Pub Date : 2020-02-01DOI: 10.1109/ICoSTA48221.2020.1570610835
Erni Widarti, Baltra Agusti Pramajuri, S. Suyoto
Supermarkets are modern markets that sell various household needs ranging from equipment items to various food products, one of which is beef. Consuming beef can have good or bad effects depending on the quality of the freshness of the beef itself. Therefore, before the beef is sold and consumed by the customer, it is necessary first to control the quality of the freshness of the beef. In supermarkets, the process of controlling the quality of beef still uses manual methods, so that the output produced is inaccurate and requires a long time. The presence of IoT technology can overcome these problems, so the process of controlling the quality of beef can be faster and more accurate. In this study, the controlling system is proposed to identify the quality of freshness of beef, the quantity of beef and temperature control in the sales rack. This controlling system utilizes NodeMCU microcontroller, three gas sensors (MQ-136, MQ-137, and TGS-2602), TCS-3200 color sensors, LED lights, buzzers, HC-SR04 ultrasonic sensors and LM35 temperature sensors. The workings of this system are to detect the gas and color produced from the beef and detect the temperature and presence of beef on each side of the rack. It can be concluded that the success of the proposed system has fairly good representation, in accordance with the theory regarding the level of freshness of beef that already exists.
{"title":"IoT Based: Improving Control System For High-Quality Beef in Supermarkets","authors":"Erni Widarti, Baltra Agusti Pramajuri, S. Suyoto","doi":"10.1109/ICoSTA48221.2020.1570610835","DOIUrl":"https://doi.org/10.1109/ICoSTA48221.2020.1570610835","url":null,"abstract":"Supermarkets are modern markets that sell various household needs ranging from equipment items to various food products, one of which is beef. Consuming beef can have good or bad effects depending on the quality of the freshness of the beef itself. Therefore, before the beef is sold and consumed by the customer, it is necessary first to control the quality of the freshness of the beef. In supermarkets, the process of controlling the quality of beef still uses manual methods, so that the output produced is inaccurate and requires a long time. The presence of IoT technology can overcome these problems, so the process of controlling the quality of beef can be faster and more accurate. In this study, the controlling system is proposed to identify the quality of freshness of beef, the quantity of beef and temperature control in the sales rack. This controlling system utilizes NodeMCU microcontroller, three gas sensors (MQ-136, MQ-137, and TGS-2602), TCS-3200 color sensors, LED lights, buzzers, HC-SR04 ultrasonic sensors and LM35 temperature sensors. The workings of this system are to detect the gas and color produced from the beef and detect the temperature and presence of beef on each side of the rack. It can be concluded that the success of the proposed system has fairly good representation, in accordance with the theory regarding the level of freshness of beef that already exists.","PeriodicalId":375166,"journal":{"name":"2020 International Conference on Smart Technology and Applications (ICoSTA)","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114549409","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}
Pub Date : 2020-02-01DOI: 10.1109/ICoSTA48221.2020.1570615793
A. S. Sunge, Y. Heryadi, Yoga Religia, Lukas
The clustering task aims to assign a cluster for each observation data in such a way that observations data within each cluster are more homogeneous to one another than with those in the other groups. Its wide applications in many research fields have motivated many researchers to propose a plethora of clustering algorithms. K-medoids are a prominent clustering algorithm as an improvement of the predecessor, K-Means algorithm. Despite its widely used and less sensitive to noises and outliers, the performance of K-medoids clustering algorithm is affected by the distance function. This paper presents experimentation findings to compare the performance of K-medoids clustering algorithm using Euclidean, Manhattan and Chebyshev distance functions. In this study the K-medoids algorithm was tested using the village status dataset from Gorontalo Province, Indonesia. Execution time and Davies Bouldin Index were used as performance metrics of the clustering algorithm. Experiment results showed that methods of Manhattan distance and Euclidean distance with the Index Davies value of 0.050.
{"title":"Comparison of Distance Function to Performance of K-Medoids Algorithm for Clustering","authors":"A. S. Sunge, Y. Heryadi, Yoga Religia, Lukas","doi":"10.1109/ICoSTA48221.2020.1570615793","DOIUrl":"https://doi.org/10.1109/ICoSTA48221.2020.1570615793","url":null,"abstract":"The clustering task aims to assign a cluster for each observation data in such a way that observations data within each cluster are more homogeneous to one another than with those in the other groups. Its wide applications in many research fields have motivated many researchers to propose a plethora of clustering algorithms. K-medoids are a prominent clustering algorithm as an improvement of the predecessor, K-Means algorithm. Despite its widely used and less sensitive to noises and outliers, the performance of K-medoids clustering algorithm is affected by the distance function. This paper presents experimentation findings to compare the performance of K-medoids clustering algorithm using Euclidean, Manhattan and Chebyshev distance functions. In this study the K-medoids algorithm was tested using the village status dataset from Gorontalo Province, Indonesia. Execution time and Davies Bouldin Index were used as performance metrics of the clustering algorithm. Experiment results showed that methods of Manhattan distance and Euclidean distance with the Index Davies value of 0.050.","PeriodicalId":375166,"journal":{"name":"2020 International Conference on Smart Technology and Applications (ICoSTA)","volume":"60 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116258513","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}
Pub Date : 2020-02-01DOI: 10.1109/ICoSTA48221.2020.1570610828
M. A. Huda, D. Haryono, H. Nugraha, A. Fitriani, Warsito Purwo Taruno
In this study, the characterization of the magnetic induction coil sensor for detecting void within the steel plate has been conducted. The study aims to evaluate the performance of coil designs in measuring some objects accurately. Experiments were performed by varying the frequency from 10 kHz to 2.5 MHz with an input voltage of 20 V. The results obtained show that all designs can distinguish air and normal steel objects by amplitude measurements. Only designs 3 and 4, however, can distinguish the normal and defect steels. Design 3 can differentiate them at the frequency of 250 kHz, while design 4 can distinguish at the frequency of 50 kHz. From different phase measurements, no significant differences are presented by all designs.
{"title":"Characterization of Magnetic Induction Coil Sensor for VOID Detection in Steel Plate","authors":"M. A. Huda, D. Haryono, H. Nugraha, A. Fitriani, Warsito Purwo Taruno","doi":"10.1109/ICoSTA48221.2020.1570610828","DOIUrl":"https://doi.org/10.1109/ICoSTA48221.2020.1570610828","url":null,"abstract":"In this study, the characterization of the magnetic induction coil sensor for detecting void within the steel plate has been conducted. The study aims to evaluate the performance of coil designs in measuring some objects accurately. Experiments were performed by varying the frequency from 10 kHz to 2.5 MHz with an input voltage of 20 V. The results obtained show that all designs can distinguish air and normal steel objects by amplitude measurements. Only designs 3 and 4, however, can distinguish the normal and defect steels. Design 3 can differentiate them at the frequency of 250 kHz, while design 4 can distinguish at the frequency of 50 kHz. From different phase measurements, no significant differences are presented by all designs.","PeriodicalId":375166,"journal":{"name":"2020 International Conference on Smart Technology and Applications (ICoSTA)","volume":"269 0","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120905269","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}
Pub Date : 2020-02-01DOI: 10.1109/ICoSTA48221.2020.1570613859
H. Abdillah, A. Afandi, Moh. Zainul Falah, A. Firmansah
Solar power plant(SPP) installation which is located in remote areas, provides some benefits for the community, not only an economic aspect but also social, and cultural improvements. Moreover, the SPP is used to exist an electrical load, which is combined with conventional power plants through some networks, or it is operated standalone in remote areas. This operation requires great efforts for controlling and monitoring because of a faraway location. In these studies, a remote monitoring system (RMS) is proposed and developed for monitoring performances of the photovoltaic power plant without visiting the location. The RMS sends data using a radio frequency and displays in voltage, current and power values. Test results show that a portable designing of RMS can be used to measure the SPP and it provides convenient monitoring for the operating condition and technical performance. This portable device is composed of a microcontroller, monitoring display, data sender and receiver, and battery. In addition, several tests show that the sunlight intensity measurement has an error compared with the measuring reference. Data testing is performed at fixed distances between the amount meters and time response also depends on the transmitting distance.
{"title":"Solar Energy Monitoring System Design Using Radio Frequency for Remote Areas","authors":"H. Abdillah, A. Afandi, Moh. Zainul Falah, A. Firmansah","doi":"10.1109/ICoSTA48221.2020.1570613859","DOIUrl":"https://doi.org/10.1109/ICoSTA48221.2020.1570613859","url":null,"abstract":"Solar power plant(SPP) installation which is located in remote areas, provides some benefits for the community, not only an economic aspect but also social, and cultural improvements. Moreover, the SPP is used to exist an electrical load, which is combined with conventional power plants through some networks, or it is operated standalone in remote areas. This operation requires great efforts for controlling and monitoring because of a faraway location. In these studies, a remote monitoring system (RMS) is proposed and developed for monitoring performances of the photovoltaic power plant without visiting the location. The RMS sends data using a radio frequency and displays in voltage, current and power values. Test results show that a portable designing of RMS can be used to measure the SPP and it provides convenient monitoring for the operating condition and technical performance. This portable device is composed of a microcontroller, monitoring display, data sender and receiver, and battery. In addition, several tests show that the sunlight intensity measurement has an error compared with the measuring reference. Data testing is performed at fixed distances between the amount meters and time response also depends on the transmitting distance.","PeriodicalId":375166,"journal":{"name":"2020 International Conference on Smart Technology and Applications (ICoSTA)","volume":"170 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122065768","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}
Pub Date : 2020-02-01DOI: 10.1109/ICoSTA48221.2020.1570614124
A. Sofwan, S. Sumardi, Alif Ihza Ahmada, Ibrahim Ibrahim, K. Budiraharjo, K. Karno
A greenhouse aims to provide optimum light and protect plants from the adverse climate which delivers an optimum environment for plant growth. A smart greenhouse is built with capability in environment manipulation. The smart device is installed in the greenhouse consists of many sensors, which measures environment parameters, such as temperature and air humidity. One of the environmental key parameters is temperature. The device uses this parameter to provide proper temperature for plant growth. The measured data is sent to the data server by utilizing the Message Queuing Telemetry Transport (MQTT) protocol through the Internet of Things (IoT) architecture. The smart device has succeeded in measuring parameters and performed environmental engineering. The temperature and air humidity sensors have average error measurements with values of 0.9 degrees Celsius and 7.22 percentage. Moreover, the device has been successful in transmitting the measured data by using the MQTT protocol.
{"title":"Smart Greetthings: Smart Greenhouse Based on Internet of Things for Environmental Engineering","authors":"A. Sofwan, S. Sumardi, Alif Ihza Ahmada, Ibrahim Ibrahim, K. Budiraharjo, K. Karno","doi":"10.1109/ICoSTA48221.2020.1570614124","DOIUrl":"https://doi.org/10.1109/ICoSTA48221.2020.1570614124","url":null,"abstract":"A greenhouse aims to provide optimum light and protect plants from the adverse climate which delivers an optimum environment for plant growth. A smart greenhouse is built with capability in environment manipulation. The smart device is installed in the greenhouse consists of many sensors, which measures environment parameters, such as temperature and air humidity. One of the environmental key parameters is temperature. The device uses this parameter to provide proper temperature for plant growth. The measured data is sent to the data server by utilizing the Message Queuing Telemetry Transport (MQTT) protocol through the Internet of Things (IoT) architecture. The smart device has succeeded in measuring parameters and performed environmental engineering. The temperature and air humidity sensors have average error measurements with values of 0.9 degrees Celsius and 7.22 percentage. Moreover, the device has been successful in transmitting the measured data by using the MQTT protocol.","PeriodicalId":375166,"journal":{"name":"2020 International Conference on Smart Technology and Applications (ICoSTA)","volume":"59 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132152578","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}
Pub Date : 2020-02-01DOI: 10.1109/ICoSTA48221.2020.1570611361
Widi Aribowo, S. Muslim, I. Basuki
The availability of electricity demand is very high. Many households and industrial equipment are using electricity as the source energy. The reliability of the power system in saving the budget is very much needed. This can be succeeded by doing good and proper operation planning. The important step of the electric power system operation planning is to predict load electricity. The load forecasting can support the corporations of electricity to assign the cost and power generation. Long-term forecasting is a technique of predicting periods for more than one year. The old data will be a guide to solve the issues. In this research, the concept of generalized regression neural network (GRNN) is to predict long-term electricity load. The advantage of the GRNN method can estimate the absolute function between input and output data sets directly from training data. The research was compared to the results of the actual data, Feed Forward Backpropagation Neural Network (FFBNN), Cascade Forward Backpropagation Neural Network (CFBNN) and Generalized Regression Neural Network (GRNN). The results of the study will be measured and validated using the Mean Absolute Deviation (MAD) and Mean Absolute Percentage Error (MAPE) methods.
{"title":"Generalized Regression Neural Network For Long-Term Electricity Load Forecasting","authors":"Widi Aribowo, S. Muslim, I. Basuki","doi":"10.1109/ICoSTA48221.2020.1570611361","DOIUrl":"https://doi.org/10.1109/ICoSTA48221.2020.1570611361","url":null,"abstract":"The availability of electricity demand is very high. Many households and industrial equipment are using electricity as the source energy. The reliability of the power system in saving the budget is very much needed. This can be succeeded by doing good and proper operation planning. The important step of the electric power system operation planning is to predict load electricity. The load forecasting can support the corporations of electricity to assign the cost and power generation. Long-term forecasting is a technique of predicting periods for more than one year. The old data will be a guide to solve the issues. In this research, the concept of generalized regression neural network (GRNN) is to predict long-term electricity load. The advantage of the GRNN method can estimate the absolute function between input and output data sets directly from training data. The research was compared to the results of the actual data, Feed Forward Backpropagation Neural Network (FFBNN), Cascade Forward Backpropagation Neural Network (CFBNN) and Generalized Regression Neural Network (GRNN). The results of the study will be measured and validated using the Mean Absolute Deviation (MAD) and Mean Absolute Percentage Error (MAPE) methods.","PeriodicalId":375166,"journal":{"name":"2020 International Conference on Smart Technology and Applications (ICoSTA)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121069461","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}
Pub Date : 2020-02-01DOI: 10.1109/ICoSTA48221.2020.1570615299
Ani Dijah Rahajoe, Rifki Fahrial Zainal, B. M. Mulyo, Boonyang Plangkang, Rahmawati Febrifyaning Tias
Feature selection is the pre-processing step that is widely used, especially in the field of data mining, to simplify processes that can reduce costs and computing time. Selected features can improve the best classification accuracy. In this work, a wrapper method approach is proposed using a modified harmony search. Modification is to update memory harmony using binary encoding. The coding process is adopted from the coding process of genetic algorithms for feature selection. The process of finding a new solution is done by manipulating each variable of the decision solution based on the harmony memory consideration and pitch adjustment procedures and the non-uniform mutation procedure. Evaluate its features using a support vector machine and is called a modified HS-SVM. The results showed that the proposed method has the same genetic algorithm performance for feature selection with SVM classification (GA-SVM), but has faster access time. This performance will reduce costs and computing time, especially if applied to high dimensional data. Both of these algorithms have 96.6 percent accuracy with one feature selected, and the harmony memory size is 50, and the generation size is 100.
{"title":"Feature Selection Based on Modified Harmony Search Algorithm","authors":"Ani Dijah Rahajoe, Rifki Fahrial Zainal, B. M. Mulyo, Boonyang Plangkang, Rahmawati Febrifyaning Tias","doi":"10.1109/ICoSTA48221.2020.1570615299","DOIUrl":"https://doi.org/10.1109/ICoSTA48221.2020.1570615299","url":null,"abstract":"Feature selection is the pre-processing step that is widely used, especially in the field of data mining, to simplify processes that can reduce costs and computing time. Selected features can improve the best classification accuracy. In this work, a wrapper method approach is proposed using a modified harmony search. Modification is to update memory harmony using binary encoding. The coding process is adopted from the coding process of genetic algorithms for feature selection. The process of finding a new solution is done by manipulating each variable of the decision solution based on the harmony memory consideration and pitch adjustment procedures and the non-uniform mutation procedure. Evaluate its features using a support vector machine and is called a modified HS-SVM. The results showed that the proposed method has the same genetic algorithm performance for feature selection with SVM classification (GA-SVM), but has faster access time. This performance will reduce costs and computing time, especially if applied to high dimensional data. Both of these algorithms have 96.6 percent accuracy with one feature selected, and the harmony memory size is 50, and the generation size is 100.","PeriodicalId":375166,"journal":{"name":"2020 International Conference on Smart Technology and Applications (ICoSTA)","volume":"22 1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126208950","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}
Pub Date : 2020-02-01DOI: 10.1109/ICoSTA48221.2020.1570614082
Hafidlotul F. Ahmad, M. Hardhienata, K. Priandana
This paper considers multi-robot search problems where a group of robots must discover and allocate themselves to targets. To solve this problem, we embed the robot with an algorithm called the Neighborhood with the Guaranteed Convergence Particle Swarm Optimization (N-GCPSO). This study considers the problem in a simulation environment. To reduce collision between robots, we integrate the N-GCPSO algorithm with a spatial particle extension algorithm. Simulation results show that the integration of N-GCPSO with a spatial partial extension algorithm increases the effectiveness of N-GCPSO by reducing the number of collisions between robots without reducing its performance in discovering and allocating targets.
{"title":"Integration of N-GCPSO Algorithm with Spatial Particle Extension Algorithm for Multi-Robot Search","authors":"Hafidlotul F. Ahmad, M. Hardhienata, K. Priandana","doi":"10.1109/ICoSTA48221.2020.1570614082","DOIUrl":"https://doi.org/10.1109/ICoSTA48221.2020.1570614082","url":null,"abstract":"This paper considers multi-robot search problems where a group of robots must discover and allocate themselves to targets. To solve this problem, we embed the robot with an algorithm called the Neighborhood with the Guaranteed Convergence Particle Swarm Optimization (N-GCPSO). This study considers the problem in a simulation environment. To reduce collision between robots, we integrate the N-GCPSO algorithm with a spatial particle extension algorithm. Simulation results show that the integration of N-GCPSO with a spatial partial extension algorithm increases the effectiveness of N-GCPSO by reducing the number of collisions between robots without reducing its performance in discovering and allocating targets.","PeriodicalId":375166,"journal":{"name":"2020 International Conference on Smart Technology and Applications (ICoSTA)","volume":"594 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126904252","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}
Pub Date : 2020-02-01DOI: 10.1109/ICoSTA48221.2020.1570613873
Muh Hanafi, Rusdah
YouTube is the biggest video-sharing website on the internet. Two billion users accessing YouTube every month and watch for one billion hours of video every day. Around 70% of YouTube watch time comes from mobile devices. Therefore, improving the quality of customer experience at a satisfactory level is a must for a network provider. Otherwise, customers will be dissatisfied and possibly switching to another network provider. A customer-centric strategy must be done to target the most profitable ones. Thus, understanding how quality of service (QoS) parameters can affect the Quality of Experience (QoE) is a significant consequence. This study aimed to segment the experiences of customers in accessing streaming videos on YouTube. Throughput, delay, and latency were used as the parameters of Quality of Service (QoS). The result shows that there are three segmentation formed, namely high, middle, and low. Those segments show the level of customers’ experiences based on QoS parameters used.
{"title":"Segmentation of Customers’ Experiences of YouTube Streaming Application Users in South Jakarta using K-means Method","authors":"Muh Hanafi, Rusdah","doi":"10.1109/ICoSTA48221.2020.1570613873","DOIUrl":"https://doi.org/10.1109/ICoSTA48221.2020.1570613873","url":null,"abstract":"YouTube is the biggest video-sharing website on the internet. Two billion users accessing YouTube every month and watch for one billion hours of video every day. Around 70% of YouTube watch time comes from mobile devices. Therefore, improving the quality of customer experience at a satisfactory level is a must for a network provider. Otherwise, customers will be dissatisfied and possibly switching to another network provider. A customer-centric strategy must be done to target the most profitable ones. Thus, understanding how quality of service (QoS) parameters can affect the Quality of Experience (QoE) is a significant consequence. This study aimed to segment the experiences of customers in accessing streaming videos on YouTube. Throughput, delay, and latency were used as the parameters of Quality of Service (QoS). The result shows that there are three segmentation formed, namely high, middle, and low. Those segments show the level of customers’ experiences based on QoS parameters used.","PeriodicalId":375166,"journal":{"name":"2020 International Conference on Smart Technology and Applications (ICoSTA)","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115170414","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}
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