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"Supermarket adalah tempat yang sering menjadi pilihan untuk orang berbelanja. Hampir semua supermarket masih menggunakan keranjang belanja (shopping basket). Proses belanja banyak memakan waktu. Oleh karena itu dibutuhkan suatu perangkat pada smart shopping basket berbasis Internet of Things (IoT) agar kegiatan beberlanja lebih efektif dan efisien. Skripsi ini telah melakukan percobaan ekspreimental untuk sistem Edge Computing pada Smart Shopping Basket sebagai Alternatif Sistem Cloud Computing Internet of Things untuk membantu pembeli dalam kegiatan berbelanja menjadi lebih cepat. Sistem terdiri dari perangkat keras Raspberry Pi dan webcam dan perangkat lunak Python, TFLite, OpenCV dan Google Cloud Vision API untuk mendeteksi objek belanja dan mengukur berapa lama objek dideteksi. Hasil deteksi objek tersebut dikalkulasi dan dikirimkan ke end-user dengan bentuk struk hasil belanja melalui aplikasi Telegram.Penulis telah melakukan uji coba perangkat dengan 2 skenario utama yaitu Skenario #1 “Edge Computing” dan #2 “Cloud Computing”. Uji coba dilakukan dengan menggeser perangkat sejauh 0.3 meter sebanyak 10 kali dari titik acuan berupa router dengan 2 jenis propagasi yaitu Line of Sight dan Non-Line of Sight. Penulis juga memberi beberapa variabel tambahan untuk mengukur beberapa faktor yang mungkin mempengaruhi performa waktu perangkat. Varibel itu berupa resolusi gambar (480p dan 720p) dan banyak objek yang dideteksi (2 Objek dan 4 Objek). Berdasarkan uji coba skenario di atas, didapatkan waktu rata-rata total sebesar 1.75 detik untuk Skenario #1 “Edge Computing” dan 8.24 detik untuk Skenario #2 “Cloud Computing”.

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Supermarket is a place that is often the choice to fulfill their basic needs. Almost all supermarkets still use shopping basket. The shopping process takes a lot of time. Therefore, we need a device on the Internet of Things (IoT) -based smart shopping basket so that shopping activities are more effective and efficient. This thesis has conducted experimental experiments for the Edge Computing system on Smart Shopping Basket as an Alternative Cloud of Computing Internet of Things System to help shoppers shop faster. The system consists of Raspberry Pi hardware and webcam and Python, TFLite, OpenCV, and Google Cloud Vision API software to detect shopping objects and measure how long they are detected. The object detection results are calculated and sent to end-users in the form of shopping receipts through the Telegram application.

The author has tested the device with 2 main scenarios namely Scenario # 1 "Edge Computing" and # 2 "Cloud Computing". The trial was carried out by shifting the device as far as 0.3 meters 10 times from the reference point in the form of a router with 2 types of propagation namely Line of Sight and Non-Line of Sight. The author also provides several additional variables to measure several factors that might affect the device's time performance. The variable is in the form of image resolution (480p and 720p) and many objects are detected (2 Objects and 4 Objects). Based on the above scenario test, a total average time of 1.75 seconds is obtained for Scenario # 1 "Edge Computing" and 8.24 seconds for Scenario # 2 "Cloud Computing"."

"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."

""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"--"

""Key Features: Explains how to make design choices and tradeoffs to consider when building applications to run in a virtual cloud environment; Test and experiment with a live cloud system on the Aneka platform; Real-world case studies include scientific, business, and energy-efficiency considerations; Download examples and instructor support materials on the book's companion page.Cloud computing is a technological advancement that focuses on the way in which we design computing systems, develop applications, and leverage existing services for building software. It is based on the concept of dynamic provisioning, which is applied not only to services, but also to compute capability, storage, networking, and IT (Information Technology) infrastructure in general. Resources are made available through the Internet and offered on a pay-per-use basis from Cloud computing vendors. Today, anyone with a credit card can subscribe to Cloud services and deploy and configure servers for an application in hours, growing and shrinking the infrastructure serving its application according to the demand, and paying only for the time these resources have been used"--"

"Industri perbankan di Indonesia saat ini belum mengadopsi layanan cloud computing akibat dari peraturan Bank Indonesia dan Pemerintah. Penelitian ini bertujuan menjelaskan faktor-faktor yang mempengaruhi perusahaan perbankan untuk mengadopsi layanan cloud computing dilihat dari konteks technology, environtment, dan organization. Analisis ini menggunakan kerangka TOE (Technology-Organization-Environtment), teori DOI (Diffusion of Innovation), dan metoda PLS (Partial Least Square). Hasil penelitian ini menunjukkan bahwa faktor technology tidak berpengaruh signifikan bagi perusahaan perbankan untuk mengadopsi cloud computing, namun faktor organization dan environtment berpengaruh signifikan bagi perusahaan perbankan dalam mengadopsi cloud computing, dimana model yang dihasilkan dalam penelitian ini cukup kuat (moderate) sebesar 36.1 %.

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The banking industry in Indonesia has yet to adopt cloud computing services as a result of the regulation of Bank Indonesia and the Government. This study aims to explain the factors that affect the banking companies to adopt cloud computing services viewed from the context of technology, environtment, and organization. This analysis uses a framework TOE (Technology-Organization-Environment), the theory DOI (Diffusion of Innovation), and the method of PLS (Partial Least Square).

These results indicate that the technology factors had no significant effect for banking companies to adopt cloud computing, but the organization and environtment influential factors significant for the banking companies to adopt cloud computing, where the model is produced in this study is quite strong (moderate) of 36.1%."

"As part of the Syngress basics series, The basics of cloud computing provides readers with an overview of the cloud and how to implement cloud computing in their organizations. Cloud computing continues to grow in popularity, and while many people hear the term and use it in conversation, many are confused by it or unaware of what it really means. This book helps readers understand what the cloud is and how to work with it, even if it isn’t a part of their day-to-day responsibility.Authors Derrick Rountree and Ileana Castrillo explains the concepts of cloud computing in practical terms, helping readers understand how to leverage cloud services and provide value to their businesses through moving information to the cloud. The book will be presented as an introduction to the cloud, and reference will be made in the introduction to other Syngress cloud titles for readers who want to delve more deeply into the topic."

"Segmentasi semantik adalah sebuah task pada bidang computer vision yang dewasa ini menjadi semakin penting. Segmentasi semantik sendiri dapat dipakai untuk memisahkan satu benda dengan benda yang lainnya, baik pada dua dimensi maupun tiga dimensi. Segmentasi semantik tiga dimensi umumnya mengutilisasikan sebuah point cloud yang dapat diambil menggunakan sensor Light Detection and Ranging (LIDAR). Sejak 2020, Apple menyertakan sensor LIDAR pada beberapa model iPhone. Hal tersebut memungkinkan orang awam untuk merekonstruksi berbagai objek dan keadaan di sekitarnya. Berdasarkan hal tersebut, dapat dirumuskan sebuah aplikasi yang dapat membantu penggunanya untuk melakukan scan terhadap benda rumah tangga untuk mengetahui panjang, lebar, tinggi, dan volume melalui kombinasi dari segmentasi semantik dan beberapa metode lainnya. Dibandingkan juga performa beberapa model yang menjadi kandidat integrasi dengan aplikasi tersebut, yaitu Dynamic Graph Convolutional Neural Network (DGCNN), Kernel Point Convolutional Neural Network (KPConv), Point Transformer, dan Point Transformer dengan Contrast Boundary Learning (CBL). Hasil pengujian menujukkan bahwa Point Transformer dengan CBL memiliki Intersection over Union yang paling baik. Didapatkan juga bahwa DGCNN adalah model yang paling baik untuk diimplementasikan sepenuhnya pada iPhone untuk edge computing.

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Semantic segmentation is a computer vision task that has become increasingly important in recent years. Semantic segmentation can be utilized to separate one object from another in a two dimensional or three dimensional environment. Semantic segmentation normally utilizes a point cloud that can be obtained using a Light Detection and Ranging (LIDAR) sensor. As of 2020, Apple has packaged a built-in LIDAR sensor on a few iPhone models. This allows everyday users to reconstruct all sorts of objects around them. Owing to that

fact, there can be formulized an application that helps its users to find the length, width, height, and volume of an object through a combination of semantic segmentation along with a few other methods. We also compared the performance of different models as candidates to be integrated into the application, which are Dynamic Graph Convolutional Neural Network (DGCNN), Kernel Point Convolutional Neural Network (KPConv), Point Transformer, and Point Transformer with Contrast Boundary Learning (CBL). We found that Point Transformer with CBL has the best Intersection over Union result. We also found that DGCNN is the best model to be fully implemented on an iPhone for edge computing."

"Private Cloud merupakan pemodelan teknologi Cloud Computing yang hanya memberikan layanan kepada pengguna tertentu. Kebutuhan akan performa server yang baik, tentunya mempengaruhi layanan yang ditawarkan suatu provider cloud kepada penggunanya. Oleh karena itu, pembuatan Cloud Computing dengan memilih layanan Private Cloud pada skripsi ini dilakukan dengan membuat simulasi nyata pada jaringan fisik menggunakan CentOS dengan Eucalyptus di dalamnya. Metode instalasi menggunakan konfigurasi Cloud in a Box. Lima pengujian diimplementasikan untuk mengetahui performa server Private Cloud ini. Instances dibuat sibuk seolah-olah menjalankan sebuah aplikasi sehingga terlihat kinerja dari server cloud. Parameter pengujian yang digunakan untuk pengukuran performa server adalah Load Average, CPU Usage dan Memory Usage. Dari hasil pengukuran menunjukkan bahwa parameter Load Average dengan load tertinggi sebesar 4,35 satuan proses dan CPU Usage tertinggi mencapai nilai 95,04% ketika seluruh instance aktif menjalankan aplikasi. Memory usage server mencapai 4740,95 MB dan untuk seluruh instance menggunakan memori sebesar 29,37% dari penggunaan memori pada server. Tiga parameter di atas menunjukkan kesesuaian server cloud dalam menangani pengguna pada jaringan privat IaaS ini dengan konsep Cloud IaaS pada umumnya.

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Private Cloud is a technology model of Cloud computing that only provide service to a particular user. Requirement for a good server performance, of course, affect the service offered to the user of a cloud provider. Therefore, making cloud computing by choosing a Private Cloud service in this thesis was to create a simulation of the real physical network using CentOS with Eucalyptus in it. Installation method using configuration Cloud in a Box. Five tests are implemented to determine the performance of the server's Private Cloud. Instances kept busy as running an application and visible the performance of the server cloud. Testing parameters used to measure the performance of the server is the Load Average, CPU Usage and Memory Usage.

From the measurement results indicate that the parameter Load Average with the highest load of 4.35 units and the highest CPU usage reaches 95.04% when all active instances running the application. Memory usage of server and to achieve 4740.95 MB memory instances amounting to 29.37%. Three parameters above indicates suitability of cloud servers to handle the user's private network to the concept of Cloud IaaS IaaS in general."