Pub Date : 2020-11-03DOI: 10.1109/ICIC50835.2020.9288628
Titus Kristanto, Walid Maulana Hadiansyah, M. Nasrullah
Higher education has an important role in educating the nation's life through education. The higher education management process must have a standardization that has been determined in accordance with Permenristekdikti Number 44 of 2015 concerning National Higher Education Standards. To achieve the quality of higher education performance, it is necessary to rank the accreditation of study programs and higher education according to Permenristekdikti Number 32 of 2016 concerning Accreditation of Study Programs and Higher Education. The purpose of this study was to analyze comprehensive higher education performance measurements. This research method uses the Academic Scorecard approach with the Analytical Hierarchy Process. The Academic Scorecard method used is a combination of Permenristekdikti Number 44 of 2015 in the form of determining from each cluster, while data processing uses the Analytical Hierarchy Process in the form of decision making with several criteria. The results of the research are the level of importance of Academic Management Perspective of 16.79%, Stakeholder Perspective 19.73%, Internal Business Process Perspective 57.45%, and Innovation and Learning Perspective 55.52%. Meanwhile, from the Academic Scorecard, the perspective attainment level reached 41.72%.
{"title":"Analysis of Higher Education Performance Measurement Using Academic Scorecard and Analytical Hierarchy Process","authors":"Titus Kristanto, Walid Maulana Hadiansyah, M. Nasrullah","doi":"10.1109/ICIC50835.2020.9288628","DOIUrl":"https://doi.org/10.1109/ICIC50835.2020.9288628","url":null,"abstract":"Higher education has an important role in educating the nation's life through education. The higher education management process must have a standardization that has been determined in accordance with Permenristekdikti Number 44 of 2015 concerning National Higher Education Standards. To achieve the quality of higher education performance, it is necessary to rank the accreditation of study programs and higher education according to Permenristekdikti Number 32 of 2016 concerning Accreditation of Study Programs and Higher Education. The purpose of this study was to analyze comprehensive higher education performance measurements. This research method uses the Academic Scorecard approach with the Analytical Hierarchy Process. The Academic Scorecard method used is a combination of Permenristekdikti Number 44 of 2015 in the form of determining from each cluster, while data processing uses the Analytical Hierarchy Process in the form of decision making with several criteria. The results of the research are the level of importance of Academic Management Perspective of 16.79%, Stakeholder Perspective 19.73%, Internal Business Process Perspective 57.45%, and Innovation and Learning Perspective 55.52%. Meanwhile, from the Academic Scorecard, the perspective attainment level reached 41.72%.","PeriodicalId":413610,"journal":{"name":"2020 Fifth International Conference on Informatics and Computing (ICIC)","volume":"51 Suppl 53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126902655","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 : 2020-11-03DOI: 10.1109/ICIC50835.2020.9288513
Rizki Alfarizi Harahap, Eri Prasetyo Wibowo, Robby Kurniawan Harahap
Frequent loss of time caused by finding a vacant parking space is an undesirable event by vehicle users. This paper discusses making simulation software that can provide information about available parking spots. The methods used include character recognition with the EAST text detector algorithm, vehicle detection with the Haar cascade classification algorithm, and Detection of vacant parking spots. This research presents a detector using feature text to detect vehicles in parking slots. The three methods are then combined into a simulation system. Python and OpenCV libraries are used as simulation tools in this research. The simulation runs using 60 seconds of video-stream, then observes the results every 10 seconds. The results obtained that the information can appear in the form of a text containing the available parking slots. This simulation system can facilitate the monitoring of parking areas, mainly for vacant parking slots, and make parking systems more efficient for parking management.
{"title":"Detection and Simulation of Vacant Parking Lot Space Using EAST Algorithm and Haar Cascade","authors":"Rizki Alfarizi Harahap, Eri Prasetyo Wibowo, Robby Kurniawan Harahap","doi":"10.1109/ICIC50835.2020.9288513","DOIUrl":"https://doi.org/10.1109/ICIC50835.2020.9288513","url":null,"abstract":"Frequent loss of time caused by finding a vacant parking space is an undesirable event by vehicle users. This paper discusses making simulation software that can provide information about available parking spots. The methods used include character recognition with the EAST text detector algorithm, vehicle detection with the Haar cascade classification algorithm, and Detection of vacant parking spots. This research presents a detector using feature text to detect vehicles in parking slots. The three methods are then combined into a simulation system. Python and OpenCV libraries are used as simulation tools in this research. The simulation runs using 60 seconds of video-stream, then observes the results every 10 seconds. The results obtained that the information can appear in the form of a text containing the available parking slots. This simulation system can facilitate the monitoring of parking areas, mainly for vacant parking slots, and make parking systems more efficient for parking management.","PeriodicalId":413610,"journal":{"name":"2020 Fifth International Conference on Informatics and Computing (ICIC)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128144796","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 : 2020-11-03DOI: 10.1109/ICIC50835.2020.9288568
Yudhi Dwi Fajar Maulana, Y. Ruldeviyani, D. Indra Sensuse
Family planning program implementation in Indonesia has a plethora of challenges. One of the biggest challenges to implement the family planning program in Indonesia is the huge percentage of contraceptive discontinuation rates for around 29% in 2019. Based on that problem, the data mining classification approach is proposed to produce a model that can predict the duration of contraceptive use by productive couples. Through Cross-Industry Standard Process for Data Mining (CRISP-DM) process, it tested four experiments to seven data mining techniques with 39.594 contraceptives used histories dataset which is sourced from the Demography and Health Survey of Indonesia (DHS) in 2017. The result shows that the Adaboost data mining technique produced the best performance of contraceptive used prediction model, with the accuracy score of the classification model as 85.1%, precision score as 85.1%, recall score as 85.2%, and F1 as 85.1%. The model produced in this study can be used to estimate the length/duration of a particular type of contraceptive method which is used by each productive couple. That information is useful to prevent discontinuation potencies among contraceptive users for a further period.
{"title":"Data Mining Classification Approach to Predict The Duration of Contraceptive Use","authors":"Yudhi Dwi Fajar Maulana, Y. Ruldeviyani, D. Indra Sensuse","doi":"10.1109/ICIC50835.2020.9288568","DOIUrl":"https://doi.org/10.1109/ICIC50835.2020.9288568","url":null,"abstract":"Family planning program implementation in Indonesia has a plethora of challenges. One of the biggest challenges to implement the family planning program in Indonesia is the huge percentage of contraceptive discontinuation rates for around 29% in 2019. Based on that problem, the data mining classification approach is proposed to produce a model that can predict the duration of contraceptive use by productive couples. Through Cross-Industry Standard Process for Data Mining (CRISP-DM) process, it tested four experiments to seven data mining techniques with 39.594 contraceptives used histories dataset which is sourced from the Demography and Health Survey of Indonesia (DHS) in 2017. The result shows that the Adaboost data mining technique produced the best performance of contraceptive used prediction model, with the accuracy score of the classification model as 85.1%, precision score as 85.1%, recall score as 85.2%, and F1 as 85.1%. The model produced in this study can be used to estimate the length/duration of a particular type of contraceptive method which is used by each productive couple. That information is useful to prevent discontinuation potencies among contraceptive users for a further period.","PeriodicalId":413610,"journal":{"name":"2020 Fifth International Conference on Informatics and Computing (ICIC)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125199386","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 : 2020-11-03DOI: 10.1109/ICIC50835.2020.9288654
S. Masruroh, Angga Zain Sauqy Perdana, Hendra Bayu Suseno, Andrew Fiade, D. Khairani, H. Sukmana
Mobile Ad-Hoc Network (MANET) has problems in terms of dynamic topology changes, limited energy consumption, and without the support of existing infrastructure. Also, another problem with MANET is the malicious node. A malicious node has a purpose to disrupt the operation of the routing protocol that is running on the network. Therefore, energy efficiency evaluation is needed at MANET. This research uses the AOMDV routing protocol. Data collection methods use literature studies and simulation methods using NS2, NAM, and AWK to evaluate the performance of the AOMDV routing protocol. Quality of Service (QoS) parameters used in this research are throughput, packet loss, jitter, and energy used to examine the energy efficiency used. The simulation is carried out using a malicious node, assuming the malicious node appears at different times. The results of this study that the value of throughput decreases, the value of packet loss increases, the value of unbalanced jitter, and the energy used is also increasing.
{"title":"Energy Efficient Routing Protocol AOMDV on MANET (Mobile Ad-Hoc Network) with Malicious Node","authors":"S. Masruroh, Angga Zain Sauqy Perdana, Hendra Bayu Suseno, Andrew Fiade, D. Khairani, H. Sukmana","doi":"10.1109/ICIC50835.2020.9288654","DOIUrl":"https://doi.org/10.1109/ICIC50835.2020.9288654","url":null,"abstract":"Mobile Ad-Hoc Network (MANET) has problems in terms of dynamic topology changes, limited energy consumption, and without the support of existing infrastructure. Also, another problem with MANET is the malicious node. A malicious node has a purpose to disrupt the operation of the routing protocol that is running on the network. Therefore, energy efficiency evaluation is needed at MANET. This research uses the AOMDV routing protocol. Data collection methods use literature studies and simulation methods using NS2, NAM, and AWK to evaluate the performance of the AOMDV routing protocol. Quality of Service (QoS) parameters used in this research are throughput, packet loss, jitter, and energy used to examine the energy efficiency used. The simulation is carried out using a malicious node, assuming the malicious node appears at different times. The results of this study that the value of throughput decreases, the value of packet loss increases, the value of unbalanced jitter, and the energy used is also increasing.","PeriodicalId":413610,"journal":{"name":"2020 Fifth International Conference on Informatics and Computing (ICIC)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125518799","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 : 2020-11-03DOI: 10.1109/ICIC50835.2020.9288528
Po Abas Sunarya, Henderi, Sulistiawati, Alfiah Khoirunisa, Pipit Nursaputri
The rapid development of technology has caused some systems to have changed; most of them in the Industrial Revolution 4.0 era using new methods from various aspects of people's lives. Family Deed Certificate is a family identity that contains data about arrangements, relationships, and the number of family members. A family certificate is an essential thing for every citizen to have. However, related problems that occur are still using conventional systems that cause problems such as loss of family deed, and various manipulations of identity data. Thus, from this problem emerged a solution to guarantee all data and information security using blockchain technology. Blockchain technology is a technology for recording transactions with modern technology, which can only be added but cannot be changed or replaced. Blockchain technology can support various fields such as banking, education, health, and priorities for governance. For this research, this is applied in the field of government, which is a blockchain technology family certificate, various problems in terms of a family certificate that is a copy of a lot of family member data, and editing a deed of change, is very inflexible. With the family certificate system, blockchain technology, data security can be guaranteed so that there is no data falsification and can replace any loss on the family deed. This system uses the literature method that contains and how blockchain works. The Certificate of Family Deed on the blockchain is expected to impact the digital world positively.
{"title":"Blockchain Family Deed Certificate for Privacy and Data Security","authors":"Po Abas Sunarya, Henderi, Sulistiawati, Alfiah Khoirunisa, Pipit Nursaputri","doi":"10.1109/ICIC50835.2020.9288528","DOIUrl":"https://doi.org/10.1109/ICIC50835.2020.9288528","url":null,"abstract":"The rapid development of technology has caused some systems to have changed; most of them in the Industrial Revolution 4.0 era using new methods from various aspects of people's lives. Family Deed Certificate is a family identity that contains data about arrangements, relationships, and the number of family members. A family certificate is an essential thing for every citizen to have. However, related problems that occur are still using conventional systems that cause problems such as loss of family deed, and various manipulations of identity data. Thus, from this problem emerged a solution to guarantee all data and information security using blockchain technology. Blockchain technology is a technology for recording transactions with modern technology, which can only be added but cannot be changed or replaced. Blockchain technology can support various fields such as banking, education, health, and priorities for governance. For this research, this is applied in the field of government, which is a blockchain technology family certificate, various problems in terms of a family certificate that is a copy of a lot of family member data, and editing a deed of change, is very inflexible. With the family certificate system, blockchain technology, data security can be guaranteed so that there is no data falsification and can replace any loss on the family deed. This system uses the literature method that contains and how blockchain works. The Certificate of Family Deed on the blockchain is expected to impact the digital world positively.","PeriodicalId":413610,"journal":{"name":"2020 Fifth International Conference on Informatics and Computing (ICIC)","volume":"91 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124651945","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 : 2020-11-03DOI: 10.1109/ICIC50835.2020.9288520
Triyanna Widiyaningtyas, I. Made Wirawan, Sabilla Halimatus Mahmud
Cats are one type of animal that is very popular with many people, and there is even a community of cat fans known as cat lovers. The health indicator in cats lies in the condition of their skin, so it needs special care to maintain their skin condition. Many cat owners are not aware of the skin diseases suffered by their cats. This is due to the owner's limited knowledge of the diseases experienced by cats and the difficulty in identifying the similar symptoms experienced by cats. To overcome this problem, we need a method to diagnose skin diseases that occur in cats. Diagnosis of symptoms of cat skin disease can be done by a classification method in data mining. In this study, the classification method used to diagnose skin diseases in cats is the C4.5 algorithm. The dataset used was obtained from the animal clinic “Purple Shop” in Malang. The algorithm testing process is carried out using k-fold cross-validation. Algorithm performance evaluation is measured by using a confusion matrix, namely by measuring the value of accuracy, precision, and recall. The results of this study indicate that the resulting accuracy value is 95.42%, the average precision is 96.93%, and the average recall is 97.19%. These results indicate that the C4.5 algorithm shows a very high level of performance and can be applied to diagnose symptoms of skin disease in cats.
{"title":"Diagnosis of Feline Skin Disease Using C4.5 Algorithm","authors":"Triyanna Widiyaningtyas, I. Made Wirawan, Sabilla Halimatus Mahmud","doi":"10.1109/ICIC50835.2020.9288520","DOIUrl":"https://doi.org/10.1109/ICIC50835.2020.9288520","url":null,"abstract":"Cats are one type of animal that is very popular with many people, and there is even a community of cat fans known as cat lovers. The health indicator in cats lies in the condition of their skin, so it needs special care to maintain their skin condition. Many cat owners are not aware of the skin diseases suffered by their cats. This is due to the owner's limited knowledge of the diseases experienced by cats and the difficulty in identifying the similar symptoms experienced by cats. To overcome this problem, we need a method to diagnose skin diseases that occur in cats. Diagnosis of symptoms of cat skin disease can be done by a classification method in data mining. In this study, the classification method used to diagnose skin diseases in cats is the C4.5 algorithm. The dataset used was obtained from the animal clinic “Purple Shop” in Malang. The algorithm testing process is carried out using k-fold cross-validation. Algorithm performance evaluation is measured by using a confusion matrix, namely by measuring the value of accuracy, precision, and recall. The results of this study indicate that the resulting accuracy value is 95.42%, the average precision is 96.93%, and the average recall is 97.19%. These results indicate that the C4.5 algorithm shows a very high level of performance and can be applied to diagnose symptoms of skin disease in cats.","PeriodicalId":413610,"journal":{"name":"2020 Fifth International Conference on Informatics and Computing (ICIC)","volume":"106 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123308567","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 : 2020-11-03DOI: 10.1109/ICIC50835.2020.9288615
Mustika Ulina, Ronsen Purba, Arwin Halim
In Foreign Exchange (Forex) Prediction with high accuracy it becomes a challenge because time series data has chaotic characteristics, uncertainty, and complexity. To improve the accuracy of the forex prices prediction, prediction models are proposed which Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) and Improved Firefly Algorithm-Long Short Term Memory (IFA-LSTM). In this model the preprocessing data using the CEEMDAN to decomposed into IMF sequence and residual sequence. LSTM prediction models are established for all each characteristic series from CEEMDAN deposition. IFA is applied to optimize neural network structure to improve the performance of the model prediction accuracy. We compare our proposed models with LSTM and CEEMDAN-LSTM models, the experimental results show that the proposed models performs better in the prediction of forex time series.
{"title":"Foreign Exchange Prediction using CEEMDAN and Improved FA-LSTM","authors":"Mustika Ulina, Ronsen Purba, Arwin Halim","doi":"10.1109/ICIC50835.2020.9288615","DOIUrl":"https://doi.org/10.1109/ICIC50835.2020.9288615","url":null,"abstract":"In Foreign Exchange (Forex) Prediction with high accuracy it becomes a challenge because time series data has chaotic characteristics, uncertainty, and complexity. To improve the accuracy of the forex prices prediction, prediction models are proposed which Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) and Improved Firefly Algorithm-Long Short Term Memory (IFA-LSTM). In this model the preprocessing data using the CEEMDAN to decomposed into IMF sequence and residual sequence. LSTM prediction models are established for all each characteristic series from CEEMDAN deposition. IFA is applied to optimize neural network structure to improve the performance of the model prediction accuracy. We compare our proposed models with LSTM and CEEMDAN-LSTM models, the experimental results show that the proposed models performs better in the prediction of forex time series.","PeriodicalId":413610,"journal":{"name":"2020 Fifth International Conference on Informatics and Computing (ICIC)","volume":"23 3 Suppl 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130295517","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 : 2020-11-03DOI: 10.1109/ICIC50835.2020.9288532
Dimas Sony Dewantara, I. Budi
Propaganda is a way of disseminating information, regardless of whether the information is true or not. Propaganda usually uses bias in obscuring the understanding of the propaganda targets. News articles are one of the media that is often used in spreading propaganda. Text classification in the form of propaganda detection in news articles is a crucial thing to do in relation to preventing the spread of the propaganda. Long Short-Term Memory (LSTM) is a variant of the Recurrent Neural Network (RNN) which has been widely used in text classification. However, LSTM has a weakness in the form of a tendency to high bias in extracting context from information through word order. Convolutional Neural Network (CNN) in text analysis can perform important feature extraction through the use of convolutional layers but is weak when assigned to context extraction. This research tries to compare LSTM, CNN and the combination of the two methods in text classification in the form of propaganda detection in news articles. The combination of each method is proved to improve classification performance and also shorten the required running time.
{"title":"Combination of LSTM and CNN for Article-Level Propaganda Detection in News Articles","authors":"Dimas Sony Dewantara, I. Budi","doi":"10.1109/ICIC50835.2020.9288532","DOIUrl":"https://doi.org/10.1109/ICIC50835.2020.9288532","url":null,"abstract":"Propaganda is a way of disseminating information, regardless of whether the information is true or not. Propaganda usually uses bias in obscuring the understanding of the propaganda targets. News articles are one of the media that is often used in spreading propaganda. Text classification in the form of propaganda detection in news articles is a crucial thing to do in relation to preventing the spread of the propaganda. Long Short-Term Memory (LSTM) is a variant of the Recurrent Neural Network (RNN) which has been widely used in text classification. However, LSTM has a weakness in the form of a tendency to high bias in extracting context from information through word order. Convolutional Neural Network (CNN) in text analysis can perform important feature extraction through the use of convolutional layers but is weak when assigned to context extraction. This research tries to compare LSTM, CNN and the combination of the two methods in text classification in the form of propaganda detection in news articles. The combination of each method is proved to improve classification performance and also shorten the required running time.","PeriodicalId":413610,"journal":{"name":"2020 Fifth International Conference on Informatics and Computing (ICIC)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125712974","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 : 2020-11-03DOI: 10.1109/ICIC50835.2020.9288594
N. Idris, Aspian Achban, Siti Andini Utiarahman, Jorry Karim, F. Pontoiyo
The automotive industry is increasingly competitive every year by releasing cars featured with innovative specifications offered by automotive manufacturing companies. The specifications, supported by the technology and performance a car has, are a tool to determine a car's price. However, today the automotive industry frequently releases a new product or type of car with the latest specifications, affecting a car's price to change. It perplexes car manufacturing companies when they are determining a car's price. Responding to this issue, an approach to a decision-making strategy to predict a car's price is needed. One of the approaches that can be implemented is business intelligence with its primary aspects i.e. descriptive, predictive, and prescriptive. Using the concept, we implement Business Intelligence and use the feed-forward backpropagation algorithm to predicts the selling price of a car based on its specification and predict a car price based on the latest specification which has never been on sale. The research findings, identified by using a dataset containing the specifications of BMW, reveal that the actual price and predicted price are close at a mean error of 11.46%. Besides, the research findings also state that the predicted price of a new car with new specifications is $55,754. This research aims to analyze the estimation of the price of a car with the latest specification, which is the focus of the implementation of the business intelligence method we do.
{"title":"Predicting the Selling Price of Cars Using Business Intelligence with the Feed-forward Backpropagation Algorithms","authors":"N. Idris, Aspian Achban, Siti Andini Utiarahman, Jorry Karim, F. Pontoiyo","doi":"10.1109/ICIC50835.2020.9288594","DOIUrl":"https://doi.org/10.1109/ICIC50835.2020.9288594","url":null,"abstract":"The automotive industry is increasingly competitive every year by releasing cars featured with innovative specifications offered by automotive manufacturing companies. The specifications, supported by the technology and performance a car has, are a tool to determine a car's price. However, today the automotive industry frequently releases a new product or type of car with the latest specifications, affecting a car's price to change. It perplexes car manufacturing companies when they are determining a car's price. Responding to this issue, an approach to a decision-making strategy to predict a car's price is needed. One of the approaches that can be implemented is business intelligence with its primary aspects i.e. descriptive, predictive, and prescriptive. Using the concept, we implement Business Intelligence and use the feed-forward backpropagation algorithm to predicts the selling price of a car based on its specification and predict a car price based on the latest specification which has never been on sale. The research findings, identified by using a dataset containing the specifications of BMW, reveal that the actual price and predicted price are close at a mean error of 11.46%. Besides, the research findings also state that the predicted price of a new car with new specifications is $55,754. This research aims to analyze the estimation of the price of a car with the latest specification, which is the focus of the implementation of the business intelligence method we do.","PeriodicalId":413610,"journal":{"name":"2020 Fifth International Conference on Informatics and Computing (ICIC)","volume":"755 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132868546","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 : 2020-11-03DOI: 10.1109/ICIC50835.2020.9288586
Herlawati Herlawati, E. Abdurachman, Y. Heryadi, Haryono Soeparno
Extended urbanization phenomenon in smaller-sized cities in Java should be considered by the government. However, the local governments can only use the plans from the central government. The negative effects of this, among others, are improper land use allocation, lack of facilities, and crimes that are difficult to handle. This study proposes a geographic information system (GIS)-based method to analyze the proper central business district in Karawang, a small district in Java, Indonesia. Multi-criteria analysis of factors affecting the candidate locations was used through a weighted sum method in a model. The spatial data were retrieved and analyzed using a GIS tool to classify region into urban, peri-urban, and rural. Some central business locations have been found after reclassification which is located near the toll gates. Two locations in the north of Karawang were classified have the potential to become the new central business locations.
{"title":"GIS-Based MCDM for Central Business Suitability in a Small City","authors":"Herlawati Herlawati, E. Abdurachman, Y. Heryadi, Haryono Soeparno","doi":"10.1109/ICIC50835.2020.9288586","DOIUrl":"https://doi.org/10.1109/ICIC50835.2020.9288586","url":null,"abstract":"Extended urbanization phenomenon in smaller-sized cities in Java should be considered by the government. However, the local governments can only use the plans from the central government. The negative effects of this, among others, are improper land use allocation, lack of facilities, and crimes that are difficult to handle. This study proposes a geographic information system (GIS)-based method to analyze the proper central business district in Karawang, a small district in Java, Indonesia. Multi-criteria analysis of factors affecting the candidate locations was used through a weighted sum method in a model. The spatial data were retrieved and analyzed using a GIS tool to classify region into urban, peri-urban, and rural. Some central business locations have been found after reclassification which is located near the toll gates. Two locations in the north of Karawang were classified have the potential to become the new central business locations.","PeriodicalId":413610,"journal":{"name":"2020 Fifth International Conference on Informatics and Computing (ICIC)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132885009","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：