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

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

A Proposed Hidden Markov Model Method for Dynamic Device Pairing on Internet of Things End-Devices

Dynamic device pairing is a context-based zero-interaction method to pair end-devices in an IoT System based on Received Signal Strength Indicator (RSSI) values. But if RSSI detection is done in high level, the accuracy is troublesome due to poor sampling rates. This research proposes the Hidden Markov Model method to increase the performance of dynamic device pairing detection. This research implements an IoT system consisting an Access Point, an IoT End Device, an IoT Platform, and an IoT application and performs a comparison of two different methods to prove the concept. The results show that the precision of dynamic device pairing with HMM is better than without HMM and the value is 83,93%

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

Evaluation of IoT-Based Grow Light Automation on Hydroponic Plant Growth

This research aims to design, create, and evaluate a hydroponic automation system by monitoring the quality of plant growth that uses LED grow lights and natural light conditions on hydroponics. Checking whether the proposed system has a significant effect on the box Choy hydroponic growth is also an important aspect and becomes the contribution of this paper. The contribution of this paper is by discussing in detail the automation of LED grow lights using RTC modules and relays while also discussing the significance of LED light performance in hydroponic growth. On the proposed hydroponic automation systems, light-feeding is done automatically, this can be carried out with the help of a real-time clock (RTC) module and relays. Furthermore, the monitoring function is carried out through temperature and humidity measurement sensors. The data obtained from the sensor will be stored in the database for research on plant quality. The results of a comparison test show that the LED grows lights are superior in terms of fresh weight, the number of leaves, and plant height respectively with an average value of 23.6 grams, 11.2 leaves, and 18.1 cm on the 30th day. Compared to sunlight, respectively with an average value of 20.2 grams, 9.3 leaves, and 17.1 cm on the 30th day. PDF calculation and t-test are used to calculate the growth significance. The results are that the H0 for fresh weight and leaf growth rate is rejected and the H0 for plant growth rate is not rejected. It can be concluded that the LED grow lights give a significant effect on the fresh weight and leaf growth rate of IoT-based box Choy hydroponics if compared to sunlight

An Evaluation of SVM in Hand Gesture Detection Using IMU-Based Smartwatches for Smart Lighting Control

Hand gesture detection with a smartwatch can be used as a smart lighting control on the internet of things (IoT) environment using machine learning techniques such as support vector machine (SVM). However, several parameters affect the SVM model's performance and need to be evaluated. This study evaluates the parameters in building an SVM model for hand gesture detection in intelligent lighting control. In this study, eight gestures were defined to turn on and off four different lights, and then the data were collected through a smartwatch with an Inertial Measurement Unit (IMU) sensor. Feature selection using Pearson Correlation is then carried out on 36 features extracted from each gesture data. Finally, two sets of gestures were compared to evaluate the effect of gesture selection on model performance. The first set of gestures show that the accuracy of 10 features compared to the accuracy of 36 features is 94% compared to 71%, respectively. Furthermore, the second set of gestures has an accuracy lower than the first set of gestures, which is 64%. Results show that the lower the number of features, the better the accuracy. Then, the set of gestures that are not too distinctive show lower accuracy than the highly distinctive gesture sets. The conclusion is, in implementing gesture detection with SVM, low data dimensions need to be maintained through feature selection methods, and a distinctive set of gesture selection is required for a model with good performance.Hand gesture detection with a smartwatch can be used as a smart lighting control on the internet of things (IoT) environment using machine learning techniques such as support vector machine (SVM). However, several parameters affect the SVM model's performance and need to be evaluated. This study evaluates the parameters in building an SVM model for hand gesture detection in intelligent lighting control. In this study, eight gestures were defined to turn on and off four different lights, and then the data were collected through a smartwatch with an Inertial Measurement Unit (IMU) sensor. Feature selection using Pearson Correlation is then carried out on 36 features extracted from each gesture data. Finally, two sets of gestures were compared to evaluate the effect of gesture selection on model performance. The first set of gestures show that the accuracy of 10 features compared to the accuracy of 36 features is 94% compared to 71%, respectively. Furthermore, the second set of gestures has an accuracy lower than the first set of gestures, which is 64%. Results show that the lower the number of features, the better the accuracy. Then, the set of gestures that are not too distinctive show lower accuracy than the highly distinctive gesture sets. The conclusion is, in implementing gesture detection with SVM, low data dimensions need to be maintained through feature selection methods, and a distinctive set of gesture selection is required for a model with good performance

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

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%

Sistem Deteksi Mobil pada Aplikasi Pembayaran Gerbang Tol Berbasis Internet of Things

Keberadaan jalan tol saat ini sangat penting sebagai sarana penunjang transportasi dan menigkatkan perkembangan suatu daerah. Dengan adanya peningkatan sarana transportasi seperti jalan tol, maka akan berdampak pada produktivitas ekonomi bangsa. Semakin banyaknya jumlah kendaraan yang melewati jalan tol justru sering menyebabkan terjadinya penumpukan arus lalu lintas ketika mendekati pintu masuk dan pintu keluar tol. Penelitian ini mencoba membangun sistem yang dapat membantu mengurangi kemacetan pada gerbang tol untuk melakukan transaksi pembayaran tol. Penggunaan teknologi Internet of Things dan WiFi digunakan untuk melakukan deteksi kendaraan yang melewati jalan tol. Sehingga proses transaksi dapat dilakukan tanpa melakukan pemberhentian pada gerbang tol dengan jarak radius 10 sampai 15 meter dan dengan kecepatan hingga 15 Km/h. Waktu yang transaksi dihasilkan dengan menggunakan sistem ini adalah 9 sampai 15 detik. Dengan sistem yang ada saat ini dengan menggunakan RFID, transaksi harus dilakukan dengan melakukan pemberhentian terlebih dahulu dengan waktu transaksi 15 detik pada kondisi lancar dan 15 sampai 20 detik pada kondisi mace