Proceeding of the Electrical Engineering Computer Science and Informatics
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Method Using IOT Low Earth Orbit Satellite to Monitor Forest Temperature in Indonesia
Get PDFThe Purpose of this paper is to ensure the proper functioning of the Monitoring Forest Temperature program in Indonesia using the IoT Narrow-Band Low earth orbit Satellite. As a new technology for monitoring the temperature continue to expand, its implementation in developing countries particularly in Indonesia requires strategic guidance of how the whole process will be executed. Nevertheless, due to this, cross-sectoral partnership in technology, policy, budget, industry is essential to be addressed. The World Bank has recorded the loss from forest fire where 28 million people directly affected including 19 people who died and over 500 thousand people suffered from respiratory problems. Smokes from forest and land fires have also struck Malaysia, Singapore, and Brunei Darussalam respectively. To respond to this, the IoT ( Internet of Things ) now comes with an extensive feature, using the capability of satellite reach. The Narrow Band Low Earth Orbit Satellite has released a feature for IoT connect to Low Orbit Satellite and transmit the data from the sensor directly. Therefore, we argue that this technology is crucial and needs to be functioned immediately to monitor forest temperature in Indonesia
Performance Comparison of Schedulers in MmWave Communication using NS-3
Get PDFMillimeter-wave (mmWave) has proven to provide the bandwidth requirement for the new radio (NR) on 5G. MmWave has been developed as a new technology to support enhanced mobile broadband (eMBB), massive machine-type communication (mMTC), and ultra-reliable low latency communication (URLLC). Since using a high frequency, mmWave also has some disadvantages that could not be avoided, such as small coverage, high signal attenuation, limited against some obstacles, and sensitive to the influence of signal quality. This paper discusses the effect of signal quality on 5G performance using mmWave while sending or receiving packet data by using three types of the scheduler, such as Round Robin, Proportional Fairness, and Max Rate scheduler. Signal quality will impact the value of modulation and coding scheme (MCS) that will be used. Our experiments using NS-3 based on the scenario showed that in the same location and number of UEs, performance throughput using Round Robin and Max Rate with excellent signal strength could reach the maximum throughput. The use of Proportional Fairness could lead only to reaching 50% of the maximum throughput. On the other hand, the use of the Proportional Fairness scheduler causes the weak signal to be unstable. Using Round Robin scheduler, the throughput is more stable. Different from the result using the Max Rate scheduler, the UE with the best signal quality compared to other UEs, was the only UE that get the resources allocation
Automatic Grading System for Spreadsheet Formula
Get PDFSpreadsheet is one of the tools that can be used to learn data analysis. Data analysis in spreadsheet can be done using formula. Spreadsheet tools can also be used for exams. For the assessment, there is a problem when the number of answers that need to be checked is large, that is it takes a long time to check all the answers. For this reason, an automatic grading system (autograder) that can evaluate formula in spreadsheet is needed. The method used in developing the autograder system is matching the answer key formula with the student's answer formula. The autograder system assesses the answer by calculating the similarity of the student's answer formula with the answer key formula. This paper explains how to build an autograder system that can evaluate the formula. At the end, an autograder system has been built successfully. It has been tested with 43 testcases and all of them are passed
Potential for Reducing CO2 Emissions in the Operation of Subcritical Power Plants into Supercritical
Get PDFThe consumption of electricity that increase anytime, also increases CO2 emissions in the air as a result of coal combustion flue gas at the power plant. The operation of supercritical boilers on the power plant will lead to higher thermal efficiency compared to subcritical boilers. Higher steam pressure boiler will increase the thermal efficiency and automatically reduce CO2 emissions due to a reduction in fuel consumption at the same boiler efficiency and heating value of coal. At 166.9 bar subcritical steam boiler thermal efficiency was 45.47 % and CO2 emissions were 602.2 tons while at supercritical pressure 240 bar, efficiency increased to 47.12 % with a reduction in CO2 emissions of 20.9 tons to 581.3 tons
Design of Integrated Bioimpedance Analysis and Body Mass Index for Users with Special Needs
Get PDFThis research was conducted with the aim to build integration between Bioimpedance Analysis (BIA) and Body Mass Index (BMI) for users with special needs. The proposed system can measure height, weight, BMI and body composition simultaneously to be used by the elderly population and handicapped users. The proposed system is developed as a chair equipped with several system blocks, namely BIA block, BMI block, power supply block, and microcontroller block. Before starting the measurement, users only need to enter their age and gender data. The whole system is controlled by using Arduino Mega 2560 on the microcontroller block equipped with keypad for data input and an LCD to display measurement results. System testing is performed by comparing the measurement results with Omron HBF-375. The test involved 8 volunteers (4 males and 4 females). The test results show that the integrated BIA-BMI works well with an average error of 1.5%
Spoken Word and Speaker Recognition Using MFCC and Multiple Recurrent Neural Networks
Get PDFIdentification of spoken word and speaker has been featured in many kinds of research. The problem or obstacle that persists is in the pronunciation of a particular word. So it is the noise that causes the difficulty of words to be identified. Furthermore, every human has different pronunciation habits and is influenced by several variables, such as amplitude, frequency, tempo, and rhythmic. This study proposed the identification of spoken sounds by using specific word input to determine the patterns of the speaker and spoken using Mel-frequency Cepstrum Coefficients (MFCC) and Multiple Recurrent Neural Networks (RNN). The Mel coefficient of MFCC is used as an input feature for identifying spoken words and speakers using RNN and Long Short Term Memory (LSTM). Multiple RNN works spoken word and speaker in parallel. The results obtained by multiple RNN have an accuracy of 87.74%, while single RNNs have 80.58% using Adam of new data. In order to test our model computational regularly, the experiment tested K-fold Cross-Validation of datasets for spoken and speakers with an average accuracy of 86.07%, which means the model to be able to learn on the dataset without being affected by the order or selection of test data
Software Defect Prediction Using Neural Network Based SMOTE
Get PDFSoftware defect prediction is a practical approach to improve the quality and efficiency of time and costs for software testing by focusing on defect modules. The defect prediction software dataset naturally has a class imbalance problem with very few defective modules compared to non-defective modules. Class imbalance can reduce performance from classification. In this study, we applied the Neural Networks Based Synthetic Minority Over-sampling Technique (SMOTE) to overcome class imbalances in the six NASA datasets. Neural Network based on SMOTE is a combination of Neural Network and SMOTE with each hyperparameters that are optimized using random search. The results use a nested 5-cross validation show increases Bal by 25.48% and Recall by 45.99% compared to the original Neural Network. We also compare the performance of Neural Network based SMOTE with SMOTE + Traditional Machine Learning Algorithm. The Neural Network based SMOTE takes first place in the average rank
Analysis of Autopsy Mobile Forensic Tools against Unsent Messages on WhatsApp Messaging Application
Get PDFThis paper discusses the new feature that is implemented in most social media messaging applications: the unsent feature, where the sender can delete the message he sent both in the sender and the recipient devices. This new feature poses a new challenge in mobile forensic, as it could potentially delete sent messages that can be used as evidence without the means to retrieves it. This paper aims to analyze how well the Autopsy open-source mobile forensics tools in extracting and identifying the deleted messages, both that are sent or received. The device used in this paper is a Redmi Xiaomi Note 4, which has its userdata block extracted using linux command, and the application we're using is WhatsApp. Autopsy will analyze the extracted image and see what information can be extracted from the unsent messages. From the result of our experiment, Autopsy is capable of obtaining substantial information, but due to how each vendor and mobile OS store files and databases differently, only WhatsApp data can be extracted from the device. And based on the WhatsApp data analysis, Autopsy is not capable of retrieving the deleted messages. However it can detect the deleted data that is sent from the device. And using sqlite3 database browser, the author can find remnants of received deleted messages from the extracted files by Autopsy
Search Engine for Halal Linked Open Data Using Entity Ranking Approach
Get PDFHalal concept is an essential aspect of Muslim daily life. Currently, many organizations around the world provide halal certification services as known as halal certification bodies. In the majority, these organizations provide halal product information on their website. However, information is presented in different formats, such as pdf, table, and text. As a result, the user is difficult to search for information on these websites. Therefore, we develop search engine on halal linked open data to facilitate users for searching halal products. We use an entity ranking approach to retrieve relevant items based on user queries that consist of an independent-ranking and dependent-ranking method. Independent ranking employs a link-count approach to indicate the information richness of the entity. Dependent ranking employs term frequency-inverse entity frequency (TF-IEF) to measure the similarity of an entity based on terms. We use Apache Lucene to perform indexing and search process. Also, we use the Neo4j graph database to save entity ranking computation results. The results show that the system delivers excellent results. The Mean Average Precision (MAP) for top-5 results is 91,2%
Cholesterol Detection Based on Eyelid Recognition Using Convolutional Neural Network Method
Get PDFLack of public awareness of health will cause serious problems. A small example, people now tend to always consume fatty foods without thinking about the risk of cholesterol levels in the body. Information on the level of cholesterol suffered by humans can be seen on the human eyelids. The eyelids, one part of the eye, can be known as a person's cholesterol level by observing the eyelids' shape and condition, but many people do not know about this. This application is an application made to detect cholesterol based on the shape of the eyelids. This can determine whether a person is exposed to cholesterol or not, using the Convolutional Neural Network (CNN) method in the classification process. This study provides an output in the form of early detection of cholesterol and prevention so that users can minimize the possibility of illness that will be suffered. This research was conducted to detect cholesterol one eyelid based on digital images. For detecting a cholesterol level, this system got 95.83% of accuracy