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"ABSTRAKPemakaian energi pada data center berbasis cloud computing semakin besar maka dari itu diperlukan penghematan pemakaian energi pada komponen data center seperti server dan switch. Skripsi ini membahas evaluasi pemakaian energi pada data center berbasis cloud computing menggunakan simulator GreenCloud yang merupakan ekstensi dari Network Simulator 2 (NS2). Simulasi evaluasi diuji pada arsitektur two-tier, three-tier dan three-tier high-speed dengan tipe Computationally Intensive Workload (CIW) dengan penerapan metode penghematan Dynamic Voltage and Frequency Scaling (DVFS), Dynamic Network Shutdown (DNS) dan DVFS+DNS. Hasil yang diperoleh menunjukkan bahwa penerapan dengan metode DNS menunjukkan penghematan yang paling efisien yaitu penghematan sekitar 69,13% pada server dan hampir 100% pada switch.

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ABSTRACTEnergy consumption in the cloud computing data center is huge. Energy consumption in the data center consists of computation energy consumption, communication energy consumption and non-IT data center facilities energy consumption. In this project we evaluate IT data center energy consumption such as server and switch using GreenCloud Simulator, that is an extension of Network Simulator 2 (NS2). Simulation applied is in the three kind of data center architectures such as two-tier, three-tier and three-tier high-speed with energy saving method such as Dynamic Voltage and Frequency Scaling (DVFS) mechanism, Dynamic Network Shutdown (DNS) mechanism, and both DVFS and DNS mechanism. The type of workload is Computationally Intensive Workload (CIW). The results indicate that the most efficient energy saving mechanism is DNS which saves energy the average of 69,13% on server and almost 100% on switch."

"Keberadaan data center pada sistem cloud computing sangat besar artinya. Data center yang terletak pada lapisan IaaS pada sistem cloud berisi komponen fisik yang meliputi komponen komputasi seperti server dan switch dan komponen non komputasi seperti sistem pendingin dan pengaturan suhu. Seiring dengan meningkatnya jumlah pengguna data center, maka konsumsi daya listrik pada data center akan meningkat. Telah diusulkan skema penghematan energi pada data center yakni skema DVFS dan DNS. Pada penelitian ini telah disimulasikan menggunakan GreenCloud, yang merupakan ekstensi dari NS2, kepada tiga macam arsitektur data center yakni two-tier, three-tier dan three-tier high-speed dengan jenis workload adalah High Performance Computing HPC. Penerapan skema penghematan meliputi skema DVFS dan DNS saja serta DVFS dan DNS sekaligus. Dari hasil yang diperoleh menunjukkan bahwa penerapan skema DNS menunjukkan hasil terbaik karena berhasil melakukan penghematan rata-rata sebesar 63,42% pada server dan hampir 100% pada switch.

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The existence of a data center in the cloud computing system was huge. Data center is located on the IaaS layer cloud systems containing physical component includes computing components such as servers and switches and non-computing components such as cooling systems and temperature regulation. Along with the increasing number of users of data center, then the electric power consumption in the data center will increase. Energy conservation schemes have been proposed in the data center is DNS and DVFS.

In this study has been simulated using GreenCloud, which is an extension of NS2, the three kinds of data center architecture these are two-tier, three-tier and three-tier high-speed with the type of data center workloads is HPC High Performance Computing. The applications of the savings schemes include schemes DVFS only, DNS only and both DVFS and DNS. From the results obtained indicate that the application of the DNS control scheme is the best because it managed to save an average of 63.42% on the server and almost 100% on the switch for all data center architecture."

"Dalam era revolusi industri 4.0, integrasi teknologi menjadi kunci untuk meningkatkan produktivitas di sektor manufaktur. Dalam konteks ini, penggunaan Cloud Computing, Internet of Things (IoT), dan Machine Learning (ML) memainkan peran penting. IoT dan Cloud Computing digunakan untuk mengelola proses pengumpulan dan pengolahan data, terutama dari sensor mesin. Data ini kemudian dapat digunakan untuk pelatihan model ML, khususnya dalam kasus Predictive Maintenance. Predictive Maintenance bertujuan untuk memprediksi kapan suatu mesin memerlukan perawatan. Dalam penelitian sebelumnya, pendekatan masalah hanya memilih satu metode (klasifikasi atau regresi). Oleh karena itu, penelitian ini menciptakan metode Predictive Maintenance yang menggabungkan keduanya. Model yang dikembangkan menggunakan dua jenis pendekatan: Random Forest Tree untuk klasifikasi dan LSTM (Long Short-Term Memory) dengan Fully Connected layer untuk prediksi. Hasil pengujian menunjukkan bahwa model yang menggunakan LSTM untuk klasifikasi dan regresi mencapai akurasi 100%. Diikuti dengan hasil recall, precission, dan F-1 score yang mencapai 1.00. Oleh karena itu, LSTM dapat dianggap sebagai algoritma terbaik untuk Predictive Maintenance dalam industri manufaktur.

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In the era of the 4th industrial revolution, technology integration is key to improving productivity in the manufacturing sector. In this context, the use of Cloud Computing, Internet of Things (IoT), and Machine Learning (ML) plays a crucial role. IoT and Cloud Computing are used to manage the process of data collection and processing, especially from machine sensors. This data can then be used for ML model training, particularly in the case of Predictive Maintenance. Predictive Maintenance aims to predict when a machine requires maintenance. In previous research, the problem approach often involved choosing only one method (classification or regression). Therefore, this study created a Predictive Maintenance method that combines both approaches. The developed model uses two types of approaches: Random Forest Tree for classification and LSTM (Long Short-Term Memory) with a Fully Connected layer for prediction. Test results show that the model using LSTM for both classification and regression achieves 100% accuracy. Additionally, the recall, precision, and F-1 score results also reach 1.00. Therefore, LSTM can be considered the best algorithm for Predictive Maintenance in the manufacturing industry."

"Data kehadiran adalah data yang penting baik di lingkup sekolah, universitas, maupun perkantoran untuk karyawan. Presensi yang berupa tandatangan dapat dipalsukan oleh siapa saja dan kapan saja. Sehingga dibutuhkan sebuah perangkat yang dapat mempermudah proses absensi sekaligus mendeteksi keterlambatan siswa/pegawai sebelum memasuki ruangan. Skripsi ini mengembangkan Smart Presence System berbasis Face Recognition dengan machine learning yang dirancang dengan komputasi pada awan (Cloud Computing) dan komputasi pada sebuah node/titik (Fog Computing). Skripsi ini melakukan perbandingan performa Smart Presence System yang dibangun dengan Cloud Computing menggunakan layanan AWS Face Rekognition dan Fog Computing yang ditulis menggunakan bahasa Python dengan library OpenCV yang menggunakan perangkat Raspberry Pi sebagai titik komputasi. Penulis telah melakukan pengujian perbandingan waktu komputasi, penggunaan memori, serta penggunaan biaya antara Cloud Computing dan Fog Computing. Pengujian waktu komputasi dilakukan dengan menggeser router/titik uji sejauh 3 meter, 5 meter dan 7 meter dari sensor kamera. Pengujian waktu komputasi pada Cloud Computing didpat sebesar 11.02 detik, 2.99 detik dan 3.02 detik dengan total penggunaan memori sebesar 0.0042 MB dan total biaya yang diperlukan untuk membangun rancangan Cloud Computing sebesar Rp2.819.516 dalam penggunaan 12 bulan. Dan rata-rata waktu untuk komputasi pada fog sebesar 0.723 detik, 0.99 detik, 1.94 detik dengan total penggunaan memori sebesar 540MB dan total biaya untuk membangun rancangan ini sebesar Rp2.220.00 dalam penggunaan 12 bulan.

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Attendance document is an important thing in schools, universities, and offices for employees. Attendance is usually done by giving a signature on a piece of paper, and it can be forged by anyone. In school, attendance is usually done manually by the teacher and it takes time. So we need a device that can simplify the attendance process and can not be forged. This thesis has developed a Smart Presence System with machine learing designed with Cloud Computing and Fog Computing. This Thesis compared the performance of The Smart Presence System that built with Cloud Computing using AWS Rekognition and Fog Computing that built in Raspberry pi and written in python and library Opencv. The author has tested the comparison of Cloud Computing and Fog Computing in Computing Time, Memory usage and Cost. Computing time testing is done by shifting the router/test point as far as 3 meters, 5 meters, and 7 meters. The computing time on Cloud Computing were 11.02s, 2.99s, and 3.02s with total memory usage of 0.0042MB and the total cost is Rp.2.819.516 in 12 months of use. And The computing time on Fog Computing were o.72s, 0.99s, and 1.94s with the total memory usage of 540MB and the total cost to build this architecture is Rp2.220.000 in 12 months of use."

"This book analyses the various security threats in cloud computing. A host-based IDS (HIDS) using signature verification is developed and implemented for the concerned security issues. Further, owing to the vulnerability of distributed denial of service (DDoS) attacks in cloud computing, a network based IDS (NIDS) is developed and implemented against such attacks. The performance of these IDS is verified in the Cloud scenario as well against the standard data set. Finally, a simple data storage and security model is developed and implemented for the Cloud computing scenario. The contents of this book will be of interest to researchers and professionals alike."

""The first chapter gives an overview of cloud computing at a level accessible to a lay person. To motivate the reasons for a paradigm shift in the way we compute and store information, we introduce the concept of network-centric computing and network-centric content. A brief discussion of peer-to-peer systems, a first step in the shift from local to remote data storage and processing follows. The chapter continues with a discussion of technological advances that have made cloud computing possible and of the economical reasons why this new paradigm is attractive for many users and applications. Then we take a closer look at the cloud computing delivery models, Software as a Service (SaaS), Platform as a Service (PaaS), and Infrastructure as a Service (IaaS). SaaS gives the users capability to use applications supplied by the service provider but allows no control of the platform or the infrastructure. PaaS gives the capability to deploy consumer-created or acquired applications using programming languages and tools supported by the provider. IaaS allows the user to deploy and run arbitrary software, which can include operating systems and applications. The new paradigm raises ethical questions and has significant vulnerabilities each dissected in separate sections. Finally, the chapter presents the major challenges faced by this new paradigm. The chapter concludes with a overview of the literature and with a historic perspective"--"

"Teknologi Cloud Computing merupakan tren teknologi informasi saat ini yang memanfaatkan jaringan internet untuk penyediaan akses ke berbagai layanan TI. Teknologi ini diprediksikan akan semakin berkembang karena layanan cloud menawarkan berbagai keunggulan seperti efektivitas biaya, fleksibilitas layanan, kemudahan akses, kecepatan proses komputasi dan lain-lain. Terdapat juga faktor-faktor yang menjadi penghambat untuk adopsi cloud seperti faktor keamanan dan privasi data, lambatnya akses internet, proses migrasi yang rumit, dan lain-lain. Berbagai faktor tersebut menjadi pertimbangan organisasi untuk melihat benefit yang diperoleh jika adopsi layanan cloud dilakukan. Karya akhir ini akan mengidentifikasikan prioritas dari faktor-faktor seperti efektifitas biaya, efektivifitas keamanan, kebutuhan organisasi, koneksi internet, reliabilitas, dan kepercayaan terhadap provider dalam kaitan dengan adopsi layanan cloud computing di organisasi, serta identifikasi model layanan cloud computing yang paling sesuai untuk organisasi.Penelitian ini mengumpulkan data dari organisasi dengan metode survey. Organisasi dipilih dengan cara purposive sampling dengan menyebarkan kuesioner yang diisi oleh staf yang kompeten/ahli dengan masalah cloud computing atau bertanggungjawab terhadap infrastruktur TI di organisasi. Data primer yang dikumpulkan selanjutnya diolah dengan metode Analytic Hierarchy Process. Hasilnya adalah bahwa urutan prioritas dari faktor-faktor yang berpengaruh dalam adopsi layanan cloud computing di organisasi adalah faktor keandalan/reliability (0.237), faktor keamanan/security (0.224) dan faktor koneksi internet (0.175). Untuk model layanan cloud yang paling sesuai untuk organisasi, urutannya adalah layanan Infrastruktur (0.557), layanan Aplikasi (0.246) dan layanan Platform (0.196).

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Cloud Computing technology is a recent information technology trends that utilize the Internet connections to provide access to a range of IT services. This technology is expected to further evolve as cloud services offer many advantages such as cost effectiveness, service flexibility, ease of access, swift computing process, etc. There are also some factors listed as an obstacle in cloud computing adoption such as security concern, data privacy, slow internet access, migration process, etc. These various factors will be considered by organization to analyze the benefits obtained if cloud service adoption is performed. This thesis will identify the priority of factors such as cost effectiveness, security effectiveness, organization needs, internet connection, reliability, and trust in cloud provider in influencing cloud service adoption in organizations and also identifying cloud service model that most suited with organizations.

This study collects data from organizations using survey methods. Organizations selected by purposive sampling in which questionnaires filled out by competent staff or cloud computing experts in organization or staff that responsible for IT infrastructure. Primary data collected then processed using Analytic Hierarchy Process. This resulting prioritization of factors that influencing adoption of cloud services in the organization which are : Reliability factor (0.237), Security factor (0.224) and Internet connection factor (0.175). In determining cloud service model that suited with organization needs, the priority order are : Infrastructure services (0.557), Application services (0.246) and Platform services (0.196)."