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"This book introduces social manufacturing, the next generation manufacturing paradigm that covers product life cycle activities that deal with Internet-based organizational and interactive mechanisms under the context of socio-technical systems in the fields of industrial and production engineering. Like its subject, the book's approach is multi-disciplinary, including manufacturing systems, operations management, computational social sciences and information systems applications. It reports on the latest research findings regarding the social manufacturing paradigm, the architecture, configuration and execution of social manufacturing systems and more. Further, it describes the individual technologies enabled by social manufacturing for each topic, supported by case studies. The technologies discussed include manufacturing resource minimalization and their socialized reorganizations, blockchain models in cybersecurity, computing and decision-making, social business relationships and organizational networks, open product design, social sensors and extended cyber-physical systems, and social factory and inter-connections.This book helps engineers and managers in industry to practice social manufacturing, as well as offering a systematic reference resource for researchers in manufacturing. Students also benefit from the detailed discussions of the latest research and technologies that will have been put into practice by the time they graduate."

"Permasalaban: sistem informasi secara nyata membantu upaya peningkatan mutu suatu rumah sakit. Orientasi pada mutu pelayanan harus terus dik:embangkan, sejalan tuntutan masyarakat yang terus bertambah karena masyarakat akan dengan mudah membandingkan pelayanan yang didapat. Peran alat bantu berupa DSS (Decision Support System) akan sangat berguua dalam menetapkan dan membantu adanya keputusan yang unggul. Rumah Sakit "X" sebagai saJah satu rumah sakit terbesar di Jakarta mempunyai sistem informasi yang mengintegrasikan seluruh data. Selama ini sistem telah berjalan dengan memenuhi fungsinya dalam memproses informasi-infonnasi yang ada, tetapi hal tersebut belum menjamin keamanan informasi yang ada. Tujuan: mengetahui seberapa jauh penerapan keamanan sistem infurmasi (security system) di Rumah Sakit "X", menganalisis dan mendesain DSS (Decision Support System) untuk audit keamanan sistem informasi Rumah Sakit "X", dan melaksaaakan uji coba DSS (Decision Support System) di Rumah Sakit "X". Metode Penelitian: analisis data pada penelitian ini menggunakan analisis data kuantitatif deskriptif dengan pengujian DSS audit keamanan sistem informasi Rumah Sakit "X". Peogumpulan data menggunakan instrumen berupa pengisian kuisiODer dan form data pada DSS yang telah dibuat yang sebelumnya dilakukan pelatiban pengguaaan perangkat lunak: tersebut. Hasil: basil audit keamanan sistem infonnasi Rumah Sakit "X" menggunakan DSS me unjukkan dengan jumlah nilai perolehan 75 (59".4). Kesimpulan: kondisi keamanan sistem informasi Rumah Sakit "X" masuk dalam kategori kuraag aman, DSS pada audit

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Problem Statement: the real information system helps to increase the quality of hospital. The oriented of service must be developed continually. According society needed more service, so that they can be easy to compare the service. The role of the tool DSS (Decision Support System) will be useful to state and help for getting excellent decision. "X" Hospital is one of the biggest hospital in Jakarta has information system that coverage's all data. The system has worked with its function in processing all information but it doesn't guarantee the security system.

Purpose: to know how for the application of security system at "X" Hospital, to analyze and design DSS for security system audit, and try out DSS in "X" Hospital.

Research Method: to analyze data on research using descriptive quantitative for testing DSS security system audit in "X" Hospital, to get data using questioner and form at DSS it has training for using this software.

Result: the result of security system audit in "X" Hospital uses DSS that show the security system at that hospital is unsafe with score 7S (S -"). of security system at "X" Hospital shows unsafe category, Audit DSS at "X" Hospital consist of S steps input, processing. information, analyzing and report DSS has been made it helps and make easier to lalwe audit for security system at "X" Hospital.

Recommendations: to give information about the essential audit for security system, using DSS software for security system information, to increase human resource quality."

"This book describes the implementation of green IT in various human and industrial domains. Consisting of four sections: “Development and Optimization of Green IT”, “Modelling and Experiments with Green IT Systems”, “Industry and Transport Green IT Systems”, “Social, Educational and Business Aspects of Green IT”, it presents results in two areas – the green components, networks, cloud and IoT systems and infrastructures; and the industry, business, social and education domains. It discusses hot topics such as programmable embedded and mobile systems, sustainable software and data centers, Internet servicing and cyber social computing, assurance cases and lightweight cryptography in context of green IT. Intended for university students, lecturers and researchers who are interested in power saving and sustainable computing, the book also appeals to engineers and managers of companies that develop and implement energy efficient IT applications."

"Perusahaan XYZ menerapkan Customer Life Cycle atau CLC yang sudah disesuaikan dengan kebutuhan perusahaan demi menjaga loyalitas pengguna. Tak hanya menjaga loyalitas, Perusahaan XYZ menerapkan CLC guna memperluas bisnis yang dijalani olehnya. Dengan bantuan teknologi, CLC dapat dengan mudah untuk dianalisis lebih mendalam. Teknologi yang digunakan berupa pembelajaran mesin. Pembelajaran mesin ini diimplementasikan untuk mendapatkan insight dari data yang dimiliki Perusahaan XYZ. Dalam mendapatkan insight tersebut, digunakan beberapa metode seperti Support Vector Machine, Logistic Regression, Gradient Boosting, Random Forest, Decision Tree, dan FPGrowth. Insight yang didapatkan selanjutnya ditampilkan dalam bentuk visualisasi data yang diaplikasikan ke dalam website. Terdapat tiga permasalahan berbeda yaitu prediksi pembeli potensial, prediksi produk yang akan dibeli, dan prediksi waktu pembelian berikutnya. Permasalahan pertama dapat diselesaikan dengan model Logistic Regression dengan f1-score sebesar 76.35%. Permasalahan kedua diselesaikan dengan model FP-Growth dengan nilai minimum support dan confidence sebesar 0.001. Untuk permasalahan ketiga dapat diselesaikan dengan model Decision Tree dengan nilai akurasi 78.76% dan f1-score sebesar 77.01%. Dilakukan pula pengujian terhadap response time serta SQL query yang digunakan pada setiap endpoint yang bekerja sebagai aktor untuk melakukan distribusi data kepada aplikasi frontend dan aktor untuk melakukan update database. Terakhir, dilakukan pula pengujian terhadap visualisasi data. Pengujian terhadap visualisasi data dilakukan secara kualitatif. Pengujian ini dilakukan dengan menerapkan beberapa tipe visualisasi data untuk tiap business question yang ada. Setelah itu, dilakukan perbandingan pada tiap tipe visualisasi data sehingga mendapatkan visualisasi data yang tepat untuk tiap business question yang ada.

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XYZ Company implements customized Customer Life Cycle or CLC that fits with company’s needs in order to maintain user loyalty. Not only maintaining user loyalty, XYZ Company implements CLC in order to expand its business. With the help of technology, CLC can be easily analyzed with more depth. Technology that is being used within this research is machine learning. Machine learning is implemented to gain insights from data owned by Company XYZ. While obtaining insights, machine learning use several various methods such as Support Vector Machine, Logistic Regression, Gradient Boosting, Random Forests, and Decision Trees. The insights obtained from machine learning are displayed in the form of data visualization that is applied to website. Examination on the machine learning model was formed with different data balancing techniques. Examination using Undersampling balancing technique along with Decision Tree model gives the highest f1-score value at 88.70%. Examination were also conducted on the response time and SQL queries were also carried out for each endpoint that works as an actor to distribute data to frontend applications and actors to update the database. Finally, examination and comparison is conducted on data visualization using qualitative approach. Moreover, this examination is conducted by applying several types of data visualization for each existing business questions. At the end, comparisons were made for each type of data visualization to get the optimum visualization regarding each business question."

"PT Data Sinergitama Jaya (PT DSJ) sebagai penyedia layanan data center bagi pihak ketiga, dihadapkan pada tantangan dalam mengelola data center-nya yaitu efisiensi sumber daya infrastruktur data center yang rendah. Untuk membantu mengatasi masalah rendahnya efisiensi sumber daya infrastruktur data center yang rendah tersebut, sesuai dengan best practice yang ada, PT DSJ berencana untuk menggunakan tool Data Center Infrastructure Management (DCIM). Produk DCIM yang ada di pasaran cukup banyak, sedangkan biaya investasi yang harus dikeluarkan cukup besar serta DCIM adalah sistem yang cukup kompleks, untuk itu diperlukan analisis yang komprehensif atas alternatif produk DCIM yang akan digunakan. Analisis perbandingan produk DCIM dilakukan dengan menggunakan metode Analytic Hierarchy Process dengan membandingkan alternatif produk DCIM yang ada dengan kriteria kualitas perangkat lunak berdasarkan standar ISO/IEC 25010 dan kriteria bisnis. Hasil akhir pemeringkatan menunjukkan peringkat produk DCIM yang paling tepat bagi PT DSJ berturut-turut adalah StruxureWare, RaMP, Cormant-CS, dcTrack dan Nlyte DCIM.

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PT Data Sinergitama Jaya as a data center provider that give data center service for third party; facing challenge in managing their data center; which is low efficiency of their infrastructure resources. In order to solve data center infrastructure resources inefficiency problem, following known best practice PT DSJ is planned to implement Data Center Infrastructure Management system. Currently there are several DCIM product that available in the market. Considering DCIM implementation cost is high and DCIM is a complex system, therefore a comprehensive analysis has to be performed before deciding which DCIM product that will be implemented. The process of selecting the most suitable DCIM product was done by using AHP methode that compared between alternatives with software quality criteria that follow ISO/IEC 25010 standard quality model and business criteria. The final result of prioritation process is this following order: StruxureWare, RaMP, Cormant-CS, dcTrack and Nlyte DCIM"

"Industri telekomunikasi Indonesia saat ini sedang berada pada tahap pertumbuhan yang sangat pesat seiring dengan berkembangnya teknologi informasi yang terkait dengan telekomunikasi. Di tengah persaingan industri ini yang sangat ketat, strategi untuk mempertahankan pelanggan untuk tetap loyal menggunakan layanan lebih baik daripada strategi untuk mengakuisisi pelanggan baru (Yeshwanth, Raj, & Saravanan, 2011). Oleh sebab itu, PT XL Axiata Tbk (XL) menjalankan kegiatan churn retention dalam upaya menjaga pelanggan mereka untuk tetap setia. Namun demikian, tingkat churn pelanggan di lima bulan terakhir pada tahun 2012 tidak mencapai KPI yang telah ditetapkan. Salah satu penyebabnya adalah rendahnya akurasi dari model yang digunakan untuk memprediksi pelanggan yang akan churn. Penambahan variabel-variabel baru yang lebih relevan dapat meningkatkan akurasi dari model. Penelitian terdahulu seperti yang dilakukan oleh S. Rossett & E. Neumann (2012) dengan memperhitungkan customer value, dan penelitian yang dilakukan oleh W. Gruszczynski & P. Arabas (2011) yang memasukan variabel social network ke dalam model, terbukti dapat meningkatkan akurasi dari churn model. Hasil kegiatan modeling dalam penelitian ini menghasilkan churn model baru untuk pelanggan low value dengan menambahkan variabel social network, dan churn model baru untuk pelanggan high value tanpa menambahkan variabel social network.

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Currently, telecommunication industry in Indonesia growing fastly, inline with the growth of information technology related to telecommunications. It is always better to retain a customer than having to find a new customer in the present competitive environment (Yeshwanth, Raj, & Saravanan, 2011). To align with that, PT XL Axiata Tbk (XL) do churn retention to keep their subscriber. But in last five months of 2012, subscriber churn rate is higher than targeted. One of the reason for this is accuracy of churn prediction model getting worst. Addition of relevan variable can increase model accuracy.

In the previous research, S. Rossett & E. Neumann (2012) proving that customer value variable can improve their churn prediction model. W. Gruszczynski & P. Arabas (2011) also tried to improve their churn model by adding some variable related to social network. This research produce new and better churn model comparing to previous XL’s churn model. This new model predicting churn for low value subscriber and high value subscriber with different model. Also, the social network variable included in the new churn model. "

"Contents : Introduction -- Reality check : the permeating trends of social media & social business -- A social media strategy : the framework for the ever-changing world of social media -- Determining objectives and background for your social -- Audit your social -- Acknowledgments."

"This book provides the technical foundation for processing combined transactional and analytical operations in the same database. In the year since we published the first edition of this book, the performance gains enabled by the use of in-memory technology in enterprise applications has truly marked an inflection point in the market. The new content in this second edition focuses on the development of these in-memory enterprise applications, showing how they leverage the capabilities of in-memory technology. "