Media Pembelajaran Pengenalan Alat Transportasi dan Rambu Lalu Lintas Berbasis Android untuk Sekolah Dasar

Transportation equipment and traffic signs are one of the important lessons for elementary school students. However, many elementary school students only know the means of transportation without knowing the traffic signs. The purpose of this study is to build and create learning media to make it easier for students to recognize transportation tools and traffic signs using the Android platform. The method used in building this application is the waterfall method, which starts with making observations to testing the system. The tests used are black box testing and Mean Opinion Score (MOS). The results of the black box testing test are that the menu has been able to run according to their respective functions, while for MOS, it produces about 98% results stating that the application is running well. So that this application is used in accordance with the needs of elementary school students in helping to recognize means of transportation and traffic signs.

User Quality of Experience (QoE) Satisfaction for Video Content Selection (VCS) Framework in Smartphone Devices

Video streaming is widely available nowadays. Moreover, since the pandemic hit all across the globe, many people stayed home and used streaming services for news, education, and entertainment. However, when streaming in session, user Quality of Experience (QoE) is unsatisfied with the video content selection while streaming on smartphone devices. Users are often irritated by unpredictable video quality format displays on their smartphone devices. In this paper, we proposed a framework video selection scheme that targets to increase QoE user satisfaction. We used a video content selection algorithm to map the video selection that satisfies the user the most regarding streaming quality. Video Content Selection (VCS) are classified into video attributes groups. The level of VCS streaming will gradually decrease to consider the least video selection that users will not accept depending on video quality. To evaluate the satisfaction level, we used the Mean Opinion Score (MOS) to measure the adaptability of user acceptance towards video streaming quality. The final results show that the proposed algorithm shows that the user satisfies the video selection, by altering the video attributes.

Quality Evaluation of Online Mental Health Education Based on Reinforcement Learning in the Pandemic

The COVID-19 pandemic has become one of the biggest major health crises reported due its massive impact on many countries. From mental health experts, we know that we cannot lose sight of an equally alarming issue which is the long-term mental health impact the pandemic is going to leave on the society. The rapid spread of the pandemic gives little chance to prepare for or even process all that has happened in terms of job losses and the complete uprooting of everyday life and relationships. It is understandable that students may feel irritable, frustrated, or sad sometimes. Loneliness, confusion, and anxiety are also common, but the issue is how we can know if students’ emotions are a normal reaction to an abnormal situation. Therefore, online mental health education has become pretty important for students during the pandemic. Furthermore, it is important to evaluate the quality of online mental health education through microlessons. In this paper, based on Q-learning algorithm, the real-time adaptive bitrate (ABR) configuration parameters mechanism is proposed to detect the changes of network state constantly and select the optimal precalculated configuration according to the current network state. The simulation results show that the proposed algorithm based on Q-learning outperforms other baselines in average latency, average bitrate, and Mean Opinion Score (MOS) on Chrome DevTools and Clumsy. Meanwhile, the experimental results also reveal that the average number of identified mental health problems of the proposed mechanism has always been the best with the bandwidth from 10 Mbit/s to 500 Mbit/s.

Deep Reinforcement Learning for UAV Trajectory Design Considering Mobile Ground Users

A network composed of unmanned aerial vehicles (UAVs), serving as base stations (UAV-BS network), is emerging as a promising component in next-generation communication systems. In the UAV-BS network, the optimal positioning of a UAV-BS is an essential requirement to establish line-of-sight (LoS) links for ground users. A novel deep Q-network (DQN)-based learning model enabling the optimal deployment of a UAV-BS is proposed. Moreover, without re-learning of the model and the acquisition of the path information of ground users, the proposed model presents the optimal UAV-BS trajectory while ground users move. Specifically, the proposed model optimizes the trajectory of a UAV-BS by maximizing the mean opinion score (MOS) for ground users who move to various paths. Furthermore, the proposed model is highly practical because, instead of the locations of individual mobile users, an average channel power gain is used as an input parameter. The accuracy of the proposed model is validated by comparing the results of the model with those of a mathematical optimization solver.

Automatic Path Planning Offloading Mechanism in Edge-Enabled Environments

The utilization of edge-enabled cloud computing in unmanned aerial vehicles has facilitated advances in autonomous control by employing computationally intensive algorithms frequently related to traversal among different locations in an environment. A significant problem remains in designing an effective strategy to offload tasks from the edge to the cloud. This work focuses on creating such a strategy by employing a network evaluation method built on the mean opinion score metrics in concoction with machine learning algorithms for path length prediction to assess computational complexity and classification models to perform an offloading decision on the data provided by both network metrics and solution depth prediction. The proposed system is applied to the A* path planning algorithm, and the presented results demonstrate up to 94% accuracy in offloading decisions.

Sistem Informasi Marketplace Penyewaan Barang Berbasis Web Dengan Framework Laravel

Pengguna internet di Indonesia terus mengalami peningkatan. Berdasarkan data dari Asosiasi Penyelenggara Jasa Internet Indonesia (APJII) tahun 2016, jumlah pengguna internet di Indonesia tumbuh sebesar 10,12%. Perkembangan yang semakin pesat ini di manfaatkan oleh berbagai pihak guna memaksimalkan fungsi dari kemajuan teknologi ini, yang salah satunya adalah dalam bidang jasa yaitu penyewaan atau rental barang. Banyaknya bisnis penyewaan barang yang muncul saat ini membuat penyewa barang seringkali lupa terkait informasi terkait produk yang ditawarkan. Kurangnya informasi atau promosi dari pemilik barang membuat calon penyewa sedikit kesulitan dan memerlukan usaha yang lebih untuk mendapatkan barang yang diinginkan, sehingga perlu adanya penyewaan berbasis website yang memuat informasi terkait produk yang ditawarkan. Penelitian ini bertujuan untuk membuat sistem marketplace yang memuduahkan bagi calon penyewa dalam menemukan barang yang diinginkan dan bagi pemilik barang untuk memudahkan memasarkan barangnya dan mengelola barang serta pesanannya. Cara kerja sistem ini, penyewa mencari barang yang diinginkan melalui sistem lalu memesannya. Pesanan tersebut kemudian dikirimkan ke pemilik barang oleh sistem setelah pembayaran untuk biaya sewa dikonfirmasi oleh admin dan pemilik barang dapat memberikan barangnya kepada penyewa sesuai data pesanannya. Sistem ini berbasis web dengan bahasa pemrograman PHP menggunakan framework laravel yang didukung basis data MySQL dan mengacu pada metode pengembangan waterfall pada proses pembuatannya. Tahap pengujian untuk sistem marketplace penyewaan barang dilakukan dengan metode pengujian blackbox testing dan Mean Opinion Score. Hasilnya menunjukan bahwa sitem berjalan sesuai dengan yang diharapkan dan mampu mebantu bagi calon penyewa dalam mencari informasi barang sewaan maupun bagi para pemilik barang dalam mempromosikan barangnya.

Sistem Informasi Pelayanan Administrasi Kependudukan Berbasis Web Menggunakan Metode Waterfall Di Desa Sidakangen Purbalingga

Sistem Informasi Pelayanan Administrasi Kependudukan merupakan sistem berbasis web yang digunakan untuk mengelola proses pelayanan administrasi pembuatan surat-menyurat. Pembuatan surat-menyurat di Desa Sidakangen masih menggunakan pembukuan atau arsip secara manual. Dari pembukuan atau arsip tersebut kemudian di rekap kedalam Microsoft Excel untuk dicetak sebagai laporan, belum lagi petugas akan melayani masyarakat dalam pembuatan surat menyurat sehingga pada prosesnya membutuhkan waktu yang lebih untuk melakukan pelayanan administrasi kependudukan dengan baik. Untuk mengatasi beberapa permasalah tersebut maka perlu dirancang suatu Sistem Informasi Pelayanan Administrasi Kependudukan Berbasis Web sehingga dapat meningkatkan kinerja Perangkat Desa dalam mengolah data - data dan informasi yang dimiliki agar lebih akurat dan efisien, serta dapat menangani permasalahan dalam pelayanan administrasi kependudukan di Desa Sidakangen. Sistem Informasi Pelayanan Administrasi ini menggunakan metode pengembangan Waterfall dengan metode pengujian Blackbox dan MOS (Mean Opinion Score) yang membuktikan bahwa adanya sistem ini masyarakat akan lebih mudah dalam melakukan proses pelayanan administrasi tingkat desa.