taxpayer error. The supporting factor for implementing data analytics at DGT is due to the dominance of digital reporting and the demands of the times. However, there are also obstacle factors, namely the difficulty of the ETL process, the existence of sectoral egos between institutions, budgets, and human resources. Going forward, there are several strategic steps that will be developed, namely optimizing tax evasion and tax avoidance policies, developing the CoreTax system, optimizing the MoU on data, and improvising technology."

"Fitur analitik merupakan suatu fitur yang menyediakan beberapa kemampuan business analytics yang disediakan oleh pihak online marketplace bagi para penjual di marketplace terkait. Meski telah dibuat aksesibel, hal tersebut tidak menjamin bahwa fitur analitik akan digunakan oleh para penjual di online marketplace. Fitur analitik yang dapat menyajikan informasi siap pakai bagi para penjual membuat kualitas dari informasi yang disediakan menarik untuk dikaji. Penelitian ini bertujuan untuk menganalisis faktor kualitas informasi terhadap keinginan penjual untuk mengadopsi fitur analitik yang disediakan oleh pihak online marketplace menggunakan teori technology acceptance model yang dimoderasi dengan faktor analytical decision making culture (ADMC). Penelitian ini dilakukan dengan pendekatan kuantitatif dengan menyebarkan kuesioner penelitian secara daring dan berhasil memperoleh 337 respons yang valid. Respons yang diperoleh kemudian dianalisis menggunakan metode covariance based structural equation modeling. Hasil penelitian membuktikan bahwa dari 16 hipotesis yang diajukan, 9 di antaranya berhasil diterima. Penelitian ini memperlihatkan bahwa dari 6 faktor kualitas informasi yang digunakan, hanya 4 faktor yaitu, accessibility, interpretability, relevancy, dan novelty yang terbukti dapat memengaruhi keinginan penjual untuk menggunakan fitur analitik. Efek moderasi membuktikan bahwa hubungan interpretability dengan perceived ease of use dan novelty dengan perceived usefulness menjadi lebih kuat pada lingkungan dengan nilai ADMC yang rendah. Di lain sisi, hubungan accuracy dengan perceived usefulness menjadi lebih kuat pada lingkungan dengan nilai ADMC yang tinggi. Penelitian ini memiliki beberapa kontribusi dalam menambah dan memperkaya literatur terkait pengadopsian business analytics di lingkungan online marketplace dan bermanfaat bagi pelaku penyedia layanan online marketplace dalam mengembangkan fitur analitik.

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Analytical feature is a feature that provides several business analysis capabilities provided by the online marketplace for sellers in said marketplace. Although it has been made accessible, this does not guarantee that the analytical features will be used by sellers in the online marketplace. Analytical feature that presents ready to use information to sellers makes the quality of the information provided by the analytical feature interesting to be analyzed. This study aims to analyze the effect of information quality towards the adoption of the analytical feature by sellers of online marketplace through the technology acceptance model and moderated by the role of analytical decision making culture (ADMC). This research was conducted using a quantitative approach by spreading the research online and succeeded in obtaining 337 valid responses. The responses obtained were then analyzed using the covariance-based structural equation modeling method. The results of the study proved that out of the 16 hypotheses proposed, 9 of them are successfully accepted. This study explains that out of the 6 factors of information quality used, only 4 factors, accessibility, interpretability, relevance, and novelty have been shown to influence sellers' intention to adopt the analytical feature. The moderating effect show that the relationship between interpretability towards perceived ease of use and novelty towards perceived usefulness are stronger in the environment with low value of ADMC. However, the relationship between accuracy towards perceived usefulness is stronger in an environment with high value of ADMC. This research has several contributions in adding and enriching previous literature related to the adoption of business analytics in the online marketplace environment and is useful for online marketplace providers in developing the analytical feature."

"ABSTRAKNama : Adi MulyadiProgram studi : Magister ManajemenJudul : Analisis Segmentasi Konsumen Pada Perusahaan Real Estate Menggunakan Big Data Analytics Studi pada PT. ISPI Pratama LestariPembimbing : Arga Hananto, M.Bus. Studi tentang segmentasi konsumen dipengaruhi oleh kebutuhan perusahaan untuk bersaing dengan kompetitornya dan menciptakan keunggulan kompetitif bagi perusahaannya. Segmentasi produk merupakan salah satu hal utama dalam dunia bisnis, karena kesalahpahaman dalam segmentasi konsumen dapat mengakibatkan berkurangnya pendapatan. Real estate merupakan industri senilai milyaran dolar yang sangat tersegmentasi, dikarenakan karakteristik konsumennya yang beragam. Indonesia merupakan pasar yang potensial dan bertumbuh bagi industri real estate dan perumahan, karena Indonesia memiliki jumlah penduduk yang besar sekitar 260 juta jiwa dan memiliki area geografis yang luas. Untuk menganalisa data dengan jumlah besar tersebut, perusahaan real estate menggunakan Big Data Analytics, sebagai alat untuk mendapatkan masukan yang berarti dari data tersebut. Big Data mulai banyak digunakan sebagai alat untuk mempelajari tentang kondisi atau untuk memprediksi perilaku yang mungkin terjadi melalui berbagai pemodelan analisis data. Penelitian ini menyajikan analisis segmentasi untuk membantu perusahaan pengembang real estate dalam memahami segmentasi konsumen mereka, dengan menggunakan data transaksi penjualan perusahaan periode 2013 - 2017. Analisis segmentasi dalam penelitian ini telah dikembangkan menggunakan cluster analysis, dengan menggunakan metode hierarchical clustering, Elbow Method, dan K-Means. Hasil dari cluster analysis menunjukkan bahwa terdapat 4 segmen konsumen, yang memiliki karakteristik demografis dan preferensi produk yang berbeda. Selain itu, penelitian ini juga melakukan analisis tabulasi silang untuk mengetahui hubungan antar variabel. Selanjutnya dilakukan analisis diskriminan, dari situ diketahui bahwa gaji dan harga jual merupakan 2 variabel yang secara signifikan memberikan pengaruh paling besar terhadap penentuan cluster membership. Setelah mengetahui karakteristik dan melakukan analisa, dapat diusulkan bentuk promosi yang sesuai bagi masing ndash; masing segmen.Kata kunci:Segmentasi konsumen, real estate, big data, cluster analysis, tabulasi silang

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ABSTRACTABSTRACT Name Adi MulyadiStudy Program Magister of ManagementTitle Customer Segmentation Analysis In Real Estate Using Big Data Analytics A Study In PT. ISPI Pratama LestariCounsellor Arga Hananto, M.Bus. The study of consumer segmentation is influenced by a company 39 s need to compete with its competitors and create a competitive advantage. Product segmentation is one of the main things in the business world, because misunderstanding in consumer segmentation can lead to reduced revenue. Real estate is a multi billion dollar industry that is highly segmented, due to the diverse characteristics of its customers. Indonesia is a potential and growing market for the real estate and housing industries, as Indonesia has a large population around 260 million people and has a large geographical area. To analyze such big amounts of data, real estate companies use Big Data Analytics, as a means to gain meaningful insight from the data. Big Data is widely used as a tool to learn about conditions or to predict behaviors that may occur through various data analysis models. This study presents segmentation analysis to help real estate developers to understand their customer segmentation using company sales transaction data from 2013 to 2017 period. Segmentation analysis in this research has been developed using cluster analysis, with hierarchical clustering, Elbow Method, and K Means. The results of cluster analysis show that there are 4 segments of consumers, which have different demographic characteristics and product preferences. In addition, this study also conducted cross tabulation analysis to determine the relationship between variables. Then from discriminant analysis, it is known that salary and selling price are 2 variables that significantly give the most influence on cluster membership determination. After knowing the characteristics and perform the analysis, it can be proposed the appropriate form of promotion for each segment. Key words Customer segmentation, real estate, big data, cluster analysis, cross tabulation"

"This book presents a language integrated query framework for big data. The continuous, rapid growth of data information to volumes of up to terabytes (1,024 gigabytes) or petabytes (1,048,576 gigabytes) means that the need for a system to manage and query information from large scale data sources is becoming more urgent. Currently available frameworks and methodologies are limited in terms of efficiency and querying compatibility between data sources due to the differences in information storage structures. For this research, the authors designed and programmed a framework based on the fundamentals of language integrated query to query existing data sources without the process of data restructuring. A web portal for the framework was also built to enable users to query protein data from the Protein Data Bank (PDB) and implement it on Microsoft Azure, a cloud computing environment known for its reliability, vast computing resources and cost-effectiveness."

"The book describes the emergence of big data technologies and the role of Spark in the entire big data stack. It compares Spark and Hadoop and identifies the shortcomings of Hadoop that have been overcome by Spark. The book mainly focuses on the in-depth architecture of Spark and our understanding of Spark RDDs and how RDD complements big data’s immutable nature, and solves it with lazy evaluation, cacheable and type inference. It also addresses advanced topics in Spark, starting with the basics of Scala and the core Spark framework, and exploring Spark data frames, machine learning using Mllib, graph analytics using Graph X and real-time processing with Apache Kafka, AWS Kenisis, and Azure Event Hub. It then goes on to investigate Spark using PySpark and R. Focusing on the current big data stack, the book examines the interaction with current big data tools, with Spark being the core processing layer for all types of data.The book is intended for data engineers and scientists working on massive datasets and big data technologies in the cloud. In addition to industry professionals, it is helpful for aspiring data processing professionals and students working in big data processing and cloud computing environments."