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Abstrak :
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”. ......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".

Abstrak :
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. ......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.

Abstrak :
In this book, contributors provide insights into the latest developments of Edge Computing/Mobile Edge Computing, specifically in terms of communication protocols and related applications and architectures. The book provides help to Edge service providers, Edge service consumers, and Edge service developers interested in getting the latest knowledge in the area. The book includes relevant Edge Computing topics such as applications; architecture; services; inter-operability; data analytics; deployment and service; resource management; simulation and modeling; and security and privacy. Targeted readers include those from varying disciplines who are interested in designing and deploying Edge Computing.;

Abstrak :
Peningkatan trafik data terus naik secara signifikan, khususnya di Indonesia. Berdasarkan survei yang dilakukan oleh Hootsuite-We are Social, dikatakan bahwa pada tahun 2018 penetrasi pengguna Internet di Indonesia hingga 50% dari jumlah populasi Indonesia, atau sebesar 132,7 Milyar jiwa. 91% penggunaan Internet diakses dari smartphone atau tablet. Trafik data pada operator seluler PT XYZ kian meningkat setiap tahunnya. Pada tahun 2017, tercatat hingga 2juta TeraByte trafik yang ditangani. Hal ini menjadi permasalahan dalam hal trafik dan kapasitas pada PT XYZ selaku operator seluler. Maka dibutuhkan solusi dari permasalahan tersebut, agar operator XYZ dapat terus kompeten dalam melayani pengguna dalam hal akses data dan akses Internet. Berdasarkan literatur dan informasi sebelumnya, penambahan jumlah kapasitas pemrosesan dan pengolahan paket data adalah jawaban dari permasalahan. Penambahan kapasitas dapat dilakukan dengan tiga pilihan solusi, yaitu adalah: penambahan modul GGSN, implementasi arsitektur Multi-access Edge Computing (MEC) atau penambahan GGSN pada jaringan yang sudah ada. Dalam penelitian ini, akan dilakukan analisis kelayakan MEC pada operator PT XYZ. Akan dilakukan analisis dari tiga pilihan solusi. Analisis yang dilakukan pada penelitian ini dilihat dari aspek kelayakan teknologi dan aspek kelayakan investasi lalu melakukan analisis biaya-manfaat. Hasil dari penelitian ini menunjukkan bahwa solusi utama layak diimplementasikan. Berdasarkan hasil NPV dan rasio B/C solusi implementasi MEC menjadi pilihan terbaik, memiliki kemampuan yang cukup dalam menangani trafik hingga beberapa tahun ke depan serta kemampuan fleksibilitasnya. Terdapat dua skenario dalam implementas MEC, pertama dilakukan setelah implementasi solusi alternatif pertama sebagai solusi sementara pada Oktober 2021 dan kedua langsung dilakukan saat awal tahun 2020.

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Increased data traffic continues significantly, especially in Indonesia. Based on a survey by Hootsuite-We are Social, it is said that in 2018 the penetration of Internet users in Indonesia is up to 50% of the total population of Indonesia or amounting to 132.7 billion people. 91% of Internet use is accessed from a smartphone or tablet. Data traffic on PT XYZ cellular operators is increasing every year. In 2017, up to 2 million TeraByte of traffic is handled. This has become a problem in terms of traffic and capacity at PT XYZ as a cellular operator. So, a solution to these problems is needed, so that XYZ operators can continue to be competent in serving users in terms of data access and Internet access. Based on the literature and previous information, the addition of the amount of processing capacity and processing of data packages is the answer to the problem. Addition of capacity can be done with three solutions choices, namely: the addition of the GGSN module, the implementation of the Multi-access Edge Computing (MEC) architecture or the addition of the GGSN to existing networks. In this study, an analysis of the feasibility of MEC for PT XYZ operators will be conducted. Analysis of three solutions choices will be carried out. The analysis conducted in this study is seen from the aspects of technological feasibility and investment feasibility aspects and then conducts a cost-benefit analysis. The results of this study indicate that the main solution is feasible to implement. Based on the NPV results and the B/C ratio, the MEC implementation solution is the best choice, has sufficient ability to handle traffic for the next several years and its flexibility. There are two scenarios in the implementation of the MEC, the first is done after the implementation of the first alternative solution as a temporary solution in October 2021 and the second is immediately at the beginning of 2020.

Abstrak :
Tol Laut adalah infrastruktur maritim yang menghubungkan wilayah Barat dan Timur Indonesia berupa ketersediaan kapal laut angkutan barang yang rutin dan terjadwal. Tujuan Tol Laut adalah menjamin kesediaan barang, mengurangi disparitas harga barang, dan menjamin kelangsungan angkutan barang dari dan ke daerah tertinggal, terpencil, terluar, dan perbatasan. Walau telah banyak pencapaian sejak diimplementasikan tahun 2015, analisis atas data kinerja 2016 - 2020 menunjukkan bahwa faktor beban muatan balik Tol Laut hanya 16%. Evaluasi para pemangku kepentingan Tol Laut tahun 2020, mengidentifikasikan bahwa volume kargo balik dan utilitas kapal rendah. Di lain pihak, Peraturan Presiden Republik Indonesia no. 26 tahun 2012 menetapkan perlu dibangun sistem e-logistik untuk menangani logistik domestik dan internasional. Walau sistem e-logistik internasional sudah tersedia pada saat penelitian ini, sistem e-logistik domestik belum direalisasikan. Physical Internet (PI) adalah inovasi penting yang berpotensi merevolusi industri logistik dengan cara mengatasi Gejala Tidak Berkelanjutan Logistik Global, seperti kemasan kosong atau angkutan bermuatan kosong. Penelitian ini mengkaji rekomendasi sejumlah penelitian yang dilakukan Uni Eropa (EU) sehubungan implementasi PI di negara anggota EU. Hasil kajian lalu diadaptasikan pada rancangan sistem e-logistik berbasis PI (SELPI) yang memiliki kemampuan meningkatkan faktor beban muatan balik Tol Laut. Selanjutnya rancangan SELPI ini akan ditinjau menggunakan Tabel Manfaat Bisnis SI/TI Generik dan Kerangka Kesejahteraan Digital. Tujuan penelitian ini adalah mengusulkan rancangan SELPI dan melakukan identifikasi manfaat ekonomi rancangan SELPI ......Tol Laut is maritime infrastructure connecting Indonesia’s western and eastern regions in the form of availability of regular and scheduled sea freight. Tol Laut aims to guaranteeing availability of household goods, narrowing disparity of commodity prices, and ensuring continuity of sea freight to and from Indonesia’s underdeveloped, desolate, outermost, and border regions. Despite many achievements since inception in 2015, analysis of 2016 - 2020 reports shows that Tol Laut average return cargo load factor is only 16%. Evaluation conducted by Tol Laut stakeholders in 2020 identified that return cargo and ship utilization is low. On the other hand, Regulation of the President of Republic of Indonesia no. 26 year 2012 stipulated the need to build e-logistics systems to manage domestic- and international-bound logistics. Whilst international-bound logistics e-logistics systems have been developed at the time of this research, such system for domestic-bound logistics is yet to be developed. Physical Internet (PI) is an important innovation that has the potential of revolutionizing logistics through meeting the Global Logistics Sustainability Grand Challenge, such as empty packaging or empty travel. This paper examines recommendations published by studies carried out by the European Union (EU) on PI implementation in EU member countries. The result of the study is then adapted into PI-based e-logistics system (SELPI) design featuring capabilities to improve Tol Laut return cargo load factor. Further, the benefits of SELPI design is then reviewed applying the Generic IS/IT Business Value Table and Digital Prosperity framework. The objectives of this research are to propose design of SELPI and to identify the economic benefits of SELPI design.

Abstrak :
This book presents solutions to the problems arising in two trends in mobile computing and their intersection: increased mobile traffic driven mainly by sophisticated smart phone applications; and the issue of user demand for lighter phones, which cause more battery power constrained handhelds to offload computations to resource intensive clouds (the second trend exacerbating the bandwidth crunch often experienced over wireless networks). The authors posit a new solution called spectrum aware cognitive mobile computing, which uses dynamic spectrum access and management concepts from wireless networking to offer overall optimized computation offloading and scheduling solutions that achieve optimal trade-offs between the mobile device and wireless resources. They show how in order to allow these competing goals to meet in the middle, and to meet the promise of 5G mobile computing, it is essential to consider mobile offloading holistically, from end to end and use the power of multi-radio access technologies that have been recently developed. Technologies covered in this book have applications to mobile computing, edge computing, fog computing, vehicular communications, mobile healthcare, mobile application developments such as augmented reality, and virtual reality. - Gives readers valuable insights into the future of mobile computing and communication; - Touches on wireless technologies such as 5G, mobile edge computing (MEC), mobile cloud services, and cognition-based networking; - Provides examples throughout the book to provide insight into real world scenarios.

Abstrak :
This timely book provides broad coverage of vehicular ad-hoc network (VANET) issues, such as security, and network selection. Machine learning based methods are applied to solve these issues. This book also includes four rigorously refereed chapters from prominent international researchers working in this subject area. The material serves as a useful reference for researchers, graduate students, and practitioners seeking solutions to VANET communication and security related issues. This book will also help readers understand how to use machine learning to address the security and communication challenges in VANETs. Vehicular ad-hoc networks (VANETs) support vehicle-to-vehicle communications and vehicle-to-infrastructure communications to improve the transmission security, help build unmanned-driving, and support booming applications of onboard units (OBUs). The high mobility of OBUs and the large-scale dynamic network with fixed roadside units (RSUs) make the VANET vulnerable to jamming. The anti-jamming communication of VANETs can be significantly improved by using unmanned aerial vehicles (UAVs) to relay the OBU message. UAVs help relay the OBU message to improve the signal-to-interference-plus-noise-ratio of the OBU signals, and thus reduce the bit-error-rate of the OBU message, especially if the serving RSUs are blocked by jammers and/or interference, which is also demonstrated in this book. This book serves as a useful reference for researchers, graduate students, and practitioners seeking solutions to VANET communication and security related issues.

Abstrak :
This research aims to evaluate the investment feasibility of an edge computing project in Jakarta's Central Business District (CBD) using the Value at Risk (VaR) approach. By leveraging financial projections, technical specifications, and risk analysis, this study provides a comprehensive assessment of the project's potential. The analysis involves calculating the Net Present Value (NPV), Internal Rate of Return (IRR), and Payback Period based on projected cash flows and costs. The edge computing infrastructure, consisting of 20 edge nodes distributed across multiple sites, is evaluated for its capacity to handle significant data traffic and provide efficient computing resources. The financial model includes detailed capital expenditure (CAPEX) and operational expenditure (OPEX) projections, ensuring a robust assessment of the project's viability. The results of the analysis show a positive NPV, indicating that the project is financially feasible. Additionally, sensitivity analysis and Monte Carlo simulation are used to assess the impact of various risk factors on the project's financial performance. This study contributes to the practical understanding of edge computing investments and provides valuable insights for stakeholders considering similar projects in urban settings. ......Penelitian ini bertujuan untuk mengevaluasi kelayakan investasi proyek edge computing di Kawasan Pusat Bisnis (CBD) Jakarta menggunakan pendekatan Value at Risk (VaR). Dengan memanfaatkan proyeksi keuangan, spesifikasi teknis, dan analisis risiko, studi ini memberikan penilaian komprehensif terhadap potensi proyek tersebut. Analisis melibatkan perhitungan Nilai Kini Bersih (NPV), Tingkat Pengembalian Internal (IRR), dan Periode Pengembalian berdasarkan arus kas dan biaya yang diproyeksikan. Infrastruktur edge computing, yang terdiri dari 20 node edge yang didistribusikan di beberapa lokasi, dievaluasi untuk kapasitasnya dalam menangani lalu lintas data yang signifikan dan menyediakan sumber daya komputasi yang efisien. Model keuangan mencakup proyeksi pengeluaran modal (CAPEX) dan pengeluaran operasional (OPEX) secara rinci, memastikan penilaian yang kuat terhadap kelayakan proyek. Hasil analisis menunjukkan NPV positif, yang mengindikasikan bahwa proyek ini layak secara finansial. Selain itu, analisis sensitivitas dan simulasi Monte Carlo digunakan untuk menilai dampak berbagai faktor risiko terhadap kinerja keuangan proyek. Studi ini berkontribusi pada pemahaman praktis tentang investasi edge computing dan memberikan wawasan berharga bagi para pemangku kepentingan yang mempertimbangkan proyek serupa di lingkungan perkotaan.

Abstrak :
This Springerbrief presents a deep reinforcement learning approach to wireless systems to improve system performance. Particularly, deep reinforcement learning approach is used in cache-enabled opportunistic interference alignment wireless networks and mobile social networks. Simulation results with different network parameters are presented to show the effectiveness of the proposed scheme. There is a phenomenal burst of research activities in artificial intelligence, deep reinforcement learning and wireless systems. Deep reinforcement learning has been successfully used to solve many practical problems. For example, Google DeepMind adopts this method on several artificial intelligent projects with big data (e.g., AlphaGo), and gets quite good results.