Pub Date : 2017-11-01DOI: 10.1109/ICITISEE.2017.8285555
Sharifa Rania Mahmud, J. Maowa, F. W. Wibowo
Today world population is seven billion plus, and it will be nine billion by 2050. More than 53% are women who are experiencing diversified situation from born to death. Being women they faces challenges in treatment choice and hence women are neglected and isolated from performing social responsibility for the sake of so called vulnerable women health. In many developed countries woman's health, education, nutrition and economic power have indicated that women are still inferior to men. Women live in rural areas, are responsible for most of the domestic work without economical impact analysis that is done in rural areas. Women in cities can't advance further in manufacturing job. In this paper we discuss about violence against women (VAW) and also different health issues of women. We have designed and present a skeleton of a user friendly mobile application named Women Empowerment which will contain different laws related to VAW and also contains different health tips for women, which will help the rural as well as urban women. It includes emergency call system, which will be active by the victim women when they are in danger.
{"title":"Women empowerment: One stop solution for women","authors":"Sharifa Rania Mahmud, J. Maowa, F. W. Wibowo","doi":"10.1109/ICITISEE.2017.8285555","DOIUrl":"https://doi.org/10.1109/ICITISEE.2017.8285555","url":null,"abstract":"Today world population is seven billion plus, and it will be nine billion by 2050. More than 53% are women who are experiencing diversified situation from born to death. Being women they faces challenges in treatment choice and hence women are neglected and isolated from performing social responsibility for the sake of so called vulnerable women health. In many developed countries woman's health, education, nutrition and economic power have indicated that women are still inferior to men. Women live in rural areas, are responsible for most of the domestic work without economical impact analysis that is done in rural areas. Women in cities can't advance further in manufacturing job. In this paper we discuss about violence against women (VAW) and also different health issues of women. We have designed and present a skeleton of a user friendly mobile application named Women Empowerment which will contain different laws related to VAW and also contains different health tips for women, which will help the rural as well as urban women. It includes emergency call system, which will be active by the victim women when they are in danger.","PeriodicalId":130873,"journal":{"name":"2017 2nd International conferences on Information Technology, Information Systems and Electrical Engineering (ICITISEE)","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122571974","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 : 2017-11-01DOI: 10.1109/ICITISEE.2017.8285558
C. H. Primasari, D. Setyohadi
More than 90% of goat farm business done by farmers in rural areas in Indonesia are small-scale farm business. Mostly small-scale farms raise goats as its main commodity. To build a goat farm, farmer has to choose the type of goats that have the potential benefit. The aim of this study is to select the most profitable investment proposal of goat farming. To understand the investment profit, this research used several financial analysis methods like NPV (Net Present Value), ROI (Return On Investment), BCR (Benefit Cost Ratio), BEP (Break Event Point), and PBP (Payback Period). The results of the financial analysis will be ranked by TOPSIS to obtain the most profitable investment proposal.
{"title":"Financial analysis and TOPSIS implementation for selecting the most profitable investment proposal in goat farming","authors":"C. H. Primasari, D. Setyohadi","doi":"10.1109/ICITISEE.2017.8285558","DOIUrl":"https://doi.org/10.1109/ICITISEE.2017.8285558","url":null,"abstract":"More than 90% of goat farm business done by farmers in rural areas in Indonesia are small-scale farm business. Mostly small-scale farms raise goats as its main commodity. To build a goat farm, farmer has to choose the type of goats that have the potential benefit. The aim of this study is to select the most profitable investment proposal of goat farming. To understand the investment profit, this research used several financial analysis methods like NPV (Net Present Value), ROI (Return On Investment), BCR (Benefit Cost Ratio), BEP (Break Event Point), and PBP (Payback Period). The results of the financial analysis will be ranked by TOPSIS to obtain the most profitable investment proposal.","PeriodicalId":130873,"journal":{"name":"2017 2nd International conferences on Information Technology, Information Systems and Electrical Engineering (ICITISEE)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117151233","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 : 2017-11-01DOI: 10.1109/ICITISEE.2017.8285486
B. Kusuma
One of the disease that require X-ray diagnosis is scoliosis. Early detection of scoliosis is important to do for anyone. From the early detection information, the doctor may take the firts step to further treatment quickly. Determination of spinal curvature is a first step method that used to measure how severe the degree of scoliosis. The severity degree of scoliosis can be assess by using Cobb angle. Therefore, by approximate the spinal curvature, we can approximate the cobb angle too. From previous work that interobserver measurement value may reach 11.8° and intraobserver measurement error is 6°. So, as far as the cobb angle measuring, the subjectivity aspect is the natural thing and can be tolerated until now. This research propose an algorithm how to define spinal curvature with the aid of a computer in digital X-ray image quickly but has a standard error that can be tolerated. The preprocessing has been done by canny edge detection. The k-means clustering algorithm can detect the centroid point after segmentation preprocessing of the spinal segment and polynomial curve fitting will be used in the process for determining the spinal curve. From the spinal curvature information, the scoliosis curve can be classified into 4 condition, normal, mild, moderate, and severe scoliosis.
{"title":"Determination of spinal curvature from scoliosis X-ray images using K-means and curve fitting for early detection of scoliosis disease","authors":"B. Kusuma","doi":"10.1109/ICITISEE.2017.8285486","DOIUrl":"https://doi.org/10.1109/ICITISEE.2017.8285486","url":null,"abstract":"One of the disease that require X-ray diagnosis is scoliosis. Early detection of scoliosis is important to do for anyone. From the early detection information, the doctor may take the firts step to further treatment quickly. Determination of spinal curvature is a first step method that used to measure how severe the degree of scoliosis. The severity degree of scoliosis can be assess by using Cobb angle. Therefore, by approximate the spinal curvature, we can approximate the cobb angle too. From previous work that interobserver measurement value may reach 11.8° and intraobserver measurement error is 6°. So, as far as the cobb angle measuring, the subjectivity aspect is the natural thing and can be tolerated until now. This research propose an algorithm how to define spinal curvature with the aid of a computer in digital X-ray image quickly but has a standard error that can be tolerated. The preprocessing has been done by canny edge detection. The k-means clustering algorithm can detect the centroid point after segmentation preprocessing of the spinal segment and polynomial curve fitting will be used in the process for determining the spinal curve. From the spinal curvature information, the scoliosis curve can be classified into 4 condition, normal, mild, moderate, and severe scoliosis.","PeriodicalId":130873,"journal":{"name":"2017 2nd International conferences on Information Technology, Information Systems and Electrical Engineering (ICITISEE)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130912076","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 : 2017-11-01DOI: 10.1109/ICITISEE.2017.8285544
M. F. Rachman, G. Saptawati
Database integration can potentially result in format, structure, syntax and semantic conflicts as data sources come from different places or applications. To handle this conflict, there are several schema matching techniques on the database individually or in combination. Schema matching is one of the stages in database integration using the bottom-up design method. This research proposes matching of hybrid combination schemas from two schema matching techniques, which are linguistic-based and constraint-based. Hybrid combination matching allows matching of individual schemas to complement each other. The result of matching this combination schema can be applied in query rewriting as multi-database query processing.
{"title":"Database integration based on combination schema matching approach (case study: Multi-database of district health information system)","authors":"M. F. Rachman, G. Saptawati","doi":"10.1109/ICITISEE.2017.8285544","DOIUrl":"https://doi.org/10.1109/ICITISEE.2017.8285544","url":null,"abstract":"Database integration can potentially result in format, structure, syntax and semantic conflicts as data sources come from different places or applications. To handle this conflict, there are several schema matching techniques on the database individually or in combination. Schema matching is one of the stages in database integration using the bottom-up design method. This research proposes matching of hybrid combination schemas from two schema matching techniques, which are linguistic-based and constraint-based. Hybrid combination matching allows matching of individual schemas to complement each other. The result of matching this combination schema can be applied in query rewriting as multi-database query processing.","PeriodicalId":130873,"journal":{"name":"2017 2nd International conferences on Information Technology, Information Systems and Electrical Engineering (ICITISEE)","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127035546","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 : 2017-11-01DOI: 10.1109/ICITISEE.2017.8285494
Ronald Adrian, Akhmad Dahlan, K. Anam
Software Defined-Network is a new technology in the network engineering. This technology allows a server which is called by a controller and it controls all connected devices. All configurations and resources of network devices become centralized to the controller. One of them is routing configuration. This makes it easier for network administrators to configure routing on complex networks. This study focused on OSPF implementation and QoS performance analysis on SDN networks. OSPF implementation can be configured using cost and no cost. This configuration can be done as a configuration of cost settings on conventional networks. It affects the selection of main data paths on OSPF routing. This implementation in this research used the Mikrotik devices. Data retrieval involves convergence time and some QoS parameters such as Throughput, PLR, Jitter and Delay. In testing phase used traffic data which generated by iperf and D-ITG with variations of existing data types. The goal of this research is to find the best routing configuration on SDN networks, especially in OSPF. This research can be expanded with the various parameters and complex topology.
{"title":"OSPF cost impact analysis on SDN network","authors":"Ronald Adrian, Akhmad Dahlan, K. Anam","doi":"10.1109/ICITISEE.2017.8285494","DOIUrl":"https://doi.org/10.1109/ICITISEE.2017.8285494","url":null,"abstract":"Software Defined-Network is a new technology in the network engineering. This technology allows a server which is called by a controller and it controls all connected devices. All configurations and resources of network devices become centralized to the controller. One of them is routing configuration. This makes it easier for network administrators to configure routing on complex networks. This study focused on OSPF implementation and QoS performance analysis on SDN networks. OSPF implementation can be configured using cost and no cost. This configuration can be done as a configuration of cost settings on conventional networks. It affects the selection of main data paths on OSPF routing. This implementation in this research used the Mikrotik devices. Data retrieval involves convergence time and some QoS parameters such as Throughput, PLR, Jitter and Delay. In testing phase used traffic data which generated by iperf and D-ITG with variations of existing data types. The goal of this research is to find the best routing configuration on SDN networks, especially in OSPF. This research can be expanded with the various parameters and complex topology.","PeriodicalId":130873,"journal":{"name":"2017 2nd International conferences on Information Technology, Information Systems and Electrical Engineering (ICITISEE)","volume":"149 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128138944","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 : 2017-11-01DOI: 10.1109/ICITISEE.2017.8285473
Periantu Marhendri Sabuna, D. Setyohadi
Text summarization is a process of compressing a text from the source to be a shorter version, but the version still contains the main information there. By reading the summary, the readers might be easy and fast to understand the contents instead of reading all the text. Because of that, it needs a method to understand, clarify, and present the whole information needed clearly and succinctly in the summary. So, it allows the readers save the time and energy. This research combining sentence scoring and decision tree method for automatic text summarization in Indonesian language. It uses the decision tree algorithm to choose which of sentences will be selected in summarization system. To produce the rules for decision tree, it uses 50 news texts as the training data. The produced-model from the training stage will be implemented for sentence selection process to the summarization system. The result shows the highest f-measure score is 0, 80 and the average is 0, 58. Based on this, it concludes that the result of document summarization using sentence scoring and decision tree shows a better accuracy score for news text document.
{"title":"Summarizing Indonesian text automatically by using sentence scoring and decision tree","authors":"Periantu Marhendri Sabuna, D. Setyohadi","doi":"10.1109/ICITISEE.2017.8285473","DOIUrl":"https://doi.org/10.1109/ICITISEE.2017.8285473","url":null,"abstract":"Text summarization is a process of compressing a text from the source to be a shorter version, but the version still contains the main information there. By reading the summary, the readers might be easy and fast to understand the contents instead of reading all the text. Because of that, it needs a method to understand, clarify, and present the whole information needed clearly and succinctly in the summary. So, it allows the readers save the time and energy. This research combining sentence scoring and decision tree method for automatic text summarization in Indonesian language. It uses the decision tree algorithm to choose which of sentences will be selected in summarization system. To produce the rules for decision tree, it uses 50 news texts as the training data. The produced-model from the training stage will be implemented for sentence selection process to the summarization system. The result shows the highest f-measure score is 0, 80 and the average is 0, 58. Based on this, it concludes that the result of document summarization using sentence scoring and decision tree shows a better accuracy score for news text document.","PeriodicalId":130873,"journal":{"name":"2017 2nd International conferences on Information Technology, Information Systems and Electrical Engineering (ICITISEE)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114441706","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 : 2017-11-01DOI: 10.1109/ICITISEE.2017.8285564
Ryan Yonata, A. Purwarianti
Emissions inventory is a data collection of accounted amount of pollutants released into the atmosphere. Emissions inventory can be useful to monitor air pollution in an area. Many countries in the world have already developed a system to manage emission inventory, excluding Indonesia. We have developed Inveo, a management information system for emissions inventory in Indonesia. As an e-administration system, Inveo aims to facilitate the process of collecting and managing regional emissions inventory data which also providing data and information to the public. Requirement analysis in this paper is defined by doing surveys and analyzing existing works and researches. Point, area, and road emissions inventory sources are modeled as a point. Inveo can manage several emissions inventory data such as emissions, locations, roads, surveys, facilities, fuels, map legend bounds, and user accounts. This system provides data and information to the public and is visualized by emission map, graphics/charts, and comparison statistics. Inveo is tested using three methods: functional testing, usability testing using system usability scale, and visualization testing. Based on the test result, the system is already fulfilled functional requirements, usability aspects with a SUS score of 75 and fulfilled usability and user experience goal, and the visualization is understandable for the users.
{"title":"Inveo, a management information system for emissions inventory e-administration in Indonesia","authors":"Ryan Yonata, A. Purwarianti","doi":"10.1109/ICITISEE.2017.8285564","DOIUrl":"https://doi.org/10.1109/ICITISEE.2017.8285564","url":null,"abstract":"Emissions inventory is a data collection of accounted amount of pollutants released into the atmosphere. Emissions inventory can be useful to monitor air pollution in an area. Many countries in the world have already developed a system to manage emission inventory, excluding Indonesia. We have developed Inveo, a management information system for emissions inventory in Indonesia. As an e-administration system, Inveo aims to facilitate the process of collecting and managing regional emissions inventory data which also providing data and information to the public. Requirement analysis in this paper is defined by doing surveys and analyzing existing works and researches. Point, area, and road emissions inventory sources are modeled as a point. Inveo can manage several emissions inventory data such as emissions, locations, roads, surveys, facilities, fuels, map legend bounds, and user accounts. This system provides data and information to the public and is visualized by emission map, graphics/charts, and comparison statistics. Inveo is tested using three methods: functional testing, usability testing using system usability scale, and visualization testing. Based on the test result, the system is already fulfilled functional requirements, usability aspects with a SUS score of 75 and fulfilled usability and user experience goal, and the visualization is understandable for the users.","PeriodicalId":130873,"journal":{"name":"2017 2nd International conferences on Information Technology, Information Systems and Electrical Engineering (ICITISEE)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114761543","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 : 2017-11-01DOI: 10.1109/ICITISEE.2017.8285519
W. S. M. Sanjaya, D. Anggraeni, Kiki Zakaria, Atip Juwardi, M. Munawwaroh
This paper discusses the development of Social Robot named SyPEHUL (System of Physic, Electronic, Humanoid Robot and Machine Learning) which can recognize and tracking human face. Face recognition and tracking process use Cascade Classification and LBPH (Local Binary Pattern Histogram) Face Recognizer method based on OpenCV library and Python 2.7. The social robot hardware based on Arduino microcontroller contains by 12 DoF (Degree of Freedom) motor servos to actuate robotic head and its face. The face recognition system has been implemented to Social Robot which can recognize and tracking human face and then mentioned the person name. The face recognition system of Social Robot result shows a good accuracy for Human-Robot Interaction.
{"title":"The design of face recognition and tracking for human-robot interaction","authors":"W. S. M. Sanjaya, D. Anggraeni, Kiki Zakaria, Atip Juwardi, M. Munawwaroh","doi":"10.1109/ICITISEE.2017.8285519","DOIUrl":"https://doi.org/10.1109/ICITISEE.2017.8285519","url":null,"abstract":"This paper discusses the development of Social Robot named SyPEHUL (System of Physic, Electronic, Humanoid Robot and Machine Learning) which can recognize and tracking human face. Face recognition and tracking process use Cascade Classification and LBPH (Local Binary Pattern Histogram) Face Recognizer method based on OpenCV library and Python 2.7. The social robot hardware based on Arduino microcontroller contains by 12 DoF (Degree of Freedom) motor servos to actuate robotic head and its face. The face recognition system has been implemented to Social Robot which can recognize and tracking human face and then mentioned the person name. The face recognition system of Social Robot result shows a good accuracy for Human-Robot Interaction.","PeriodicalId":130873,"journal":{"name":"2017 2nd International conferences on Information Technology, Information Systems and Electrical Engineering (ICITISEE)","volume":"235 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124038686","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}
Data mining is the process of handling information from a database which is invisible directly. Data mining is predicted to become a highly revolutionary branch of science over the next decade. One of data mining techniques is classification. The most popular classification technique is K-Nearest Neighbor (KNN). But there is also the Modified K-Nearest Neighbor (MKNN) classification algorithm which is the derived algorithm of KNN. In this paper we will analyze the comparison of KNN and MKNN algorithms to classify the data of Conditional Cash Transfer Implementation Unit (Unit Pelaksana Program Keluarga Harapan) which consist of 7395 records. Comparative analysis is based on the accuracy of both algorithms. Before classification, K-Fold Cross Validation was done to search for the optimal data modeling resulted in data modeling on cross 2 with accuracy of 93.945%. The results of K-Fold Cross Validation modeling will be the model for training data samples and testing data to test KNN and MKNN for classification. Classification result produced accuracy based on the rules of confusion matrix. The test resulted in the highest accuracy of KKN by 94.95% with average accuracy during the test was 93.94% and the highest accuracy of MKNN was 99.51% with the average accuracy during the test was 99.20%, almost all testing from the first test up to the tenth, MKNN algorithm is superior and has better accuracy value than KNN so it can be analyzed that the ability of MKNN algorithm in accuracy is better than KNN. It can be concluded that MKNN algorithm is capable of handling accuracy better for classification than KNN algorithm, by ignoring other aspects such as computerization, time efficiency, and algorithm effectiveness.
数据挖掘是对数据库中不可见的信息进行处理的过程。据预测，数据挖掘将在未来十年成为一门极具革命性的科学分支。数据挖掘技术之一是分类。最流行的分类技术是k -最近邻(KNN)。但也有改进的k近邻(MKNN)分类算法，它是KNN的衍生算法。本文将比较KNN和MKNN算法对有条件现金转移实施单元(Unit Pelaksana Program Keluarga Harapan) 7395条记录的数据进行分类。对比分析是基于两种算法的准确性。分类前进行K-Fold交叉验证，寻找最优的数据建模，得到交叉2上的数据建模，准确率为93.945%。K-Fold交叉验证建模的结果将作为训练数据样本和测试数据的模型，用于测试KNN和MKNN进行分类。分类结果根据混淆矩阵的规则产生准确率。测试结果表明，KKN的最高准确率为94.95%，测试平均准确率为93.94%;MKNN的最高准确率为99.51%，测试平均准确率为99.20%，从第一次测试到第十次测试，几乎所有测试中，MKNN算法都优于KNN算法，具有更好的准确率值，因此可以分析MKNN算法在准确率方面的能力优于KNN。可以得出结论，在忽略计算机化、时间效率和算法有效性等其他方面的情况下，MKNN算法对分类的处理精度优于KNN算法。
{"title":"Comparative analysis of k-nearest neighbor and modified k-nearest neighbor algorithm for data classification","authors":"Okfalisa, Ikbal Gazalba, Mustakim, Nurul Gayatri Indah Reza","doi":"10.1109/ICITISEE.2017.8285514","DOIUrl":"https://doi.org/10.1109/ICITISEE.2017.8285514","url":null,"abstract":"Data mining is the process of handling information from a database which is invisible directly. Data mining is predicted to become a highly revolutionary branch of science over the next decade. One of data mining techniques is classification. The most popular classification technique is K-Nearest Neighbor (KNN). But there is also the Modified K-Nearest Neighbor (MKNN) classification algorithm which is the derived algorithm of KNN. In this paper we will analyze the comparison of KNN and MKNN algorithms to classify the data of Conditional Cash Transfer Implementation Unit (Unit Pelaksana Program Keluarga Harapan) which consist of 7395 records. Comparative analysis is based on the accuracy of both algorithms. Before classification, K-Fold Cross Validation was done to search for the optimal data modeling resulted in data modeling on cross 2 with accuracy of 93.945%. The results of K-Fold Cross Validation modeling will be the model for training data samples and testing data to test KNN and MKNN for classification. Classification result produced accuracy based on the rules of confusion matrix. The test resulted in the highest accuracy of KKN by 94.95% with average accuracy during the test was 93.94% and the highest accuracy of MKNN was 99.51% with the average accuracy during the test was 99.20%, almost all testing from the first test up to the tenth, MKNN algorithm is superior and has better accuracy value than KNN so it can be analyzed that the ability of MKNN algorithm in accuracy is better than KNN. It can be concluded that MKNN algorithm is capable of handling accuracy better for classification than KNN algorithm, by ignoring other aspects such as computerization, time efficiency, and algorithm effectiveness.","PeriodicalId":130873,"journal":{"name":"2017 2nd International conferences on Information Technology, Information Systems and Electrical Engineering (ICITISEE)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114970199","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 : 2017-11-01DOI: 10.1109/ICITISEE.2017.8285526
Muhammad Nurwiseso Wibisono, A. S. Ahmad
The existence of useful weather forecast information can help various parties in doing their activities. In this study, weather forecasts were made using a new Artificial Intelligence method called Knowledge Growing System. The Knowledge Growing System utilizes weather indication criteria as the basis of weather forecasts. Based on indicators that have been determined in this study, the A3S algorithm can be used to predict the occurrence according to the indicated indication with the accuracy of 70.86%. The best scenario of the study was able to forecast with an accuracy of 79%. This result suggests that decision-making is necessary considering the OM-A3S forecasts and readings from A3S for optimal forecasting results.
{"title":"Weather forecasting using Knowledge Growing System (KGS)","authors":"Muhammad Nurwiseso Wibisono, A. S. Ahmad","doi":"10.1109/ICITISEE.2017.8285526","DOIUrl":"https://doi.org/10.1109/ICITISEE.2017.8285526","url":null,"abstract":"The existence of useful weather forecast information can help various parties in doing their activities. In this study, weather forecasts were made using a new Artificial Intelligence method called Knowledge Growing System. The Knowledge Growing System utilizes weather indication criteria as the basis of weather forecasts. Based on indicators that have been determined in this study, the A3S algorithm can be used to predict the occurrence according to the indicated indication with the accuracy of 70.86%. The best scenario of the study was able to forecast with an accuracy of 79%. This result suggests that decision-making is necessary considering the OM-A3S forecasts and readings from A3S for optimal forecasting results.","PeriodicalId":130873,"journal":{"name":"2017 2nd International conferences on Information Technology, Information Systems and Electrical Engineering (ICITISEE)","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123274618","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：