Pub Date : 2018-12-01DOI: 10.1109/ICCEREC.2018.8711998
Yacob Sapan Panggau, M. Asvial
Fixed broadband access infrastructure in Indonesia has not been able to reach 100% of remote villages and important public facilities such as schools, hospitals and community health centers as scheduled in the Indonesian Broadband Plan. Implementation of High Throughput Satellite (HTS) and subsidies for customer devices is a solution made by European Union countries and other countries to reach 100% of their territory. The Indonesian Government's HTS implementation is also believed to be a solution to reach remote villages and important public facilities. But building HTS and subsidies for customer devices is a new thing for the Government of Indonesia, requires huge costs and high risk costs. This study aims to assess the implications of the costs of implementing HTS and subsidies for customer devices. The cost model is used to determine the maximum satellite user capacity, total costs, and satellite service unit costs in implementing 7 Gbps and 65 Gbps HTS. The analysis shows that the Government needs to provide, in 10 years, an annual fee of Rp 1.47 trillion for 7 Gbps HTS, or Rp 3.97 trillion for 65 Gbps HTS. HTS 65 Gbps is able to serve more, a maximum of 675,000 users with a unit cost of Rp. 468,652 before subsidies and Rp 201,445 after subsidies. 7 Gbps HTS is only able to serve a maximum of 70,000 customers with a unit cost of Rp. 1,721,605 before subsidies and Rp 1,273,241 after subsidies. This study proposes, it is better to build satellites with greater throughput because they can produce smaller unit costs, of course if all the resources for building satellites are available. The government of Indonesia needs to find a Ka Band that can be used and pay attention to public policies for subsidies and funding sources to implement satellite broadband access. Further research is also needed to assess the business feasibility if the satellite will be operated
{"title":"Analysis of Satellite Broadband Access Implementation in Indonesia Using Costing Methodology","authors":"Yacob Sapan Panggau, M. Asvial","doi":"10.1109/ICCEREC.2018.8711998","DOIUrl":"https://doi.org/10.1109/ICCEREC.2018.8711998","url":null,"abstract":"Fixed broadband access infrastructure in Indonesia has not been able to reach 100% of remote villages and important public facilities such as schools, hospitals and community health centers as scheduled in the Indonesian Broadband Plan. Implementation of High Throughput Satellite (HTS) and subsidies for customer devices is a solution made by European Union countries and other countries to reach 100% of their territory. The Indonesian Government's HTS implementation is also believed to be a solution to reach remote villages and important public facilities. But building HTS and subsidies for customer devices is a new thing for the Government of Indonesia, requires huge costs and high risk costs. This study aims to assess the implications of the costs of implementing HTS and subsidies for customer devices. The cost model is used to determine the maximum satellite user capacity, total costs, and satellite service unit costs in implementing 7 Gbps and 65 Gbps HTS. The analysis shows that the Government needs to provide, in 10 years, an annual fee of Rp 1.47 trillion for 7 Gbps HTS, or Rp 3.97 trillion for 65 Gbps HTS. HTS 65 Gbps is able to serve more, a maximum of 675,000 users with a unit cost of Rp. 468,652 before subsidies and Rp 201,445 after subsidies. 7 Gbps HTS is only able to serve a maximum of 70,000 customers with a unit cost of Rp. 1,721,605 before subsidies and Rp 1,273,241 after subsidies. This study proposes, it is better to build satellites with greater throughput because they can produce smaller unit costs, of course if all the resources for building satellites are available. The government of Indonesia needs to find a Ka Band that can be used and pay attention to public policies for subsidies and funding sources to implement satellite broadband access. Further research is also needed to assess the business feasibility if the satellite will be operated","PeriodicalId":250054,"journal":{"name":"2018 International Conference on Control, Electronics, Renewable Energy and Communications (ICCEREC)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128987767","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 : 2018-12-01DOI: 10.1109/ICCEREC.2018.8712104
Arum Sucia Saksesi, Muhammad Nasrun, C. Setianingsih
In today's social media, especially Twitter is very important for the success and destruction of one's image due to the many sentences of opinion that can compete the users. Examples of phrases that mean evil refer to hate speech to others. Evil perspectives can be categorized in hate speech, which hates speech is regulated in Article 28 of the ITE Law. Not a few people who intentionally and unintentionally oppose social media that contain hate speech. Unfortunately, social media does not have the ability to aggregate information about an existing conversation into a conclusion. One way to draw conclusions from aggregation results is to use text mining. In this paper to classify whether the text in the sentence contains elements of hate speech or not. The author hopes in this paper can make how to classify element of hate speech in the text by a computer, which later speech of hate can be recognized. By using Deep Learning method with Recurrent Neural Network (RNN) algorithm. After the creation of this program, it is hoped the computer can know and classify the existence of hate speech in the sentence. From the results of tests that have been done the average precision of 91%, recall 90% and accuracy 91%
{"title":"Analysis Text of Hate Speech Detection Using Recurrent Neural Network","authors":"Arum Sucia Saksesi, Muhammad Nasrun, C. Setianingsih","doi":"10.1109/ICCEREC.2018.8712104","DOIUrl":"https://doi.org/10.1109/ICCEREC.2018.8712104","url":null,"abstract":"In today's social media, especially Twitter is very important for the success and destruction of one's image due to the many sentences of opinion that can compete the users. Examples of phrases that mean evil refer to hate speech to others. Evil perspectives can be categorized in hate speech, which hates speech is regulated in Article 28 of the ITE Law. Not a few people who intentionally and unintentionally oppose social media that contain hate speech. Unfortunately, social media does not have the ability to aggregate information about an existing conversation into a conclusion. One way to draw conclusions from aggregation results is to use text mining. In this paper to classify whether the text in the sentence contains elements of hate speech or not. The author hopes in this paper can make how to classify element of hate speech in the text by a computer, which later speech of hate can be recognized. By using Deep Learning method with Recurrent Neural Network (RNN) algorithm. After the creation of this program, it is hoped the computer can know and classify the existence of hate speech in the sentence. From the results of tests that have been done the average precision of 91%, recall 90% and accuracy 91%","PeriodicalId":250054,"journal":{"name":"2018 International Conference on Control, Electronics, Renewable Energy and Communications (ICCEREC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128730096","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 : 2018-12-01DOI: 10.1109/iccerec.2018.8712088
William Kevin Siagian, Daniel Fernando Purba, Asrilani Sipahutar, Indra Hartarto Tambunan
Battery Management System is an electronic system that manages a rechargeable battery cell or battery pack. The main purpose of a BMS is protecting the battery from operating outside its safe operating area by monitoring its state, calculating secondary data, reporting the data, controlling its environment, authenticating or balancing it. In this research, the authors aim to create a battery management system prototype that will supervise batteries charging using solar panel. Parameters that are monitored by BMS are voltage, current, and temperature. The BMS also has an additional feature which is on-grid system. This feature allows the BMS to maximize the energy usage produced by solar panel. The BMS is created using microcontroller, sensors, and other components including Arduino Mega, temperature sensor, voltage sensor, current sensor, real time clock, relay, multiplexer, and dc fan. The BMS is applied to light street lamp everyday from 06.00 am to 06.00 pm. This BMS has three modes which are charging, discharging, and on-grid mode. Charging mode is when the BMS is used to monitor the battery charging process which last for 5 hours (10.00 am to 03.00 pm). Discharging mode is when the batteries is use to light up the street lamp. Ongrid mode is when the solar panel still able to produce energy beside charging and discharging time.
{"title":"Design and Implementation of Battery Management System for On-Grid System","authors":"William Kevin Siagian, Daniel Fernando Purba, Asrilani Sipahutar, Indra Hartarto Tambunan","doi":"10.1109/iccerec.2018.8712088","DOIUrl":"https://doi.org/10.1109/iccerec.2018.8712088","url":null,"abstract":"Battery Management System is an electronic system that manages a rechargeable battery cell or battery pack. The main purpose of a BMS is protecting the battery from operating outside its safe operating area by monitoring its state, calculating secondary data, reporting the data, controlling its environment, authenticating or balancing it. In this research, the authors aim to create a battery management system prototype that will supervise batteries charging using solar panel. Parameters that are monitored by BMS are voltage, current, and temperature. The BMS also has an additional feature which is on-grid system. This feature allows the BMS to maximize the energy usage produced by solar panel. The BMS is created using microcontroller, sensors, and other components including Arduino Mega, temperature sensor, voltage sensor, current sensor, real time clock, relay, multiplexer, and dc fan. The BMS is applied to light street lamp everyday from 06.00 am to 06.00 pm. This BMS has three modes which are charging, discharging, and on-grid mode. Charging mode is when the BMS is used to monitor the battery charging process which last for 5 hours (10.00 am to 03.00 pm). Discharging mode is when the batteries is use to light up the street lamp. Ongrid mode is when the solar panel still able to produce energy beside charging and discharging time.","PeriodicalId":250054,"journal":{"name":"2018 International Conference on Control, Electronics, Renewable Energy and Communications (ICCEREC)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125587554","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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