2022 6th International Conference on Electrical, Telecommunication and Computer Engineering (ELTICOM)最新文献

{"title":"Identification of Safe Air Quality for Activity Using Long Short-Term Memory","authors":"R. Rahmat, Fahrurrozi Lubis, M. Furqan, S. Faza, Farhad Nadi, Nur Intan Raihana Ruhayem","doi":"10.1109/ELTICOM57747.2022.10037891","DOIUrl":"https://doi.org/10.1109/ELTICOM57747.2022.10037891","url":null,"abstract":"Air pollution is one of the problems that is often occurred in the cities with high industrial and transportation levels. Several informations are needed by the community so that they can be use it for people’s health concerns. Air Quality Index is a standard for determining air quality and pollution based on the main parameters, namely: Carbon Dioxide (CO2), Sulfur Dioxide (SO2), Ozone (O3), and Particulate Dust (PM10 and PM2.5). While, for the methods, we suggest Long Short-Term Memory model which have good performance in identifying safe air quality for activities. The model conducts training with a parameter of 100 epoch, learning rate = 0.001, batch size = 32. Testing uses the optimizer, activation function, and the right function to measure air quality with an accuracy of up to 98%. Thus, we adjust LSTM model with Adamax optimizer and Sigmoid activation function is the best parameter to get the highest accuracy.","PeriodicalId":406626,"journal":{"name":"2022 6th International Conference on Electrical, Telecommunication and Computer Engineering (ELTICOM)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124992976","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Realtime Monitoring System Towards Waste Generation Management","authors":"E. W. Sinuraya, Yosua Alvin Adi Soetrisno, Annisa Putri Setianingrum, Tri Widi Indah Permata Sari","doi":"10.1109/ELTICOM57747.2022.10038115","DOIUrl":"https://doi.org/10.1109/ELTICOM57747.2022.10038115","url":null,"abstract":"Waste management is a significant concern to protect the environment and the population’s health. However, due to its uncertainty and high variability, waste generation has dynamic nurture that results in ineffective, inaccurate, and unreliable waste management. Therefore, the existence of the IoT, information systems, and artificial intelligence can help support sustainability and decrease the amount of waste. This research proposes a monitoring system equipped with a forecasting feature and implemented in a mobile application to overcome the limitations of conventional waste management systems. This system is built on two Internet of Things (IoT) architecture nodes with an android-based mobile application interface. Measures the unfilled level of the bin, processes it, and sends it to the database. The data was then computed using Levenberg Marquardt’s Artificial Neural Network to predict the height of the garbage. The results of the IoT communication show that the average delay in sending data to the database is 2. 886s and 2. 912s, with 0% packet loss. The correlation coefficients generated in the Levenberg Marquardt model training process are 0.925 and 0.965. In addition to displaying garbage height data and prediction results, this system application can also display the location of the bin and receive notifications. This monitoring system has also been tested directly on the Undip waste manager using the SUS questionnaire. Based on these tests, the SUS score of70.2 showed that the application already had good usability.","PeriodicalId":406626,"journal":{"name":"2022 6th International Conference on Electrical, Telecommunication and Computer Engineering (ELTICOM)","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116749930","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Utilization of Small Wind Turbines as Source Alternative Electrical Energy for Lighting in the Banyak Island Tourism Area, Aceh Singkil, Indonesia","authors":"A. Hasibuan, Muhammad Daud, Muhammad Sayuti, Ferdy Hidayatullah, W. Siregar, Rizqan Fachroji","doi":"10.1109/ELTICOM57747.2022.10037795","DOIUrl":"https://doi.org/10.1109/ELTICOM57747.2022.10037795","url":null,"abstract":"Currently, the development of renewable energy sources for power generation is very prominent. This is done to reduce the availability of fossil energy sources. In addition, the burning of fossil energy sources will have an impact on the environment and the production of gas emissions. Based on the availability of sources and small environmental impact, wind power plants have great energy potential to be explored. Balai island Banyak island in Aceh Singkil-Indonesia has been developed as a tourist destination that has great potential to increase regional foreign exchange. The increasing number of visitors in the Balai Island area will certainly have an impact on increasing the need for electrical energy. The electricity consumption in this area is only supplied by using diesel power plants. Renewable energy based on wind power plants needs to be developed as an alternative to preserve Balai Island from environmental impacts due to the use of fossil-based electricity. Therefore, a study of the potential resources of wind power plants must be carried out to implement wind power plants on Balai Island. The purpose of this study was to determine the potential of wind energy and determine the appropriate type of wind turbine in the Water Front Tourism Area of Balai island Banyak Island as an alternative lighting. The method used in this research is quantitative type with data processing and descriptive approach. The data obtained were analyzed using analytical methods to calculate the potential of wind energy and its conversion into electrical energy. After analyzing the wind speed data in 2020 in the Balai island tourist area with the weibull distribution equation, the parameters k = 2.935758902 and c = 8.921518. Through capacity factor analysis, it is known that small-scale wind turbines produce energy of 115,154 kWh/year per turbine with an electrical energy cost of Rp. 507,S26/kWh and the total annual net income (net income per year) is Rp. 123,602,608.34.","PeriodicalId":406626,"journal":{"name":"2022 6th International Conference on Electrical, Telecommunication and Computer Engineering (ELTICOM)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127011990","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Implementation of Outdoor to Indoor Path Loss Model at 1.8 GHz and 2.1 GHz with a Transmitter Placed on Top of The Building","authors":"M. Pinem, Panangian Mahadi Sihombing, M. Zulfin, Sihar P. Panjaitan, H. Rangkuti, Muhammad Amril Siregar","doi":"10.1109/ELTICOM57747.2022.10037980","DOIUrl":"https://doi.org/10.1109/ELTICOM57747.2022.10037980","url":null,"abstract":"Nowadays, the use of mobile communication devices has become a necessity in the lecture environment. This is evidenced by the increase in wireless communication traffic in the lecture building. Generally, users inside the building receive radio waves from transmitters placed outside the building to support wireless communication. Thus, an adequate received signal level (RSL) is required to ensure smooth wireless communication in the building. However, the power level received by the user will decrease along with the increase in distance and obstacles inside the building. This problem can generally be solved by using a power amplifier (repeater device) or wireless fidelity (WiFi) network, but nowadays most buildings do not have these devices.Therefore, an accurate path loss model is needed to predict the path loss of radio wavesfrom outdoors to indoors. Thus, the prediction of path loss can be a reference for designing transmitters that can support smooth wireless communication in the building. This study has compared three outdoor to indoor path loss models, namely the 3GPP Model, the WINNER+Model, and the COST231 Model. Based on the results of research data analysis, it is known that the COST231 Model is the most accurate of the other path loss models used. This is evidenced by the lowest dominant Mean Error (ME), Root Mean Square Error (RMSE), and Standard Deviation Error (SDE) values.","PeriodicalId":406626,"journal":{"name":"2022 6th International Conference on Electrical, Telecommunication and Computer Engineering (ELTICOM)","volume":"61 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131564379","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Design and Development of Posture Detection System Using Load Sensor","authors":"F. Fahmi, Muhammad Fahlevi","doi":"10.1109/ELTICOM57747.2022.10038202","DOIUrl":"https://doi.org/10.1109/ELTICOM57747.2022.10038202","url":null,"abstract":"A state of health, physically, mentally, spiritually, and socially, which is essential. Needed by the community in life to live productively socially and economically. One that affects health is sitting posture. Most of the time, humans spend their time sitting. The wrong sitting posture will significantly affect a person’s health. Detecting poor posture can help prevent profound damage to human health. This thesis builds a sitting posture detection system using a force sensor (load sensor), which is useful for providing information on the user’s pressure when sitting. This tool will get pressure from the hip seat. It can also provide ergonomic value to products such as Posture Cloud Medi Fit Stores. The results displayed by this tool are in the form of sit mapping. Users can draw conclusions about the correct way to sit and health diagnoses by knowing the pressure when sitting.","PeriodicalId":406626,"journal":{"name":"2022 6th International Conference on Electrical, Telecommunication and Computer Engineering (ELTICOM)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133940145","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Population Estimation Using Wi-Fi’s Received Signal Strength Indicator Based on Artificial Neural Network","authors":"Brian Aaron R. Bermudez, Carloui R. Cruz, Jushua D. Ramos, Zoren P. Mabunga, Jennifer C. Dela Cruz, Renato R. Maaliw Iii, A. Ballado","doi":"10.1109/ELTICOM57747.2022.10037906","DOIUrl":"https://doi.org/10.1109/ELTICOM57747.2022.10037906","url":null,"abstract":"The development of population estimation using three (3) constructed received signal strength indicator (RSSI) acquisition devices with NodeMCU ESP8266 as the brain for data receiving and a Wi-Fi transmitter – all channeled into ThingSpeak for monitoring RSSI data and deployed into a designed graphical user interface (GUI) built and trained on MATLAB was demonstrated in this paper. The developed system considered a controlled indoor environment capable of predicting and estimating the number of people when moving and stationary. Based on the results of the training, validation, and testing for the two cases, an overall mean squared error of 1.36337 for moving with an overall response R-value of 0.87995 based on 125 hidden layers and 0.272564 for stationary with an overall response R-value of 0.98592 based on 95 hidden layers were obtained. The numerical results show that the model based on RSSI of Wi-Fi technology can classify the number of people inside the laboratory room from zero (vacant) up to 10 students.","PeriodicalId":406626,"journal":{"name":"2022 6th International Conference on Electrical, Telecommunication and Computer Engineering (ELTICOM)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133826985","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Identification of Medicinal Mushrooms using Computer Vision and Convolutional Neural Network","authors":"Mark Jayson Y. Sutayco, M. V. Caya","doi":"10.1109/ELTICOM57747.2022.10038007","DOIUrl":"https://doi.org/10.1109/ELTICOM57747.2022.10038007","url":null,"abstract":"Recent studies have demonstrated the predictive capability of deep learning methods in different agriculture fields. In this study, the researcher developed a device through the integration of Convolutional Neural Network (CNN) deep learning models and Raspberry Pi that can classify six medicinal mushrooms including Lion’s Mane, Oyster, Reishi, Shiitake, Shimeji, and Volva. The pre-trained CNN – Inception-V3 architecture was utilized to train 600 sample images of medicinal mushroom. The study employs the 80 by 20 ratio, in which the model was trained using 80 percent of the entire data and its performance was validated using the remaining 20 percent. Overall, the accuracy of the model achieved 92.7 percent. Although a relatively satisfactory performance was obtained, improvement of model performance should be sought using different optimization methods. Furthermore, for future studies, continual learning methods that alleviate catastrophic forgetting can be applied in the developed device to allow robust predictions with other types of mushrooms or other type of tasks.","PeriodicalId":406626,"journal":{"name":"2022 6th International Conference on Electrical, Telecommunication and Computer Engineering (ELTICOM)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117262620","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Comparison Analysis Of Electricity Use By State Electricity Company With Renewable Energy Sources In Household Type 54","authors":"A. Hasibuan, Mursalin, Muhammad Daud, R. Kurniawan, W. Siregar, Putri Anjali Safna","doi":"10.1109/ELTICOM57747.2022.10037828","DOIUrl":"https://doi.org/10.1109/ELTICOM57747.2022.10037828","url":null,"abstract":"The current development of renewable energy sources for power generation is very prominent. This is because fossil energy is decreasing day by day. Indonesian people predominantly use electrical energy from the State Electricity Company (PLN). And PLN still uses conventional energy sources, namely fossil energy. So, this research discusses the comparison of the efficiency of electrical energy consumption in type 54 houses with a 220 VAC system originating from State Electricity Company with sources that come from renewable energy, namely solar cells. By comparing the main parameters, the value of loss and efficiency of electrical energy consumption in each system. There are three main stages in the proposed research activity that must be carried out. First, designing and simulating home electrical installations for AC systems originating from State Electricity Company. Second, designing and simulating home electrical installations for DC systems originating from renewable energy sources, namely solar cells. Third, compare the use of electrical energy between the two sources from State Electricity Company and from solar cells. The output that will be generated from this research is knowing the comparison of the use of electrical energy sourced from State Electricity Company with that sourced from solar cells in type 54 residential houses.","PeriodicalId":406626,"journal":{"name":"2022 6th International Conference on Electrical, Telecommunication and Computer Engineering (ELTICOM)","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124130653","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}