Pub Date : 2018-11-01DOI: 10.1109/icitisee.2018.8721004
{"title":"ICITISEE 2018 Author Index","authors":"","doi":"10.1109/icitisee.2018.8721004","DOIUrl":"https://doi.org/10.1109/icitisee.2018.8721004","url":null,"abstract":"","PeriodicalId":180051,"journal":{"name":"2018 3rd International Conference on Information Technology, Information System and Electrical Engineering (ICITISEE)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116546042","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 : 2018-11-01DOI: 10.1109/ICITISEE.2018.8721025
Zendra Mawan Leksana, Suhariyanto, F. D. Wijaya
A military radar unit is an operating unit that has the duty and responsibility to maintain the sovereignty of national airspace throughout the year. Considering that the installation area of military radar installations is in the mountainous, coastal, and marine areas, of course, this makes its own vulnerability to radar systems and their supporting electronic equipment from the threat of lightning strikes. The purpose of this study was to analyze the external protection system on radar towers based on the placement of installed air terminations. The analysis was carried out by applying the protection angle, rolling sphere, and collection volume method. From the results of research using the protection angle, it is found that using 1 air termination, increasing the air termination height to 38 m from the ground surface can protect all tower buildings and radar antenna from direct lightning strikes. The application of the rolling sphere on 4 air terminations on the radar tower is able to protect all tower buildings and radar antenna with a protection radius of each air termination as far as 45 m. By increasing the antenna height to 38 m from the ground surface, an analysis of the external lightning protection system using collection volume can protect all tower buildings and radar antenna from direct lightning strikes.
{"title":"Evaluation of Implementation of External Lightning Protection System: Case Study on the Military Radar Tower","authors":"Zendra Mawan Leksana, Suhariyanto, F. D. Wijaya","doi":"10.1109/ICITISEE.2018.8721025","DOIUrl":"https://doi.org/10.1109/ICITISEE.2018.8721025","url":null,"abstract":"A military radar unit is an operating unit that has the duty and responsibility to maintain the sovereignty of national airspace throughout the year. Considering that the installation area of military radar installations is in the mountainous, coastal, and marine areas, of course, this makes its own vulnerability to radar systems and their supporting electronic equipment from the threat of lightning strikes. The purpose of this study was to analyze the external protection system on radar towers based on the placement of installed air terminations. The analysis was carried out by applying the protection angle, rolling sphere, and collection volume method. From the results of research using the protection angle, it is found that using 1 air termination, increasing the air termination height to 38 m from the ground surface can protect all tower buildings and radar antenna from direct lightning strikes. The application of the rolling sphere on 4 air terminations on the radar tower is able to protect all tower buildings and radar antenna with a protection radius of each air termination as far as 45 m. By increasing the antenna height to 38 m from the ground surface, an analysis of the external lightning protection system using collection volume can protect all tower buildings and radar antenna from direct lightning strikes.","PeriodicalId":180051,"journal":{"name":"2018 3rd International Conference on Information Technology, Information System and Electrical Engineering (ICITISEE)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122372074","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 : 2018-11-01DOI: 10.1109/ICITISEE.2018.8720975
R. Wahyudi, Hendra Marcos, U. Hasanah, Bambang Pilu Hartato, Tri Astuti, Rizal Anjas Prasetyo
Phishing websites are a fooling technique by making victims as if they are accessing legitimate sites. Data mining is a technique for extracting hidden information in order to benefit more from existing data. Data mining is the process of discovering regularity, patterns, and relationships in large datasets. In this study, data mining will be used to determine the effect of feature selection on algorithm C4.5 and CART on phishing website dataset. From the tests that have been done the effect of feature selection on the phishing website, dataset proved to overcome the longer computational time. From the performance measurement of both algorithms that have been done, CART algorithm has a higher accuracy value than the algorithm C4.5 with an accuracy of 94.4%, while the algorithm C4.5 has an accuracy of 94.3%, so it can be concluded that CART algorithm has better performance value compared with the C4.5 algorithm.
{"title":"Algorithm Evaluation for Classification “Phishing Website” Using Several Classification Algorithms","authors":"R. Wahyudi, Hendra Marcos, U. Hasanah, Bambang Pilu Hartato, Tri Astuti, Rizal Anjas Prasetyo","doi":"10.1109/ICITISEE.2018.8720975","DOIUrl":"https://doi.org/10.1109/ICITISEE.2018.8720975","url":null,"abstract":"Phishing websites are a fooling technique by making victims as if they are accessing legitimate sites. Data mining is a technique for extracting hidden information in order to benefit more from existing data. Data mining is the process of discovering regularity, patterns, and relationships in large datasets. In this study, data mining will be used to determine the effect of feature selection on algorithm C4.5 and CART on phishing website dataset. From the tests that have been done the effect of feature selection on the phishing website, dataset proved to overcome the longer computational time. From the performance measurement of both algorithms that have been done, CART algorithm has a higher accuracy value than the algorithm C4.5 with an accuracy of 94.4%, while the algorithm C4.5 has an accuracy of 94.3%, so it can be concluded that CART algorithm has better performance value compared with the C4.5 algorithm.","PeriodicalId":180051,"journal":{"name":"2018 3rd International Conference on Information Technology, Information System and Electrical Engineering (ICITISEE)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121918339","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 : 2018-11-01DOI: 10.1109/ICITISEE.2018.8720968
D. Permatasari, D. Maharani
This paper presents a method for solving tuning PID Pan-Tilt Face Tracking. PID conventional method is developed to self-tuning gain of PID using Backpropagation Neural Network (BPNN) during the process (online) then achieves the desired target of human face which has more robust and minimal error. This plant uses three input neuros (references input), five hidden neuros, and three output neuros (Kp, Ki, and Kd). For initialization learning rate (alpha) and momentum (gamma) using 0.1 and 0.3 with random initialization weight. The pan system result has a fast response with overshoot 0.68%, peak time 0.65s, and rise time 0.48s with Kp = 2.9416, Ki = 0.393, Kd = 8.647 and for tilt system with overshoot 1.59%, rise time 0.49 s, and peak time 0.7 s. PID controller by Backpropagation Neural Network, it is obtained better reference output results with faster and fewer responses overshoot.
{"title":"Backpropagation Neural Network for Tuning PID Pan-Tilt Face Tracking","authors":"D. Permatasari, D. Maharani","doi":"10.1109/ICITISEE.2018.8720968","DOIUrl":"https://doi.org/10.1109/ICITISEE.2018.8720968","url":null,"abstract":"This paper presents a method for solving tuning PID Pan-Tilt Face Tracking. PID conventional method is developed to self-tuning gain of PID using Backpropagation Neural Network (BPNN) during the process (online) then achieves the desired target of human face which has more robust and minimal error. This plant uses three input neuros (references input), five hidden neuros, and three output neuros (Kp, Ki, and Kd). For initialization learning rate (alpha) and momentum (gamma) using 0.1 and 0.3 with random initialization weight. The pan system result has a fast response with overshoot 0.68%, peak time 0.65s, and rise time 0.48s with Kp = 2.9416, Ki = 0.393, Kd = 8.647 and for tilt system with overshoot 1.59%, rise time 0.49 s, and peak time 0.7 s. PID controller by Backpropagation Neural Network, it is obtained better reference output results with faster and fewer responses overshoot.","PeriodicalId":180051,"journal":{"name":"2018 3rd International Conference on Information Technology, Information System and Electrical Engineering (ICITISEE)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122104296","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 : 2018-11-01DOI: 10.1109/ICITISEE.2018.8720947
I. Imelda, A. Harjoko, P. Nurwantoro
Traffic information is needed to determine the cause of the accident. Problems arise when many traffic accidents or violations co-occur. Technical failures in delivering important frames also hinder the process of analyzing the video, which occurs due to disconnected network, limited bandwidth and CPU processing power. Besides, the size of the video to be processed at the same time slow the CPU down preventing the video from being treated. In this research, we propose Smart Video Sensor (SVS) resolve the missing frame issues. SVS is a video sensor recording images streaming frames for the frame. SVS extract only features of traffic objects and compress the video so that the data will be received faster and lighter. SVS also processes the primary data, so the other system is ready to use the features needed for further data processing. To demonstrate how well SVS works, we experimented it by tracking vehicles by type. This study uses 3 locations and 1000 frames in each area. The contribution of this paper is to produce a vehicle tracking model by type using Kalman Filter based SVS Architecture. The highest accuracy found for motorcycles is in Galeria (90.71%).
{"title":"Vehicle Tracking using Kalman Filter based on Smart Video Sensor Architecture","authors":"I. Imelda, A. Harjoko, P. Nurwantoro","doi":"10.1109/ICITISEE.2018.8720947","DOIUrl":"https://doi.org/10.1109/ICITISEE.2018.8720947","url":null,"abstract":"Traffic information is needed to determine the cause of the accident. Problems arise when many traffic accidents or violations co-occur. Technical failures in delivering important frames also hinder the process of analyzing the video, which occurs due to disconnected network, limited bandwidth and CPU processing power. Besides, the size of the video to be processed at the same time slow the CPU down preventing the video from being treated. In this research, we propose Smart Video Sensor (SVS) resolve the missing frame issues. SVS is a video sensor recording images streaming frames for the frame. SVS extract only features of traffic objects and compress the video so that the data will be received faster and lighter. SVS also processes the primary data, so the other system is ready to use the features needed for further data processing. To demonstrate how well SVS works, we experimented it by tracking vehicles by type. This study uses 3 locations and 1000 frames in each area. The contribution of this paper is to produce a vehicle tracking model by type using Kalman Filter based SVS Architecture. The highest accuracy found for motorcycles is in Galeria (90.71%).","PeriodicalId":180051,"journal":{"name":"2018 3rd International Conference on Information Technology, Information System and Electrical Engineering (ICITISEE)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134028584","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 : 2018-11-01DOI: 10.1109/icitisee.2018.8721002
Delpiah Wahyuningsih, H. Pradana, Hamidah
Currently, oil fuels are a primary need for the community for motorized, such as vehicles. Fuels in Indonesia, which is held by PT. Pertamina, many companies which are covered by Pertamina to diesel oil fuels. The demand for diesel fuels are very large. That is, from several entrepreneurs’ such as palm oil, supermarket, and others level of stores. The companies sometimes cannot meet the customer needs. So, for the ordering the diesel fuel is limited, especially oil orders that make by branch companies who had a limited oil distribution stock. Here, we need a system that can analyze the needs of diesel oil distribution for companies or their branch using the Distribution Requirement Planning (DRP) method. DRP is an application to determine the calculation of the need for distribution of diesel oil each period (especially for one year). Companies and their branches benefit can get from DRP application where DRP itself makes predictions or measurements for the needs of diesel fuel for one period, namely the distribution of diesel oil over the next year, the need for distribution of diesel oil every month can be seen in detail. DRP will show the calculation of Planned Order Receipt and Planned Orders Release (POR), Gross Requirements (GR), Projected on Hand (PoH), and Net Requirements (NR). So, that they do not experience delays in the request process from customers.
{"title":"Prediction of The Needs of Industrial Oil Fuels with The Implementation of Distribution Requirement Planning (DRP)","authors":"Delpiah Wahyuningsih, H. Pradana, Hamidah","doi":"10.1109/icitisee.2018.8721002","DOIUrl":"https://doi.org/10.1109/icitisee.2018.8721002","url":null,"abstract":"Currently, oil fuels are a primary need for the community for motorized, such as vehicles. Fuels in Indonesia, which is held by PT. Pertamina, many companies which are covered by Pertamina to diesel oil fuels. The demand for diesel fuels are very large. That is, from several entrepreneurs’ such as palm oil, supermarket, and others level of stores. The companies sometimes cannot meet the customer needs. So, for the ordering the diesel fuel is limited, especially oil orders that make by branch companies who had a limited oil distribution stock. Here, we need a system that can analyze the needs of diesel oil distribution for companies or their branch using the Distribution Requirement Planning (DRP) method. DRP is an application to determine the calculation of the need for distribution of diesel oil each period (especially for one year). Companies and their branches benefit can get from DRP application where DRP itself makes predictions or measurements for the needs of diesel fuel for one period, namely the distribution of diesel oil over the next year, the need for distribution of diesel oil every month can be seen in detail. DRP will show the calculation of Planned Order Receipt and Planned Orders Release (POR), Gross Requirements (GR), Projected on Hand (PoH), and Net Requirements (NR). So, that they do not experience delays in the request process from customers.","PeriodicalId":180051,"journal":{"name":"2018 3rd International Conference on Information Technology, Information System and Electrical Engineering (ICITISEE)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131108982","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 : 2018-11-01DOI: 10.1109/ICITISEE.2018.8720985
Chyntia Raras Ajeng Widiawati, Kuat Indartono
Signatures are one of the important characteristics that security needs to be considered. Some cases related to signature forgery often occur, this is certainly dangerous especially if the signature forgery can be misused. So there needs to be a verification process on the authenticity of signatures related to this. Several studies related to signature verification have been carried out, one of them using digital image processing techniques. However, some studies only propose a method without comparison of results. This study aims to compare methods and development of signature verification methods based on digital image processing with machine learning techniques. The final results of this research can later be used as a design module that can be used in system development or signature verification applications. The data used is the image of the digitization of the signature of the Lecturer in the STMIK AMIKOM Purwokerto environment. The segmentation method used in this study is adaptive maximum minimum thresholding with double morphological operation. Good segmentation results are expected to provide good classification results. Comparison of several different classifiers in the classification stage is carried out, including Linear Regression, Naïve Bayes (NB), Support Vector Machine (SVM), Multilayer Perceptron (MLP) and K-Nearest Neighbor (K-NN).
{"title":"Double Morphological Segmentation for Increasing Performance of Signature Classification Using Machine Learning Technique","authors":"Chyntia Raras Ajeng Widiawati, Kuat Indartono","doi":"10.1109/ICITISEE.2018.8720985","DOIUrl":"https://doi.org/10.1109/ICITISEE.2018.8720985","url":null,"abstract":"Signatures are one of the important characteristics that security needs to be considered. Some cases related to signature forgery often occur, this is certainly dangerous especially if the signature forgery can be misused. So there needs to be a verification process on the authenticity of signatures related to this. Several studies related to signature verification have been carried out, one of them using digital image processing techniques. However, some studies only propose a method without comparison of results. This study aims to compare methods and development of signature verification methods based on digital image processing with machine learning techniques. The final results of this research can later be used as a design module that can be used in system development or signature verification applications. The data used is the image of the digitization of the signature of the Lecturer in the STMIK AMIKOM Purwokerto environment. The segmentation method used in this study is adaptive maximum minimum thresholding with double morphological operation. Good segmentation results are expected to provide good classification results. Comparison of several different classifiers in the classification stage is carried out, including Linear Regression, Naïve Bayes (NB), Support Vector Machine (SVM), Multilayer Perceptron (MLP) and K-Nearest Neighbor (K-NN).","PeriodicalId":180051,"journal":{"name":"2018 3rd International Conference on Information Technology, Information System and Electrical Engineering (ICITISEE)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123573700","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 : 2018-11-01DOI: 10.1109/ICITISEE.2018.8720980
Irma Safitri, Ratri Dwi Atmaja, Azharudin Hidayat
In this study, we propose Huffman coding and compressive sensing (CS) for medical image watermarking. The methods used are Huffman coding, CS, discrete wavelet transform (DWT) and singular value decomposition (SVD). Experiment results show that images can be compressed generally above 50% and are lossless at the time of decompression by having the SSIM value of 1. Our system have the best MSE value of 0.172686 and the best PSNR value of 55.7582 dB.
{"title":"Wavelet Huffman Coding Image Watermarking in the Presence of Compressive Sensing","authors":"Irma Safitri, Ratri Dwi Atmaja, Azharudin Hidayat","doi":"10.1109/ICITISEE.2018.8720980","DOIUrl":"https://doi.org/10.1109/ICITISEE.2018.8720980","url":null,"abstract":"In this study, we propose Huffman coding and compressive sensing (CS) for medical image watermarking. The methods used are Huffman coding, CS, discrete wavelet transform (DWT) and singular value decomposition (SVD). Experiment results show that images can be compressed generally above 50% and are lossless at the time of decompression by having the SSIM value of 1. Our system have the best MSE value of 0.172686 and the best PSNR value of 55.7582 dB.","PeriodicalId":180051,"journal":{"name":"2018 3rd International Conference on Information Technology, Information System and Electrical Engineering (ICITISEE)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121938814","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 : 2018-11-01DOI: 10.1109/ICITISEE.2018.8720977
Ade Nurhopipah, B. Kusuma
The theory of Multiple Intelligences has been widely applied in exchange of intelligence test approach with the single score (IQ). One of the applications of MI-based learning strategies is to group students based on Multiple Intelligence Research (MIR) scores. In this study, students are grouped based on MIR scores using multilevel clustering techniques. Multiple clustering is applied to meet the needs of the equal number of students and gender. Several models of multilevel clustering using Self-Organizing Map (SOM) and K-Means algorithms are carried out. The evaluation results show that the smallest error is generated by the multilevel SOM. This method can facilitate students grouping based on MIR scores by maintaining the similarity of student features and class heterogeneity. This clustering method is expected to be an efficient way to group students automatically and effectively according to MI-based learning strategies.
{"title":"Multilevel Clustering Comparison using Self-Organizing Map and K-Means for MIR Score Clustering","authors":"Ade Nurhopipah, B. Kusuma","doi":"10.1109/ICITISEE.2018.8720977","DOIUrl":"https://doi.org/10.1109/ICITISEE.2018.8720977","url":null,"abstract":"The theory of Multiple Intelligences has been widely applied in exchange of intelligence test approach with the single score (IQ). One of the applications of MI-based learning strategies is to group students based on Multiple Intelligence Research (MIR) scores. In this study, students are grouped based on MIR scores using multilevel clustering techniques. Multiple clustering is applied to meet the needs of the equal number of students and gender. Several models of multilevel clustering using Self-Organizing Map (SOM) and K-Means algorithms are carried out. The evaluation results show that the smallest error is generated by the multilevel SOM. This method can facilitate students grouping based on MIR scores by maintaining the similarity of student features and class heterogeneity. This clustering method is expected to be an efficient way to group students automatically and effectively according to MI-based learning strategies.","PeriodicalId":180051,"journal":{"name":"2018 3rd International Conference on Information Technology, Information System and Electrical Engineering (ICITISEE)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125867714","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 : 2018-11-01DOI: 10.1109/icitisee.2018.8721008
Ahmad Chusyairi, Ramadar N.S. Pelsri, Estu Handayani
Exponential Smoothing methods are proposed in this research to predict the number of loss reports in the E-Report contained on “One-Click Service Police Resort” for Banyuwangi society. The best prediction is obtained based on smallest value of the Mean Absolut Deviation (MAD), the Mean Square Error (MSE), and the Mean Absolute Percentage Error (MAPE) to select an appropriate forecasting model using Single ES (Exponential Smoothing), Double ES, and Triple ES. However, the determination of α, β and γ parameter is still manual. Genetic Algorithm method is used to set the values optimally to overcome these problems. The result from this experience show that the Single ES is determined as the best prediction method as a result of the prediction of loss report on E-Report Police Resort based on the alpha value obtained from the genetic algorithm method.
{"title":"Optimization of Exponential Smoothing Method Using Genetic Algorithm to Predict E-Report Service","authors":"Ahmad Chusyairi, Ramadar N.S. Pelsri, Estu Handayani","doi":"10.1109/icitisee.2018.8721008","DOIUrl":"https://doi.org/10.1109/icitisee.2018.8721008","url":null,"abstract":"Exponential Smoothing methods are proposed in this research to predict the number of loss reports in the E-Report contained on “One-Click Service Police Resort” for Banyuwangi society. The best prediction is obtained based on smallest value of the Mean Absolut Deviation (MAD), the Mean Square Error (MSE), and the Mean Absolute Percentage Error (MAPE) to select an appropriate forecasting model using Single ES (Exponential Smoothing), Double ES, and Triple ES. However, the determination of α, β and γ parameter is still manual. Genetic Algorithm method is used to set the values optimally to overcome these problems. The result from this experience show that the Single ES is determined as the best prediction method as a result of the prediction of loss report on E-Report Police Resort based on the alpha value obtained from the genetic algorithm method.","PeriodicalId":180051,"journal":{"name":"2018 3rd International Conference on Information Technology, Information System and Electrical Engineering (ICITISEE)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129525068","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：