This paper discusses and provides comparison results of ESP-NOW, Wi-Fi, and Bluetooth as wireless local area network protocols. These protocols have been widely implemented on IoT devices. Inconsequential use of these protocols in areas that are not appropriate according to the characteristics of the protocols will cause problems such as fast battery drain, transmission speed is too wide with small data sent, and so on. For this reason, it is important to conduct a comparative study, to obtain information on the performance comparison of each protocol. We hope that IoT system designers can make it as a reference in the selection of these protocols. This paper will compare each protocol from the point of view of point-to-point transmission using key performance indicators such as maximum range, transmission speed, latency, power consumption, and resistance to obstructions using built-in and external antennas. Experiments were done with an equivalent testing method using the same components or tools. The data obtained were used to compare each protocol using a descriptive quantitative method by presenting the data in numerical, graphical, or descriptive form. The results of this paper are expected to be used as a consideration for which protocol is suitable for its implementation by providing an overview of the advantages and disadvantages of each protocol. A brief analysis of suitable applications for each protocol is provided.

The sixth generation of wireless protocol IEEE 802.11ax has been launched and offers better performance than the previous fifth-generation wireless protocol IEEE 802.11ac. This paper reviews the performance of both wireless protocols in the same operating frequency of 5 GHz. We used Network Simulator NS-3 as a simulation tool that offers flexibility, lesser time to set up and ease the experiment to any scenarios we need to perform. Furthermore, this paper focuses on analyzing and comparing the throughput of protocol IEEE 802.11ax Mcs-11 and 802.11ac Mcs-9 with a certain payload size and a various number of clients. The other parameters are set at certain values, such as a spatial stream, channel width, modulation and coding scheme, guard interval time and simulation time. The simulation result shows that the IEEE protocol 802.11ax Mcs-11 has better throughput performance than IEEE 802.11ac Mcs-9 with a large number of clients. In the simulation, a node of access point was accessed of 512 clients, IEEE 802.11ax Mcs-11 have more long delay response time than IEEE 802.11ac Mcs-9 at the beginning for a few milliseconds, but after 0.5 ms IEEE 802.11ax shown a stable and bigger throughput value than IEEE 802.11ac that its shown decrease.

Support Vector Machine (SVM) algorithm is a machine learning algorithm that is used to classify data by finding the best hyperplane that separates classes. In the SVM algorithm there are several types of kernel methods. Linear, Radial Basis Function (RBF), and polynomial kernel are some of the most commonly used SVM kernels. In previous research, each kernel has been used. However, the comparison of the three kernel function methods on the same dataset using accuracy, sensitivity, and specificity parameters has not been obtained. For this reason, this research is proposed to obtain comparative information of the three kernel functions using accuracy, sensitivity, and specificity parameters. The expected results can later be used as a reference for implementing the best kernel functions. The dataset used is comments on Youtube to analyze public sentiment on the increase in cases at the beginning of the entry of the COVID-19 pandemic in Indonesia. In this study, the accuracy values of the classification model were 0.86 for linear kernel, 0.90 for RBF kernel, and 0.91 for polynomial kernel. The sensitivity values obtained for each model are 0.64 for linear kernel, 0.48 for RBF kernel, and 0.20 for polynomial kernel. While the specificity values obtained for each model are 0.89 for linear kernel, 0.95 for RBF kernel, and 0.99 for polynomial kernel.

Teknologi informasi saat ini digunakan pada berbagai bidang seperti pembelajaran. Pembelajaran yang dilakukan biasanya disebut dengan e-learning atau pembelajaran jarak jauh. Pembelajaran ini dilakukan dengan menggunakan media yang memungkinkan peserta didik/mahasiswa dan pengajar/dosen dapat melakukan aktivitas pembelajaran tanpa harus bertatap muka. Universitas Diponegoro memiliki sistem otentikasi terpusat berbasis Lightweight Directory Access Protocol. Sistem otentikasi ini menggunakan username dan password yang terdapat dalam server LDAP. Universitas Diponegoro memiliki sistem informasi akademik yang beralamat di http://sia.undip.ac.id. Sistem informasi akademik ini digunakan mahasiswa untuk pengisian kartu rencana studi setiap semester. Berdasarkan latar belakang tersebut dibangun sebuah sistem pembelajaran jarak jauh yang menggunakan otentikasi login single sign on berbasis LDAP dan terintegrasi dengan sistem informasi akademik. Metodologi penelitian yang digunakan meliputi studi literatur dan bimbingan, pengambilan data dan analisis, menggunakan metode pengembangan prototyping. Sistem dibuat menggunakan LMS Moodle, ownCloud, dan CAS Server serta LDAP Server Universitas Diponegoro. Penelitian ini telah dihasilkan sebuah purwarupa sistem pembelajaran jarak jauh berbasis LMS Moodle dan ownCloud yang memiliki otentikasi Single Sign On berbasis CAS Server dan LDAP Server. Moodle telah dikembangkan sehingga dapat melakukan enroll mahasiswa kedalam course sesuai dengan mata kuliah pada kartu rencana studi.

This study aims to build a face recognition prototype that can recognize multiple face objects within one frame. The proposed method uses a local binary pattern histogram and Haar cascade classifier on low-resolution images. The lowest data resolution used in this study was 76 × 76 pixels and the highest was 156 × 156 pixels. The face images were preprocessed using the histogram equalization and median filtering. The face recognition prototype proposed successfully recognized four face objects in one frame. The results obtained were comparable for local and real-time stream video data for testing. The RR obtained with the local data test was 99.67%, which indicates better performance in recognizing 75 frames for each object, compared to the 92.67% RR for the real-time data stream. In comparison to the results obtained in previous works, it can be concluded that the proposed method yields the highest RR of 99.67%.