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"Data merupakan unsur terpenting dalam setiap penelitian dan pendekatan ilmiah. Metodologi sains data digunakan untuk memilah, memilih dan mempersiapkan sejumlah data untuk diproses dan dianalisis. Teknologi big data mampu mengumpulkan data dengan sangat banyak dari berbagai sumber dengan tujuan untuk mendapatkan informasi dengan visualisasi tren atau menyingkapkan pengetahuan dari suatu peristiwa yang terjadi baik dimasa lalu, sekarang, maupun akan datang dengan kecepatan pemrosesan data sangat tinggi. Analisis prediktif memberikan wawasan analisis lebih dalam dan kemunculan machine learning membawa analisis data ke tingkat yang lebih tinggi dengan bantuan teknologi kecerdasan buatan dalam tahap pemrosesan data mentah. Analisis prediktif dan machine learning menghasilkan laporan berbentuk visual untuk pengambil keputusan dan pemangku kepentingan. Berkenaan dengan keamanan siber, big data menjanjikan kesempatan dalam rangka untuk mencegah dan mendeteksi setiap serangan canggih siber dengan memanfaatkan data keamanan internal dan eksternal."

"Data are the prominent elements in scientific researches and approaches. Data Science methodology is used to select and to prepare enormous numbers of data for further processing and analysing. Big Data technology collects vast amount of data from many sources in order to exploit the information and to visualise trend or to discover a certain phenomenon in the past, present, or in the future at high speed processing capability. Predictive analytics provides in-depth analytical insights and the emerging of machine learning brings the data analytics to a higher level by processing raw data with artificial intelligence technology. Predictive analytics and machine learning produce visual reports for decision makers and stake-holders. Regarding cyberspace security, big data promises the opportunities in order to prevent and to detect any advanced cyber-attacks by using internal and external security data."

"Kegagalan bayar kartu kredit merupakan risiko yang perlu dikelola, sehinggaperbankan perlu menerapkan credit scoring untuk memprediksi pemegang kartuyang berisiko default. Seiring dengan perkembangan teknologi, terdapat berbagaimetode credit scoring, sehingga perlu adanya telaah mengenai efektivitas metodemetodecredit scoring. Penelitian ini memiliki tujuan untuk memprediksi defaultberdasarkan data demografi, payment, dan savings nasabah, dan membandingkanefektivitas dari beberapa metode credit scoring yang berkembang, dan mengetahuivariabel apa saja yang mempengaruhi dalam hasil permodelan. Sehingga,perusahaan dapat memitigasi resiko lebih awal dan dapat mengoptimalkan revenuedari nasabah tidak beresiko lainnya. Selain itu ditemukan pula hubungan ketikasebuah cut off point dengan akurasi dan sensitivity. Dari variabel-variabel yangdigunakan dalam model, utilisasi dan pembayaran kartu kredit menjadi variabelyang sangat berpengaruh dalam permodelan, selain itu jenis kelamin, profesi,jumlah penghasilan, status kepemilikan tempat tinggal dan tingkat pendidikan akhirmenjadi variabel yang penting dalam memprediksi default. Dalam hasil permodelanrandom forest menghasilkan hasil yang paling baik secara keseluruhan, dan modellogistic regression merupakan permodelan yang memiliki defiasi lebih sedikit stabil dibandingkan hasil permodelan lainnya.

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Failure to pay for credit cards is a risk that needs to be managed, so banks need toapply credit scoring to predict cardholders who are at risk of default. Along withtechnological developments, there are various methods of credit scoring, so there isa need for a review of the effectiveness of credit scoring methods. This study aimsto predict default from demographic, payments, and savings data from credit cardholder and compare the effectiveness of some of the growing credit scoringmethods, and to know what factors influence in the modeling results. Thus,companies can mitigate risks early and can optimize revenue from other risklesscustomers. In this research, the result shows that random forest modeling withoutfeature selections has the best overall result, and logistic regression model is amodel that has less defiation than other modeling result. In addition there is also arelationship when a cut off point with accuracy and sensitivity. From the variabelsused in the model, utilization and credit card payment to be highly influentialvariabel in modeling, besides gender, profession, income, residence status andeducation level become an important variabel in this research."

"Dengan ketatnya persaingan pada Industri asuransi kendaraan bermotor dan besarnya kemungkinan hilangnya pelanggan (customer churn), perusahaan dituntut untuk semakin memperhatikan pengelolaan hubungan pelanggan. Faktanya mempertahankan pelanggan dinilai lebih efisien secara sumber daya dibanding jika harus mencari pelanggan baru. Dengan perkembangan teknik pengelolaan (CRM) dan besarnya pertumbuhan data yang ada, kemungkinan untuk menganalisa perilaku pelanggan dan mengurangi terjadinya churn dapat dilakukan dengan analisa big data dan machine learning. Dalam penelitian ini, algoritma machine learning dalam membuat model terbaik yang dapat memprediksi churn pada pelanggan. Data yang digunakan merupakan 27.013 data transaksi pembelian dari PT. Asuransi XYZ yang merupakan salah satu perusahaan asuransi otomotif terbesar di Indonesia. Setelah melakukan eksplorasi dan descriptive analytics, peneliti membandingkan akurasi antara logistic regression, decision tree, dan random forest dalam memprediksi churn pada pelanggan. Ditemukan bahwa random forest memiliki skor akurasi total terbaik yaitu 87.74% diikuti dengan decision tree dan logistic regression. Lebih lanjut lagi, tenure, presentase komisi, dan jumlah premi adalah feature yang paling penting dari model. Hasil dari penelitian diharapkan dapat membantu perusahaan dalam mengurangi churn pada pelangan dengan memprediksi lebih awal dan membuat kampanye retensi pelanggan yang tepat

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With the tightness of the competition in auto insurance market and the possibilities of company loss because of customer churning, insurer company should put more concern in customer relationship management. Moreover, there is fact of acquiring new costumers is more expensive than retaining one. Fortunately, due to current advancement in CRM and vast growth of data provided, the possibilities to learn customer behavior from the data is becoming feasible to retain the customer. This research purpose is to used machine learning algorithm to build model provide best accuracy to predict customer churn. We use 27.013 transaction data from XYZ Insurance Ltd, one of major auto-insurer in Indonesia. After exploring the data with the descriptive analysis, we conduct predictive analysis model with logistic regression, decision tree, and random forest to determine which algorithm give the best accuracy. This research found that random forest model has highest accuracy score (87,74%) followed by decision tree and logistic regression. Furthermore, tenure, percentage of commision, and amount of premium are the most feature that impacting customer churn. This result could help insurer company to reducing customer churn by creating right marketing campaign to retain customers."

"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."

"Dalam beberapa tahun terakhir, bank semakin gencar memperbanyak nasabah prioritasnya seiring dengan pertumbuhan kekayaan dari individu dengan kekayaan di atas Rp 5 milyar di Indonesia. Penelitian ini bertujuan untuk mengidentifikasi parameter yang menentukan kriteria nasabah regular di Bank ABC yang berpotensi menjadi nasabah prioritas (preferred) secara lebih akurat. Parameter Asset Under Management, KPR, dan Kartu Kredit dari nasabah regular diolah dengan predictive analytics dan Machine Learning untuk memprediksi nasabah yang paling mendekati profile nasabah preferred. Dengan menggunakan algoritma Decision Tree, ditemukan 17 nasabah regular (3.4% dari sample data target) yang paling berpotensi menjadi nasabah preferred. Selanjutnya metode ABC Costing digunakan untuk menghitung potensi penghematan biaya akuisisi nasabah preferred, dan dihasilkan untuk setiap nasabah preferred, dihemat biaya Rp 2.3 juta. Jika diaplikasikan ke 10,300 nasabah regular yang berpotensi menjadi nasabah preferred, akan dihasilkan penghematan sebesar Rp 23.6 Milyar dari proses akuisisi nasabah preferred di Bank ABC.

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Recently, banks are more aggressive in priority customer acquisitions as the amount of wealthy customers’ with more than IDR 5 billions wealth grew rapidly in Indonesia. This research aims to identify parameters determining the criterion of ABC Bank’s regular customers which potentially can be upgraded to preferred customers with higher accuracy. Asset Under Management, Mortgage, and Credit Card facilities parameters were used and processed using predictive analytics and Machine Learning altogether to predict which regular customer have most similirities with preferred customers. Moreover, with Decision Tree algorithm, it was found 17 regular customers (3.4% of sample target data) most potentially similar and most likely can converted into preferred customers. Subsequently, ABC Costing method was used to calculate reduction of acquisition cost of preferred customers and resulted in IDR 2.3 millions save of acquisition cost per customer, or if applied to 10,300 total potential regular customers, it will yield IDR 23.6 billions acquisition cost saving for ABC Bank."