Software for Simplifying Embedded System Design Based on Event-Driven Method

Complexity of embedded system application increases along with the escalation of market demand. Embedded system design process must be enhanced to face design complexity problem. One of challenges in designing embedded system is speed, accuracy, and flexibility. The design process is usually conducted recursively to fulfill requirement of user and optimization. To solve this problem, it needs a system design that is flexible for adaptation. One of solutions is by optimizing all or some of the design steps. This paper proposes a design framework with an automatic framework code generator with of event driven approach. This software is a part of a design flow which is flexible and fast. Tron game and simple calculator are presented as a case study. Test result shows that this framework generator can increase speed of design’s flexibility

Forecasting Model for Lighting Electricity Load with a Limited Dataset using XGBoost

Energy forecasting is an important application of machine learning in renewable energy because it is used for operational, management, and planning purposes. However, using the electricity load dataset during COVID-19 is a research challenge in the forecasting model due to the limited dataset and non-stationarity. This paper proposes an extreme gradient boosting (XGBoost) forecasting model for a limited dataset. Forecasting models require large amounts of data to create high-accuracy models. We conduct research using the PT Biofarma office electricity usage dataset for eight months during the COVID-19 period. Because office activities were limited during the pandemic, the datasets obtained were few. Several methods are used for modeling limited datasets, namely XGBoost, multi-layer perceptron (MLP), autoregressive integrated moving average (ARIMA), and long short-term memory (LSTM). We have conducted several experiments using a limited dataset with these four methods. The test results with the t-test show that the electricity load data for work-from-office (WFO) and work-from-home (WFH) periods have a significant average difference. Then the test results with the augmented Dickey–Fuller (ADF) test show that our data is non-stationary. Compared to the benchmark method, the XGBoost method shows the best forecasting performance with mean absolute percentage error (MAPE), root mean squared error (RMSE), mean absolute error (MAE), and R2 of 0.48, 5.00, 3.09, and 0.61 respectively

Egg Quality Detection System Using Fuzzy Logic Method

Determining the quality of eggs in general is used by placing eggs on a flashlight. The detection system is very necessary to determine good egg quality or rotten eggs, so that the conditions of the eggs can be known by the chicken farm company and then will be sold to the community. This egg detecting system utilizes several sensor devices that are combined. The sensor used to detect the quality of eggs is a light sensor and a heavy sensor by connected with a microcontroller. So that there is no ambiguity towards the decision making of good egg or rotten eggs, then processing the data is obtained from these sensors using Fuzzy Logic and Firebase methods in real time as data storage media, and actuators will distribute or separate good eggs or the rotten eggs one. With the development of technology now, we can use the Internet of Things (IoT) technology, one of the systems check the quality of eggs which are good or not good. This system is built using a microcontroller to coordinate the running of the system using the Fuzzy Logic Method that applies inside. Final information is obtained on the form of egg quality in real time. The test results were carried out using the Fuzzy Logic method and obtained 95% results from 20 eggs and had 1 wrong egg. When using system hardware without using the fuzzy logic method on the microcontroller that using only a light sensor and a heavy sensor it produces a result of 75% from 20 eggs and had 5 wrong eggs. Using the egg detection optimization method can be increased up to 20%

Increasing The Precision Of Noise Source Detection System using KNN Method

This paper proposes Accurate Noise Source Detection System using K-Nearest Neighbor (KNN) Method. Noise or sound intensity is usually measured in decibels (dB). In an educational environment the recommended noise index limit is 55 dB. It means that noise louder than that limit is prohibited. While a loud noise in a campus area occurred, it will be troublesome for the authorities to deal with the matter. This paper proposes a noise source detection system that can locate the position of the noise source. This system used Df analog V2 voice sensor for detecting the loud noise intensity. A microcontroller with WiFi capabilities will allow the system to communicate with an Internet of Things (IoT) platform that can perform a learning method to detect the location of the loud noise source. KNN method is used as the learning method. The result shows a user is able to get a warning related to the noise that occurs in an area at once. The precision position of the noise source can be detected with 70% average accuracy rat

Performance Improvement of Non Invasive Blood Glucose Measuring System With Near Infra Red Using Artificial Neural Networks

Measurement of body blood sugar levels is one of the important things to do to reduce the number of people with diabetes mellitus. Non-invasive measurement techniques become a blood sugar measurement technique that is more practical when compared to invasive techniques, but this technique has not shown too high levels of accuracy, specificity and sensitivity. For this reason, the non-invasive measurement model using NIR and ANN is proposed to improve the performance of non-invasive gauges. Non-invasive blood sugar measuring devices will be built using a nodemcu board with photodiaodes and NIR transmitters whose data is then processed using ANN models compared to invasive blood sugar data obtained from 40 data. 40 data obtained then used as raw data to build ANN models which 75% percent of it use as training data and 25% od it will be use as testing data to validate accuration of the model been built, the split of data doing randomly without any interference from programmer or model designer. All the data gathered are data collected from all volunteers which willingly to test their blood glucose using invasive glucose meter and non invasive glucose meter which been built. The invasive glucose meter used to gather raw data of blood glucose is SafeAccu-2 with 95% level of accuracy so the accuracy and error parameter calculated in this research are based on that 95% level accurcy of the invasive device

Studi Perbandingan Performa Algoritma Penjadwalan untuk Real Time Data Twitter pada Hadoop

Hadoop is an open source and java based software framework. Hadoop consists of two main components, namely MapReduce and Hadoop Distributed File System (HDFS). MapReduce consists of Map and Reduce which are used for data processing, while HDFS is a places or directory where data can be stored. In carrying out a job that is not uncommonly diverse in its execution characteristics, a proper job scheduler is needed. There are many job schedulers that can be selected to matching job characteristics. Fair Scheduler uses a scheduler where the principle is to ensures that jobs will get the same resources as other jobs, with the aim of improving performance in terms of Average Completion Time. Hadoop Fair Sojourn Protocol Scheduler is a scheduling algorithm in Hadoop that can do scheduling based on the size of jobs provided. This study aims to compare the performance of the two schedulers for Twitter data characteristics. The test results show the Hadoop Fair Sojourn Protocol Scheduler has a better performance than the Fair Scheduler both from handling average completion time of 9.31% and job throughput of 23.46%. Then the Fair Scheduler excels in the task fail rate parameter of 23.98%.Hadoop merupakan sebuah framework software yang bersifat open source dan berbasis java. Hadoop terdiri atas dua komponen utama, yaitu MapReduce dan Hadoop Distributed File System (HDFS). MapReduce terdiri atas Map dan Reduce yang digunakan untuk pemrosesan data, sementara HDFS adalah tempat atau direktori dimana data hadoop dapat disimpan. Dalam menjalankan job yang tidak jarang terdapat keragaman karakteristik eksekusinya, diperlukan job scheduler yang tepat.  Terdapat banyak job scheduler yang dapat di pilih supaya sesuai dengan karakteristik job. Fair Scheduler menggunakan salah satu scheduler dimana prisnsipnya memastikan suatu jobs akan mendapatkan resource yang sama dengan jobs yang lain, dengan tujuan meningkatkan performa dari segi Average Completion Time. Hadoop Fair Sojourn Protocol Scheduler adalah sebuah algoritma scheduling dalam Hadoop yang dapat melakukan scheduling berdasarkan ukuran jobs yang diberikan. Penelitian ini bertujuan untuk melihat perbandingan performa kedua scheduler tersebut untuk karakteristik data twitter. Hasil pengujian menunjukan Hadoop Fair Sojourn Protocol Scheduler memiliki performansi lebih baik dibandingkan Fair Scheduler baik dari penanganan average completion time sebesar 9,31% dan job throughput sebesar 23,46%. Kemudian untuk Fair Scheduler unggul dalam parameter task fail rate sebesar 23,98%

Context-Aware Smart Door Lock with Activity Recognition Using Hierarchical Hidden Markov Model

Context-Aware Security demands a security system such as a Smart Door Lock to be flexible in determining security levels. The context can be in various forms; a person’s activity in the house is one of them and is proposed in this research. Several learning methods, such as Naïve Bayes, have been used previously to provide context-aware security systems, using related attributes. However conventional learning methods cannot be implemented directly to a Context-Aware system if the attribute of the learning process is low level. In the proposed system, attributes are in forms of movement data obtained from a PIR Sensor Network. Movement data is considered low level because it is not related directly to the desired context, which is activity. To solve the problem, the research proposes a hierarchical learning method, namely Hierarchical Hidden Markov Model (HHMM). HHMM will first transform the movement data into activity data through the first hierarchy, hence obtaining high level attributes through Activity Recognition. The second hierarchy will determine the security level through the activity pattern. To prove the success rate of the proposed method a comparison is made between HHMM, Naïve Bayes, and HMM. Through experiments created in a limited area with real sensed activity, the results show that HHMM provides a higher F1-Measure than Naïve Bayes and HMM in determining the desired context in the proposed system. Besides that, the accuracies obtained respectively are 88% compared to 75% and 82%

Increasing Smoke Classifier Accuracy using Naïve Bayes Method on Internet of Things

This paper proposes fire alarm system by implementing Naïve Bayes Method for increasing smoke classifier accuracy on Internet of Things (IoT) environment. Fire disasters in the building of houses are a serious threat to the occupants of the house that have a hazard to the safety factor as well as causing material and non-material damages. In an effort to prevent the occurrence of fire disaster, fire alarm system that can serve as an early warning system are required. In this paper, fire alarm system that implementing Naïve Bayes classification has been impelemented. Naïve Bayes classification method is chosen because it has the modeling and good accuracy results in data training set. The system works by using sensor data that is processed and analyzed by applying Naïve Bayes classification to generate prediction value of fire threat level along with smoke source. The smoke source was divided into five types of smoke intended for the classification process. Some experiments have been done for concept proving. The results show the use of Naïve Bayes classification method on classification process has an accuracy rate range of 88% to 91%. This result could be acceptable for classification accuracy

A Survey on Phishing Website Detection Using Hadoop

Phishing is an activity carried out by phishers with the aim of stealing personal data of internet users such as user IDs, password, and banking account, that data will be used for their personal interests. Average internet user will be easily trapped by phishers due to the similarity of the websites they visit to the original websites. Because there are several attributes that must be considered, most of internet user finds it difficult to distinguish between an authentic website or not. There are many ways to detecting a phishing website, but the existing phishing website detection system is too time-consuming and very dependent on the database it has. In this research, the focus of Hadoop MapReduce is to quickly retrieve some of the attributes of a phishing website that has an important role in identifying a phishing website, and then informing to users whether the website is a phishing website or not