Hasil Pencarian  ::  Simpan CSV :: Kembali

"ABSTRAKNotifikasi merupakan salah satu fitur komunikasi via pesan yang penting keberadaannya di aplikasi pada smartphone. Penerapan sistem notifikasi push notification mampu meningkatkan interaksi antar pengguna dan penggunaan layanan yang disediakan perangkat lunak Berbagi Kendaraan. Aplikasi Berbagi Kendaraan merupakan aplikasi berbasis Android yang dikerjakan pada studi ini. Tujuan aplikasi adalah untuk menciptakan platform berbagi kendaraan yang aman sebagai bagian dari aplikasi sistem transportasi cerdas. Diperlukan adanya sistem push notification yang efektif untuk meningkatkan interaksi penggunaan layanan yang disediakan. Tulisan ini membahas implementasi dan evaluasi kinerja dari layanan push notification pada aplikasi Berbagi Kendaraan menggunakan Google Cloud Message(GCM), IMF Push Bluemix, dan Telegram API. Stress testing dengan variasi concurrent request dilakukan untuk menguji implementasi. Hasil pengujian menunjukkan layanan GCM lebih baik dengan persentase minimum pesan diterima 95,09%. Diikuti dengan layanan IMF Push dengan persentase minimum pesan diterima 85,44% dan Telegram API sebesar 52,50%. Evaluasi kinerja dilakukan dengan pengambilan dan pengolahan data pada latency, jitter, packet loss dan throughput melalui tiga kondisi uji. Hasil pengujian menunjukkan bahwa layanan Google Cloud Messaging merupakan layanan push notification berbasis cloud yang memiliki kinerja paling baik pada implementasi untuk aplikasi Berbagi Tumpangan dengan nilai rata-rata latency 209,65 milidetik, jitter 107,62 milidetik, packet loss 0% dan throughput 8478,89 bit/detik.

---

ABSTRACTNotification is one of the most important communication features that should exist on smartphone‟s applications. The implementation of the push notification system may increase the interaction between users and the usage of Vehicle Sharing‟s services. Vehicle Sharing application is an Android-based application that is being studied in this experiment. The goal is to create a safe platform for vehicle sharing as a part of an intelligent transportation system. Therefore, it is necessary to implement an efficient push notification system to enhance the user experience. This paper describes the implementation and performance evaluation of several push notification services, i.e. Google Cloud Messaging (GCM), IMF Push Bluemix, and Telegram API. Stress tests with variation of concurrent request were conducted to evaluate the implementation. The result shows that the GCM service outperform others with minimum messages received of 95,09%. It is followed by the IMF Push services with 85,44% messages received and subsequently the Telegram API with 52,50% messages received. The performance evaluation was conducted by collecting and analyzing the latency, jitter, packet loss and throughput of each push services through three scenarios. The result shows that the GCM service performs better as a more reliable cloud-based push notification service on the Vehicle Sharing application with the mean latency of 209,65 ms, jitter of 107,62 ms, packet loss of 0%, and throughput of 8478,89 bit/s."

"ABSTRAKNotifikasi merupakan salah satu fitur komunikasi via pesan yang penting keberadaannya di aplikasi pada smartphone. Penerapan sistem notifikasi push notification mampu meningkatkan interaksi antar pengguna dan penggunaan layanan yang disediakan perangkat lunak Berbagi Kendaraan. Aplikasi Berbagi Kendaraan merupakan aplikasi berbasis Android yang dikerjakan pada studi ini. Tujuan aplikasi adalah untuk menciptakan platform berbagi kendaraan yang aman sebagai bagian dari aplikasi sistem transportasi cerdas. Diperlukan adanya sistem push notification yang efektif untuk meningkatkan interaksi penggunaan layanan yang disediakan. Tulisan ini membahas implementasi dan evaluasi kinerja dari layanan push notification pada aplikasi Berbagi Kendaraan menggunakan Google Cloud Message(GCM), IMF Push Bluemix, dan Telegram API. Stress testing dengan variasi concurrent request dilakukan untuk menguji implementasi. Hasil pengujian menunjukkan layanan GCM lebih baik dengan persentase minimum pesan diterima 95,09%. Diikuti dengan layanan IMF Push dengan persentase minimum pesan diterima 85,44% dan Telegram API sebesar 52,50%. Evaluasi kinerja dilakukan dengan pengambilan dan pengolahan data pada latency, jitter, packet loss dan throughput melalui tiga kondisi uji. Hasil pengujian menunjukkan bahwa layanan Google Cloud Messaging merupakan layanan push notification berbasis cloud yang memiliki kinerja paling baik pada implementasi untuk aplikasi Berbagi Tumpangan dengan nilai rata-rata latency 209,65 milidetik, jitter 107,62 milidetik, packet loss 0% dan throughput 8478,89 bit/detik.

---

ABSTRACTNotification is one of the most important communication features that should exist on smartphone's applications. The implementation of the push notification system may increase the interaction between users and the usage of Vehicle Sharing's services. Vehicle Sharing application is an Android-based application that is being studied in this experiment. The goal is to create a safe platform for vehicle sharing as a part of an intelligent transportation system. Therefore, it is necessary to implement an efficient push notification system to enhance the user experience. This paper describes the implementation and performance evaluation of several push notification services, i.e. Google Cloud Messaging (GCM), IMF Push Bluemix, and Telegram API. Stress tests with variation of concurrent request were conducted to evaluate the implementation. The result shows that the GCM service outperform others with minimum messages received of 95,09%. It is followed by the IMF Push services with 85,44% messages received and subsequently the Telegram API with 52,50% messages received. The performance evaluation was conducted by collecting and analyzing the latency, jitter, packet loss and throughput of each push services through three scenarios. The result shows that the GCM service performs better as a more reliable cloud-based push notification service on the Vehicle Sharing application with the mean latency of 209,65 ms, jitter of 107,62 ms, packet loss of 0%, and throughput of 8478,89 bit/s.;;"

"Web Services, Service-Oriented Architectures, and Cloud Computing is a jargon-free, highly illustrated explanation of how to leverage the rapidly multiplying services available on the Internet. The future of business will depend on software agents, mobile devices, public and private clouds, big data, and other highly connected technology. IT professionals will need to evaluate and combine online services into service-oriented architectures (SOA), often depending on Web services and cloud computing. This can mean a fundamental shift away from custom software and towards a more nimble use of semantic vocabularies, middle-tier systems, adapters and other standardizing aspects.This book is a guide for the savvy manager who wants to capitalize on this technological revolution. It begins with a high-level example of how an average person might interact with a service-oriented architecture, and progresses to more detail, discussing technical forces driving adoption and how to manage technology, culture and personnel issues that can arise during adoption. An extensive reference section provides quick access to commonly used terms and concepts."

"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."

"Cloud computing adalah teknologi baru hasil pengembangan dari sistem berbasis Internet dengan sumber daya komputasi dalam skala besar yang disediakan melalui Internet untuk pengguna. Aplikasi multimedia pada cloud membutuhkan Quality of Service (QoS) seperti bandwidth, delay, dan lain-lain. Penyediaan QoS merupakan tantangan pada multimedia cloud computing. Penelitian ini mengusulkan sistem QoS MEC, sebuah sistem QoS cloud computing dengan kombinasi teknologi Hadoop dan metode Load Balancing berbasis Eucalyptus untuk aplikasi multimedia. Hadoop merupakan platform yang bersifat open source digunakan untuk data berukuran besar yang diproses secara terdistribusi dan paralel. Load balancing merupakan metode untuk membagi beban kerja server. Hasil pengujian menunjukkan bahwa terjadi percepatan response time dan peningkatan throughput, terbesar terjadi pada saat jumlah koneksi per detik 5000. Persentase percepatan response time video ukuran 58,4 MB, adalah 20,42 %; video ukuran 137 MB, adalah 21,36%; video ukuran 249 MB, adalah 21,51%. Persentase peningkatan throughput video ukuran 58,4 MB, adalah 12,52%; video ukuran 137 MB, adalah 13,39%; video ukuran 249 MB, adalah 14,09%.

---

Cloud computing is a new technology development results of Internet-based systems with computing resources on a large scale are provided via the Internet to the user. Multimedia application in the cloud requires Quality of Service (QoS) such as bandwidth, delay, and others. Provision of QoS is a challenge on a multimedia cloud computing.

This research proposed a QoS MEC system, a QoS cloud computing system with technology combination of Hadoop and Load Balancing method based Eucalyptus for multimedia applications. Hadoop is an open source platform which is used for large data that is distributed and processed in parallel. Load balancing is a method to divide the workload of the server.

The test results show that there is accelerated response time and increased throughput and largest occurred when the number of simultaneous access is 5000. Percentage of acceleration response time for video size of 58.4 MB is 20.42%; video size of 137 MB is 21,36%; and video size of 249 MB is 21,51%. The increased percentage of throughput for video size of 58.4 MB is 12,52%; video size of 137 MB is 13,39%; and video size of 249 MB is 14,09%."

"Penelitian ini bertujuan untuk mengetahui bagaimana perilaku Mahasiswa Magister Ilmu Perpustakaan UI Angkatan 2020 – 2021 sebagai Pengguna Aplikasi Google Foto. Penelitian ini menggunakan metode kualitatif dengan pendekatan fenomenologi yang disajikan secara deskriptif. Penelitian ini menggunakan teknik sampling purposif terhadap 10 Informan Mahasiswa Ilmu Perpustakaan dan Informasi UI Angkatan 2020 – 2021. Pemilihan informan berdasarkan kriteria pengguna Aplikasi Google Foto dengan minimal 1 tahun dan pernah mempelajari materi selama kuliah tentang “Arsip Digital”. Pengumpulan data dilakukan dengan wawancara daring melalui Google Meet. Hasil penelitian menunjukan bahwa 5 dari 10 informan mengandalkan penuh aplikasi Google Foto dalam melakukan preservasi File foto dan mengetahui manfaat serta kegunaan aplikasi Google dengan baik. Sisanya adalah sebagai pengguna baru yang pasif dan masih mengandalkan penyimpanan pada media lain seperti harddisk. Perilaku mahasiswa Magister Ilmu Perpustakaan UI Angkatan 2020 – 2021 sebagai pengguna Aplikasi Google Foto dalam melakukan preservasi file digital terdiri dari: 1) Identify lokasi penyimpanan foto. Hampir semua informan diketahui saat ini menyimpan koleksi foto digitalnya di cloud storage, dalam hal ini Aplikasi Google Foto, namun ada juga yang tetap menyimpannya di media fisik, seperti hard disk. 2) Decide yang berarti memilih gambar yang paling signifikan untuk dipertahankan. Hampir semua informan telah melakukan pekerjaan dengan baik dalam mengimplementasikannya pada saat ini. 3) Organize; informan melakukan ini dengan mengelompokkan foto-foto. Sebagian besar informan telah mengorganisasikan foto-fotonya ke dalam folder sesuai dengan tahun, tempat, individu, dan kegiatan yang sedang berlangsung saat diambil. Akan sulit untuk mengambil foto tersebut karena hanya sedikit dari mereka yang memberikan nama atau deskripsi untuk setiap file foto. 4) Make copies. Pada titik ini, sebagian besar informan telah menyalin gambar dan menyimpannya di berbagai perangkat online dan offline.

---

The purpose of this study is to discover the behavior of 2020-2021 UI Masters in Library Science students as Google Photos users. This research employs a qualitative methodology with a phenomenological approach that is presented descriptively. Purposive sampling was performed on 10 students of UI Library and Information Science Class of 2020 - 2021. The informants were chosen based on the criterion of having used the Google Photos program for at least a year and having studied material during courses on "Digital Archives." Data was gathered through online interviews conducted using Google Meet. According to the findings, 5 out of 10 informants depended significantly on the Google Photos program to save photo files and were well-versed in the benefits and uses of the Google application. The rest are inactive new users who rely on storage on other media such as hard disks. The behavior of UI Masters in Library Science students Class of 2020 - 2021 as Google Photos users in preserving digital assets, includes: 1) Identify photo storage facilities. Almost all informants save their digital photo collections in cloud storage, in this example the Google Photos program, although some are still storing them on physical media, such as hard disks and laptops. 2) Decide, which involves selecting the most important photograph to keep. Almost all of the informants have done an excellent job of implementing it. 3) Organize the photographs; informants do this by grouping them. The majority of the informants had their images categorized based on the year, location, individual, and events when they were taken. Few of them gave a name or description for each photo file. 4) Make copies. At this stage, the majority of the informants had duplicated the photographs and saved them on a variety of online and offline devices."

"ABSTRAKTeknologi di bidang perangkat lunak dan perangkat keras semakin berkembang cepat. Masalah keterbatasan kapasitas suatu komputer memicu berkembangnya sebuah inovasi yang disebut dengan High Performance Computing HPC . HPC merupakan sekumpulan komputer yang digabungkan dalam sebuah jaringan dan dikoordinasi oleh software khusus. Cloud Computing merupakan paradigma yang relatif baru dalam bidang komputasi. Pada penelitian ini dilakukan pengujian terhadap performansi High Performance Computing Cluster HPCC berbasis cloud menggunakan layanan OpenStack dalam menjalankan fungsi dasar Message Passing Interface. Pengujian dilakukan menggunakan program Mpptest dan SIMPLE-O. Penggunaan server yang tidak mendukung hypervisor KVM pada pengujian point-to-point communication dapat menurunkan performansi HPCC berbasis cloud sebesar 3,1 - 12,4 dibandingkan dengan HPCC berbasis non-cloud. Pada pengujian point-to-point communication dengan 2 server yang mendukung hypervisor KVM, HPCC berbasis cloud unggul dibandingkan HPCC berbasis non-cloud sebesar 1,6 ndash; 2,7 . Pada pengujian performansi HPCC dalam melakukan fungsi MPI collective communication tidak ditemukan perbedaan berarti antara kedua cluster dimana HPCC berbasis non-cloud mengungguli HPCC berbasis cloud sebesar 0 - 1,4 . Pada pengujian menggunakan program SIMPLE-O didapati performansi HPCC berbasis cloud dan non-cloud imbang jika semua instance dijalankan dengan server yang mendukung hypervisor KVM, apabila terdapat instance yang dijalankan server tanpa dukungan KVM maka HPCC berbasis non-cloud unggul 96,2 dibandingkan HPCC berbasis cloud. Ketersedian modul KVM pada server yang menjadi host suatu instance sangat berpengaruh terhadap performansi HPCC berbasis cloud.

---

ABSTRACTSoftware and hardware technologies have been developing rapidly. Capacity limation problems found in computers triggered a development of a new innovation called High Performance Computing HPC . HPC is a cluster of computers in a network coordinated by a special software. Cloud Computing is a new paradigm in computation field. In this research, series of test are done to find out the performance of cloud and non cloud based High Performance Computing Cluster HPCC while running basic functions of Message Passing Interface. Tests are done using Mpptest and SIMPLE O program. By using a server that does not support KVM in point to point communication test could decrease the performance of cloud based HPCC by 3,1 to 12,4 compared to non cloud based HPCC. During the test of point to point communication using 2 servers that support KVM hypervisor, cloud based HPCC is ahead of non cloud based HPCC by 1,6 to 2,7 . During the test of collective communication, there are no significant differences between performances of the two cluster, with non cloud based HPCC is ahead by 0 to 1,4 compared to cloud based HPCC. During the test using SIMPLE O program, the two cluster is even in term of performance as long as every instance is run by servers that support KVM hypervisor, if there is an instance that is run by a server that does not support KVM hypervisor then the performance of non cloud based HPCC is still ahead by 96,2 compared to cloud based HPCC. During the performance testing of HPCC while running collective communication, noticable performance difference between cloud and non cloud based HPCC was not found. The availability of KVM module in a server that is used to host an instance is really essential to the cloud based HPCC performance."

"Perkembangan pesat teknologi menyebabkan pertumbuhan pengguna perangkat mobilesemakin meningkat. Hal tersebut mendorong para pengembang aplikasi untukmengembangkan berbagai aplikasi. Aplikasi Learn Quran Tajwid merupakan aplikasiyang diperuntukkan bagi pengguna untuk belajar dan memahami bacaan al-quran lebihdetail dengan audio yang tepat dalam melafadzkan al-quran dan pengguna dapatmempraktekkan bacaan dengan koreksi dari aplikasi. Pendapatan Learn Quran Tajwidbersumber pada layanan berlangganan dan iklan. Sumber utamanya pada pendapatanlayanan paket berlangganan khususnya di Google Play Store namun sumber pendapatanutama tersebut terus mengalami penurunan pertumbuhan bulanan dari tahun sebelumnya.Target peningkatan pertumbuhan pendapatan bulanan Aplikasi Learn Quran Tajwid diGoogle Play Store dari tahun sebelumnya (y-o-y) tidak tercapai. Oleh sebab itu, dilakukananalisis akar masalah dan didapatkan masalah utamanya adalah kepuasaan pelangganmenurun. Tujuan penelitian ini adalah melihat bagaimana pandangan pengguna AplikasiLearn Quran Tajwid di Google Play Store dengan melakukan analisis sentimen danpemodelan topik. Data ulasan yang digunakan berjumlah 5100 ulasan yang didapatkandengan melakukan scraping dari ulasan pengguna aplikasi Learn Quran Tajwid di GooglePlay Store dengan rincian 3026 ulasan sebagai data latih. Selanjutnya data latihdianotasikan manual untuk menentukan sentimen positif atau negatif kemudian dilakukanpreprocessing dan representasi teks menggunakan TF-IDF. Penelitian ini menggunakanalgoritma NB, SVM, XGBoost, CNN, LSTM dan BERT untuk klasifikasi sentimen. Hasileksperimen menunjukkan bahwa algoritma klasifikasi dengan kinerja terbaik adalahalgoritma BERT dengan akurasi 96%, diikuti SVM imbalanced class dengan akurasi95,2% serta SVM-smote dan LSTM dengan akurasi 94,8%. Sementara itu, algoritmapemodelan topik yang digunakan adalah LDA. Hasil pemodelan topik menggunakanalgoritma LDA untuk sentimen positif dan negatif. kesimpulan topik pada sentimenpositif yakni pengguna merasa aplikasi sangat bagus dan memberikan manfaat yangbesar, serta mudah digunakan Sedangkan dari topik yang muncul pada sentimen negatifdidapatkan kesimpulan yakni pengguna merasa iklan yang muncul sangat mengganggudan mengurangi pengalaman pengguna walaupun pengguna merasa aplikasi bagus danbermanfaat namun karena terdapat iklan yang sangat mengganggu berpengaruh terhadapkepuasaan pengguna sehingga memberikan rating rendah.

---

The rapid development of technology has led to an increasing growth in mobile device

users. This has driven application developers to create various apps. The Learn Quran

Tajwid app is designed for users to learn and understand the recitation of the Quran in

more detail, with accurate audio pronunciation. Users can practice their recitation and

receive corrections from the app. The revenue for Learn Quran Tajwid comes from

subscription services and advertisements. The main source of revenue is the subscription

packages, particularly on the Google Play Store. However, the main revenue source has

been experiencing a decline in monthly growth compared to the previous year. The target

of increasing monthly revenue growth for the Learn Quran Tajwid app on the Google

Play Store from the previous year (year-over-year) was not achieved. Therefore, an

analysis of the root cause was conducted, and it was found that customer satisfaction has

decreased. This research aims to examine the users' perspectives of the Learn Quran

Tajwid app on the Google Play Store through sentiment analysis and topic modelling. A

total of 5100 app reviews were used for the analysis, obtained by scraping user reviews

of the Learn Quran Tajwid app from the Google Play Store. Out of these, 3026 reviews

were used as training data. The training data was manually annotated to determine

positive or negative sentiment, and then pre-processing and text representation using TF

IDF were performed. This study used the NB, SVM, XGBoost, CNN, LSTM, and BERT

algorithms for sentiment classification. The experimental results showed that the BERT

algorithm performed the best with an accuracy of 96%, followed by SVM imbalance class

with 95.2% accuracy, and SVM-SMOTE and LSTM with 94.8% accuracy. As for the

topic modelling algorithm used, it was LDA. The topic modelling results using the LDA

algorithm for positive sentiment and negative sentiment. In conclusion, the topics

identified for positive sentiment indicate that users find the app to be excellent and highly

beneficial, as well as easy to use. On the other hand, from the topics identified for negative

sentiment, it can be concluded that users find the ads to be very disruptive and diminish

the user experience. Despite users perceiving the app as good and useful, the presence of

intrusive ads has a significant impact on user satisfaction, resulting in lower ratings. "