Proceeding of the Electrical Engineering Computer Science and Informatics
Not a member yet
649 research outputs found
Sort by
Water Contents and Monoglycerides as Development Role of Biodiesel Standard in Indonesia for B30 Implementation
Get PDFSince 2016, Indonesia has implemented a policy of mixing biodiesel into diesel oil by 20 percents, also known as B20, based on regulation of the Minister of Energy and Mineral Resources number 12/2015. B20 is applied to all sectors that use diesel oil. In 2020, Indonesia continues to implement B30. Indonesia is the first nation in the world to implement B30, so Indonesia need biodiesel standard, especially biodiesel based from palm oil. This study is to present development standard of biodiesel in Indonesia. This study based on literature studies, discussion and biodiesel test sampling. The discussion is established by forum group discussion that consisting of government, industries and laboratories, and also by surveys to producers to take the biodiesel sample. This study result that the development of biodiesel standard in Indonesia is done by both road test and laboratory test. Monoglyceride and water content are the main concern of the validation of the biodiesel standard to implement B30 in Indonesia
Probabilistic Programming with Piecewise Objective Function for Solving Supplier Selection Problem with Price Discount and Probabilistic Demand
Get PDFIn this article, a supplier selection problem with price discount and probabilistic demand was solved by formulating a new probabilistic programming model with a piecewise objective function. The proposed model was able to be used by the decision-maker to calculate the optimal decision involving the appropriate raw material quantity to be ordered from each supplier to have minimal total procurement cost. A numerical experiment was conducted with some randomly generated data and the results showed the supplier selection problem was solved by the proposed model and the optimal decision value is achieved
Speech Recognition Implementation Using MFCC and DTW Algorithm for Home Automation
Get PDFThe use of speech recognition as part of home automation, especially for smart homes, is an exciting thing that is still being developed. That is because of human needs for comfort, convenience, quality of life, and better safety. Speech recognition built in this study is used as a device to control smart home devices by identifying the commands spoken by users, especially in a state of clean speech. The command used is a predetermined consecutive word. For the extraction of voice commands, the MFCC algorithm is used to match spoken words with templates using the Dynamic Time Warping (DTW) algorithm. DTW algorithm can find the difference between 2-time series that have different lengths of time. The results of the accuracy of this system by using these algorithms were successfully carried out by 86.67%, with an average time required to identify the commands of 5.28 seconds
IoT in Patient Respiratory Condition & Oxygen Regulator's Flowrate Monitor
Get PDFRespiratory condition monitoring, including respiration rate and oxygen saturation, and oxygen flowrate in oxygen tanks needed for patients undergoing oxygen therapy. Lack of medical staff in hospitals and efforts to minimize interactions between patients and nurses during the pandemic, open opportunity to develop the respiratory condition and oxygen flowrate monitoring systems using the Internet of Things (IoT) technology. Respiration rate and oxygen saturation data send to the local web network using the ESP8266 WiFi module and router. This monitoring system website was built on a server computer in the monitoring room using the PHP-MySQL programming language with Sublime Text 3 and XAMPP software. The website consists of features of the new user registration, user login, adding patient data, editing patient data, searching for patients, and patient respiratory condition monitoring pages. Connection speed based on the ability of the router range and the distance between the router and the microcontroller. For testing the reliability of the connection, the system simulated interrupted. The reconnecting times for the router and microcontroller range 3, 5, and 7 meters are 35.4 s, 35.6 s, and 35.3 s, respectively. The average response time for the system to receive data from the microcontroller and display the data on the monitoring page is 1.998 s, and there is no different data from the data on the web database and data on the serial monitor
Security and Privacy for the Internet of Things
Get PDFThe Internet of Things (IoT) represents a great opportunity to connect people, information, and things, which will in turn cause a paradigm shift in the way we work, interact, and think. The IoT is envisioned as the enabling technology for smart cities, power grids, health care, and control systems for critical installments and public infrastructure. This diversity, increased control and interaction of devices, and the fact that IoT systems use public networks to transfer large amounts of data make them a prime target for cyber attacks. In addition, IoT devices are usually small, low cost and have limited resources. Therefore, any protocol designed for IoT systems should not only be secure but also efficient in terms of usage of chip area, energy, storage, and processing. This presentation will start by highlighting the unique security requirements of IoT devices and the inadequacy of existing security protocols and techniques of the Internet in the context to IoT systems. Next, we will focus on security solutions for the IoT, with special focus on protection against physical and side channel attacks. In particular, we will focus on mutual authentication protocols for IoT devices based on security primitives that exploit hardware level characteristics of IoT devices
An Overview of Knowledge Mapping in Digital Business Industry: A Systematic Literature Review
Get PDFThe increasing number of studies in the knowledge map shows attention from researchers in academic and professional areas. However, the knowledge map implementation has not effectively implemented in an organization whose business in the digital business industry, especially startup organization. The main reason is the lack of stakeholder's understanding of the knowledge map concept. Thus, this study gives a comprehensive understanding of knowledge map implementation in the digital business industry within the last five years period. The study will answer what problems knowledge map tackled, tools, and techniques used currently, the obstacles and benefits of using a knowledge map. The review was conducted through the structured systematic literature review procedure. It started with a review protocol declaration and ended with an analysis of the prior researches obtained from five credible sources. Only 25 of 775 studies remain after several filtering stages. It is found that a knowledge map is mostly used for decision-making purposes. Most studies adopted a visual knowledge map and concept map, even though it is difficult to align the knowledge depth. In the end, this study's result will help stakeholders to reflect on their existing knowledge relationship structure. This study also offers directions for future research and professional practices in digital business industry firms to perfect their existing organizational intellectual capital through a knowledge map
Deep Convolutional Architecture for Block-Based Classification of Small Pulmonary Nodules
Get PDFA pulmonary nodule is a small round or oval-shaped growth in the lung. Pulmonary nodules are detected in Computed Tomography (CT) lung scans. Early and accurate detection of such nodules could help in successful diagnosis and treatment of lung cancer. In recent years, the demand for CT scans has increased substantially, thus increasing the workload on radiologists who need to spend hours reading through CT-scanned images. Computer-Aided Detection (CAD) systems are designed to assist radiologists in the reading process and thus making the screening more effective. Recently, applying deep learning to medical images has gained attraction due to its high potential. In this paper, inspired by the successful use of deep convolutional neural networks (DCNNs) in natural image recognition, we propose a detection system based on DCNNs which is able to detect pulmonary nodules in CT images. In addition, this system does not use image segmentation or post-classification false-positive reduction techniques which are commonly used in other detection systems. The system achieved an accuracy of 63.49% on the publicly available Lung Image Database Consortium (LIDC) dataset which contains 1018 thoracic CT scans with pulmonary nodules of different shapes and sizes
Exploring Success Factor for Mobile based Smart Regency Service using TRUTAUT Model Approach
Get PDFCurrently, almost every country struggles to apply city management to the concept of intelligent cities. Several previous studies have modeled the success, maturity, and success of information systems to use smart city principles. However, there are significant differences between city and district definition in terms of governance frameworks, regional size, livelihood differences, population, socio-economic, and socio-cultural dimensions. Therefore, work on the Smart District IT assessment requires new and unique studies that can differ substantially from smart cities. This study aims to explore the determinants of the success of Smart Regency services with mobile technology. The model and approach are the TRUTAUT model, which combines the concepts for the TRI and the UTAUT model. Two hundred eighty-nine participants could collect data with a smart cellular district service system - data processing using the SmartPLS v.3.2.8 software. Recent findings indicate that the variables proposed in the TRUTAUT model are a positive and essential relation. This study helps to determine the success of the application of intelligent mobile regional services applications. This study confirms that policymakers pay more considerable attention to critical questions that affect the district's smart cellular services' success
Prototype Design of Mobile Application 'Hydrolite' for Hydroponics Marketplace
Get PDFHydroponics is one of the effective farming methods to apply in big cities because it does not require extensive agricultural land. In addition, hydroponic products are cleaner, higher quality and free from pesticides. However, the development of hydroponic products in Indonesia is relatively slow. One of the factors causing the slow development of hydroponic agribusiness is that online sales media for hydroponic products are still limited, especially android-based e-marketplace application. Hydrolite is present as an e-marketplace that specifically sells vegetables grown using the hydroponic method, and sells all the equipment needed to farm hydroponically. Hydrolite is a prototype e-marketplace application designed using the Marvelapp platform. Further, Marvelapp is one of the best prototyping tools to support application development on mobile devices
Dynamic Hand Gesture Recognition Using Temporal-Stream Convolutional Neural Networks
Get PDFMovement recognition is a hot issue in machine learning. The gesture recognition is related to video processing, which gives problems in various aspects. Some of them are separating the image against the background firmly. This problem has consequences when there are incredibly different settings from the training data. The next challenge is the number of images processed at a time that forms motion. Previous studies have conducted experiments on the Deep Convolutional Neural Network architecture to detect actions on sequential model balancing each other on frames and motion between frames. The challenge of identifying objects in a temporal video image is the number of parameters needed to do a simple video classification so that the estimated motion of the object in each picture frame is needed. This paper proposed the classification of hand movement patterns with the Single Stream Temporal Convolutional Neural Networks approach. This model was robust against extreme non-training data, giving an accuracy of up to 81,7%. The model used a 50 layers ResNet architecture with recorded video training