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Abstrak :
Perkembangan sistem teknologi telekomunikasi yang semakin canggih dan kompleks memicu meningkatnya kegagalan ataupun kesalahan sistem dalam sistem jaringan utama dan sistem pendukung layanan telekomunikasi, serta kesalahan yang terjadi pada bisnis proses dan sumber daya manusia yang terkait. Kegagalan dan kesalahan ini menyembabkan kerugian yang ditanggung perusahaan, kerugian yang ditimbulkan dengan istilah revenue leakage atau kebocoran pendapatan. Revenue Assurance memegang peranan penting dalam pengendalian terhadap resiko revenue leakage dengan membuat kontrol dalam mendeteksi dan mencegah terjadinya kebocoran agar mampu meminimalkan biaya dan memaksimalkan potensi pendapatan. Dalam tesis ini dikembangkan metode untuk menganalisis Big data CDR untuk mengoptimalkan proses analisis pada revenue assurance control dengan menggunakan algoritma K-means Clustering. Algortima ini mengelompokkan obyek pengamatan dalam beberapa kategori yang diindikasikan sebagai titik kebocoran. Hasil kelompok yang dihasilkan dengan kategori yang beresiko tinggi memiliki anggota yang sedikit dengan tingkat nilai evaluasi akurasi cluster, R-Squared, sekitar 90%. ......In the telco industry, Revenue Assurance plays an important role to assure the company revenue from leakage. the revenue chain is established across the process and whole sophisticated system that technologically complex to provide the unstoppable services. This case increasing the probability of system or process failure leads to the leakage. Hence necessary the revenue assurance control to detect and prevent it then it can help to minimize cost and maximize revenue. In this thesis, developed the analysis method in big data CDR to optimize analysis process at revenue assurance control using K-means Clustering algorithm. The use of the K-means clustering algorithm method able to group the object areas with high risk indications of leakage. The cluster result of high risk of leakage is having low amount of member, and the cluster evaluation result of R-Squared giving the good value about 90%.

Abstrak :
In today’s fast growing digital world, the web, mobile, social networks and other digital platforms are producing enormous amounts of data that hold intelligence and valuable information. Correctly used it has the power to create sustainable value in different forms for businesses. The commonly used term for this data is Big Data, which includes structured, unstructured and hybrid structured data. However, Big Data is of limited value unless insightful information can be extracted from the sources of data. The solution is Big Data analytics, and how managers and executives can capture value from this vast resource of information and insights. This book develops a simple framework and a non-technical approach to help the reader understand, digest and analyze data, and produce meaningful analytics to make informed decisions. It will support value creation within businesses, from customer care to product innovation, from sales and marketing to operational performance. The authors provide multiple case studies on global industries and business units, chapter summaries and discussion questions for the reader to consider and explore. Big Data for Managers also presents small cases and challenges for the reader to work on – making this a thorough and practical guide for students and managers.

Abstrak :
ABSTRAK
Kartu kredit sebagai kartu pembayaran adalah produk yang dikeluarkan oleh bank dan menjadi pilihan favorit nasabah bank dalam melakukan transaksi secara offline dan online. Berbagai program promosi yang dilakukan oleh bank untuk meningkatkan penerbitan kartu untuk nasabah baru dan untuk menarik penggunaan kartu bagi para pemegang kartu kredit saat ini. Bank XYZ, sebagai salah satu penerbit kartu kredit, secara intensif menawarkan promosi kepada pelanggannya untuk bertransaksi menggunakan kartu kredit melalui berbagai media seperti SMS Blast maupun email notifikasi. Konten promosi yang dikirimkan ke pelanggan dapat mempengaruhi keputusan pelanggan untuk melakukan transaksi di merchant manapun menggunakan kartu kredit Bank XYZ. Dengan memanfaatkan analisa Big Data dengan Recency, Frequency dan Monetary (RFM) dan Association Rules, Bank XYZ dapat mengirimkan konten promosi kartu kredit yang sesuai dengan profile pelanggan. Mengirimkan konten promosi yang sesuai dengan profil pelanggan akan meningkatkan transaksi pelanggan menggunakan kartu kredit mereka. Peningkatan transaksi ini akan berkontribusi terhadap pendapatan Bank XYZ.

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ABSTRACT
Credit cards as a payment card are products issued by banks and become favorite customer`s choice to pay multiple transactions offline and online. Many promotion programs are done by banks to raise card issuances for new customer and to attract card usage for current credit card holders. Bank XYZ, as one of credit card issuer in Indonesia, is intensively offering promotions to its customer to use their credit cards through communication media such as SMS blast and email notifications. Media content may affect customer decision to purchase in any merchant using Bank XYZ credit card. By utilizing Big Data analysis with Recency, Frequency and Monetary(RFM), and Association Rules, Bank XYZ may send credit card promotional content fit with a customer profile. Sending proper promotional content fit with a customer profile will raise customer spending using their credit cards. Transactions rising contribute to Bank XYZ revenue.