Sistem Informasi Berbasis Web Untuk Memonitoring Perangkat Internet of Things (IoT) Menggunakan Node-Red

Information systems in monitoring Internet of Things (IoT) devices in this advanced telecommunications world are very important, where we can see in real-time the data displayed by the device. This is very useful in improving the performance and efficiency of the development party. The use of this technology makes it easier to anticipate error that occurs to system Internet of Things (IoT) devices. By being displayed using a dashboard, it makes it easier for users to read data provided by the Internet of Things devices. In this study, the author develops a Dashboard information system for the Internet of Things Device system. Data from the system will be sent to a database which will then be displayed using the Node-Red tools. This tool is a middleware that connects the Internet of Things (IoT) devices with a database, the results of reading the Internet of Things system will be stored in the database and the results can be seen through the Dashboard in real-time. The result of the measurement if it exceeds the standard limit that has been determined, will give an alert in the form of a notification in the Email

Automatic Title Generation for Text with RNN and Pre-trained Transformer Language Model

The development of the Internet has made it possible to obtain large amounts of text data. Search engines are used to select text data, but examining all text search results relevant to your query is impossible. Therefore, the only way to summarize content without losing meaning is to use text summarization or automatic text summarization(ATS) in natural language processing (NLP). We have proposed a neural network algorithm to generate title text from Arxiv’s legacy data collections. Recurrent Neural Network (RNN) units and temperature functions are used to create creative content. Google colab is used for experimental setup and result analysis. The results are compared with model accuracy and loss for better analysis

FRAMEWORK PENGAMANAN DATA DENGAN WHEEL FACTORIZATION PADA ALGORITMA RSA SEBAGAI PEMBANGKIT BILANGAN PRIMA

Keamanan merupakan sebuah factor yang sangat penting di dalam pengiriman data. Banyak teknik keamanan data yang dapat digunakan untuk mengamankan data-data yang bersifat rahasia tersebut. Salah satunya adalah dengan menggunakan teknik kriptografi dengan menggunakan RSA. Akan tetapi di dalam metode tersebut kemungkinan metode tersebut dapat di retas tetap ada. Proses pembangkitan bilangan prima yang dibutuhkan di dalam metode RSA tersebut adalah proses yang paling utama sehingga proses peretasan akan semakin sulit. Di dalam penelitian ini akan memberikan sebuah framework baru di dalam teknik pengamanan data dengan RSA dengan menggunakan wheel factorization sebagai pembangkit bilangan primanya sehingga proses peretasan algoritma tersebut akan semakin sulit

Игровые приложения системы обучения OSA

For the English full text of the article please see the attached PDF-File (English version follows Russian version).ABSTRACT The authors present a new solution in the field of rail distance learning - learning mobile applications. The aim of the project is to increase interest and motivation in the process of education and self-education. Examples are given of constructing a gaming platform for training tasks regarding principles of operation of railway automation and telemechanics devices. A conceptually new approach to technical on-job training is presented, based on introduction of game and competitive components, promoting enhancement of the professionalism of personnel of any qualification. The OSApp application, shown in the article, serves as an additional module to the main OSA distance learning platform. Keywords: transport, automation and telemechanics, distance learning, OSApp, OSA, technical training, neural networks, Anytime and Anywhere Learning.Полный текст на англ. языке находится в прилагаемом файле ПДФ (англ. версия следует после русской версии).Авторами представлено новое решение в области самообучения и самоконтроля - игровые мобильные приложения. Целью проекта является повышение интереса и мотивации к процессу образования и самообразования. Приведены примеры построения игровой платформы для задач обучения принципам функционирования устройств железнодорожной автоматики и телемеханики. Представлен концептуально новый подход к технической учёбе на предприятии, основанный на внедрении игровой и соревновательной составляющих, что способствует совершенствованию профессионализма персонала любой квалификации. Демонстрируемое в статье приложение OSApp, разработанное авторами, служит дополнительным модулем к основной платформе дистанционного и самостоятельного обучения OSA

PENGELOMPOKAN DATA KESEHATAN KOTA BANDUNG MENGGUNAKAN K-MEANS CLUSTERING

Pada abad 21 ini era modern semakin maju, salah satunya yang banyak dibutuhkan sekarang adalah Data Science yang sangat berguna bagi berbagai perusahaan dalam mengumpulkan, mengkaji, dan menganalisa terhadap suatu data dan permasalahan yang ada. Salah satunya adalah Dana Mining dengan memanfaatkan Clustering (pengelompokan data). Pada penelitian ini yaitu mengelompokan data kesehatan pada penyakit demam berdarah, diare, dan TB BTA+ yang sering terjadi di Kota Bandung berdasarkan jumlah penduduk dan jumlah pengidap penyakit demam berdarah, diare, dan TB BTA+ sesuai dengan jenis kelaminnya. Data yang digunakan berasal dari Dinas Kesehatan Kota Bandung dan Dinas Kependudukan dan Catatan Sipil Kota Bandung. Pengelompokan data ini menggunakkan metode K-Means Clustering. K-Means Clustering sendiri adalah pengelompokan data yang ada kedalam beberapa kelompok, dimana setiap satu cluster memiliki karakteristik yang sama. Perhitungan clustering memanfaatkan persamaan Euclidean Distance dimana jarak antar data dengan centroid. Penelitian ini bertujuan untuk melakukan analisa multiaspek atas data penyakit demam berdarah, diare, dan TB BTA+ dan membangun sebuah sistem bebasis website yang memiliki kemampuan untuk melakukan klasterisasi

Towards Association Rule-based Item Selection Strategy in Computerized Adaptive Testing

One of the most important stages of Computerized Adaptive Testing is the selection of items, in which various methods are used, which have certain weaknesses at the time of implementation. Therefore, in this paper, it is proposed the integration of Association Rule Mining as an item selection criterion in a CAT system. We present the analysis of association rule mining algorithms such as Apriori, FP-Growth, PredictiveApriori and Tertius into two data set with the purpose of knowing the advantages and disadvantages of each algorithm and choose the most suitable. We compare the algorithms considering number of rules discovered, average support and confidence, and velocity. According to the experiments, Apriori found rules with greater confidence, support, in less time.Una de las etapas más importantes de las pruebas adaptativas informatizadas es la selección de ítems, en la cual se utilizan diversos métodos que presentan ciertas debilidades al momento de su aplicación. Así, en este trabajo, se propone la integración de la minería de reglas de asociación como criterio de selección de ítems en un sistema CAT. Se presenta el análisis de algoritmos de minería de reglas de asociación como Apriori, FP-Growth, PredictiveApriori y Tertius en dos conjuntos de datos con el fin de conocer las ventajas y desventajas de cada algoritmo y elegir el más adecuado. Se compararon los algoritmos teniendo en cuenta el número de reglas descubiertas, el soporte y confianza promedios y la velocidad. Según los experimentos, Apriori encontró reglas con mayor confianza y soporte en un menor tiempo

An Overview of Vehicular Communications

The transport sector is commonly subordinate to several issues, such as traffic congestion and accidents. Despite this, in recent years, it is also evolving with regard to cooperation between vehicles. The fundamental objective of this trend is to increase road safety, attempting to anticipate the circumstances of potential danger. Vehicle-to-Vehicle (V2V), Vehicle-to-Infrastructure (V2I) and Vehicle-to-Everything (V2X) technologies strive to give communication models that can be employed by vehicles in different application contexts. The resulting infrastructure is an ad-hoc mesh network whose nodes are not only vehicles but also all mobile devices equipped with wireless modules. The interaction between the multiple connected entities consists of information exchange through the adoption of suitable communication protocols. The main aim of the review carried out in this paper is to examine and assess the most relevant systems, applications, and communication protocols that will distinguish the future road infrastructures used by vehicles. The results of the investigation reveal the real benefits that technological cooperation can involve in road safety. Document type: Articl

Kansei engineering with online review mining methodology for robust service design

Kansei Engineering (KE) has shown its prominent applicability in service design and development, focusing on translating and interpreting customers’ emotional needs (Kansei) into service characteristics. It is critical and promising as the services sector has grown faster than the manufacturing sector in developing economies in the past three decades. It accounted for an average of 55% of GDP in some developing economies. KE’s flexibility in collaborating with other methods and covering various service settings shows its unique superiority. However, there is criticism of the collected Kansei’s validity and the proposed solution’s robustness. It might be potentially caused by the dynamics of customer emotional needs and various service settings. As a result, Kansei is found to be somewhat fuzzy, unclear, and ambiguous. Hence, a more structured KE methodology incorporating the Kansei text mining process for robust service design is proposed. Kansei text mining approach will extract and summarize service attributes and their corresponding affective responses based on the online product descriptions and customer reviews. The Taguchi method will support the robustness of the proposed improvement strategy. An empirical study of a zoo as a tourism attraction service and its practical implication is discussed and validated in the proposed integrative framework

Towards Aiding Decision-Making in Social Networks by Using Sentiment and Stress Combined Analysis

[EN] The present work is a study of the detection of negative emotional states that people have using social network sites (SNSs), and the effect that this negative state has on the repercussions of posted messages. We aim to discover in which grade a user having an affective state considered negative by an Analyzer can affect other users and generate bad repercussions. Those Analyzers that we propose are a Sentiment Analyzer, a Stress Analyzer and a novel combined Analyzer. We also want to discover what Analyzer is more suitable to predict a bad future situation, and in what context. We designed a Multi-Agent System (MAS) that uses different Analyzers to protect or advise users. This MAS uses the trained and tested Analyzers to predict future bad situations in social media, which could be triggered by the actions of a user that has an emotional state considered negative. We conducted an experimentation with different datasets of text messages from Twitter.com to examine the ability of the system to predict bad repercussions, by comparing the polarity, stress level or combined value classification of the messages that are replies to the ones of the messages that originated them.This work was supported by the project TIN2017-89156-R of the Spanish government.Aguado-Sarrió, G.; Julian Inglada, VJ.; García-Fornes, A. (2018). Towards Aiding Decision-Making in Social Networks by Using Sentiment and Stress Combined Analysis. Information. 9(5):1-13. https://doi.org/10.3390/info9050107S1139