2017 7th International Annual Engineering Seminar (InAES)最新文献

{"title":"Towards SDI services for crowdsourcing spatial data in disaster response","authors":"Arie Yulfa, T. Aditya, H. Sutanta","doi":"10.1109/INAES.2017.8068577","DOIUrl":"https://doi.org/10.1109/INAES.2017.8068577","url":null,"abstract":"Spatial data are indispensable in supporting disaster responders. Accurate locations of disaster areas will help responders to create an appropriate response in reducing the impact of the disaster. Indonesian Geospatial Information Agency (BIG-Badan Informasi Geospasial) has made spatial data available for emergency response activities across the country. In 2011, BIG launched a geoportal, which is a part of National Spatial Data Infrastructure Network (NSDIN). For data related to the disaster, Indonesian National Board for Disaster Management (BNPB) handles them that is a part of the network. At the national level, the data are in medium and small scale and not suitable for operational purposes such as in disaster response. Indonesian law on geospatial information has ordered the local government to develop local SDI to solve it. In disaster response phase, the data should reflect the latest situation, complete and reliable. They should be available in a short time period. SDI has impediments to meet these criteria because it is built based on the authoritative perspective that is not agile. On the other side, crowds enrich and update data rapidly by utilizing web 2.0 technology (e.g. social media and map applications). This paper discusses existing SDI frameworks and crowdsourcing concepts in Indonesia and global levels to come up with a new framework that can comply with disaster response activities.","PeriodicalId":382919,"journal":{"name":"2017 7th International Annual Engineering Seminar (InAES)","volume":"73 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131854296","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}

{"title":"Tourism contextual information for recommender system","authors":"Kusuma Adi Achmad, L. Nugroho, Widyawan, A. Djunaedi","doi":"10.1109/INAES.2017.8068555","DOIUrl":"https://doi.org/10.1109/INAES.2017.8068555","url":null,"abstract":"Tourism recommender system relies on several items in supporting its effectiveness in the context. The items' searching and selecting needed tools, such recommender system. The item concerns with the contextual information, such as location, time, or social. Recommendations that use contextual information in processing recommendations are Context-Aware Recommender System. However, to identify and acquire contextual information that may affect user preferences in the decision-making process is considered challenging. Therefore, in providing recommendations, the system requires the identification of relevant contextual information. The contextual information proposes a list of proper relevant tourism items information to tourists, when tourists are in a specific location at a certain time, activity on social networks, and with particular weather situations. This study aims to identify relevant contextual information based on study associated research Context-Aware Recommender System for tourism.","PeriodicalId":382919,"journal":{"name":"2017 7th International Annual Engineering Seminar (InAES)","volume":"191 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122498334","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}

{"title":"A Combination of multiple imputation and principal component analysis to handle missing value with arbitrary pattern","authors":"Novita Anindita, H. A. Nugroho, T. B. Adji","doi":"10.1109/INAES.2017.8068537","DOIUrl":"https://doi.org/10.1109/INAES.2017.8068537","url":null,"abstract":"Hepatitis is one of the major health problems which can progress to chronic hepatitis and cancer. Currently, computer based diagnosis is commonly use among medical examination. The diagnosis has been examined by using the disease dataset as a reference to make the decisions. However, the dataset was incomplete because it contained many instances containing missing values. This situation can lead the results of the analysis to be biased. One method of handling missing values is Multiple Imputation. Hepatitis dataset has an arbitrary pattern of missing values. This pattern can be handled by using Markov Chain Monte Carlo (MCMC) and Fully Conditional Specification (FCS) as Multiple Imputation algorithms. The research conducted an experiment to compare combinations of Multiple Imputations algorithm and Principal Component Analysis (PCA) as instance selection. Instance selection applied to reduce data by selecting variables that contribute greatly to the dataset. The goal was to improve the accuracy of the analysis on data which had missing values with the arbitrary pattern. The results showed that FCS-PCA is the best performance with the higher accuracy (98.80%) and the lowest error rate (0.0116).","PeriodicalId":382919,"journal":{"name":"2017 7th International Annual Engineering Seminar (InAES)","volume":"159 6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128942861","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}

{"title":"Mitigation of nonlinear impact on optical fiber","authors":"Fakhriy Hario, S. Pramono, I. Mustika, A. Susanto","doi":"10.1109/INAES.2017.8068549","DOIUrl":"https://doi.org/10.1109/INAES.2017.8068549","url":null,"abstract":"Laser is one of the light sources that transmits wavelength with frequency, phase, and polarization parameters. The response of each dielectric material to light created nonlinear in strong electromagnetic field. Nonlinearity occurs within the optical fiber with high intensity of light at the core with long span. The focus of this paper was to reduce the behavior of nonlinearity SPM (Self Phase Modulation) and GVD (Group Velocity Dispersion) characteristic on optical fiber transmission using frequency dithering technique. This paper showed signal characteristic after passing dithering system. By investigating this, we expected to mitigate and reduce nonlinearity on optical medium transmission for minimum OLP (Optical Launch Power) with varied linewidth. The peak of maximum power after the technique was applied was −30 dBm with respective SNR (Signal to Noise Ratio) and EVM (Error-Vector-Magnitude) value were 65 dB and 0.125 % on linewidth of 0.15 MHz.","PeriodicalId":382919,"journal":{"name":"2017 7th International Annual Engineering Seminar (InAES)","volume":"51 3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120996172","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}

{"title":"3D modelling and visualization of drinking water supply system using 3D GIS","authors":"Auliantya Ayurin Putri, T. Aditya","doi":"10.1109/INAES.2017.8068574","DOIUrl":"https://doi.org/10.1109/INAES.2017.8068574","url":null,"abstract":"Since 2014, Universitas Gadjah Mada (UGM) has established Drinking Water Supply System (DWSS). DWSS established in UGM is in line with Indonesian Government's target to fulfil needs for 100% drinking water access in 2019. In order to improve the system's sustainability, planning and monitoring through mapping and 3D modeling of DWSS network distribution are needed. DWSS is expected to not only be well distributed but also to be easily monitored and accessed by student and staff in UGM. The purpose of this paper is to present the utilization of 3D GIS to map water dispenser location and to model the distribution of pipeline network. It also focuses to assess the appropriateness of the planned water supply for fulfilling the need of drinking water in each faculty. In addition to that, the map of water dispenser finder is also developed to support campus community in finding the nearest drinking water facility. Data acquisition of water location has been done by using Handheld GPS, while data processing and presentation of DWSS in 3D format are done by using AutoCad 2009, ArcGIS for Desktop 10.3.1 (ArcScene and ArcMap), while Sketch Up, ArcGIS Online, and CityEngine Web Viewer are used to model and visualize the 3D map. This project produces Geographic Information System of DWSS in UGM. The 3D GIS of DWSS encompasses the map of distribution of water dispensers, the 3D model of network distribution of drinking water supply, and analysis of planned water supply in order to assess the drinking water needs in each faculty. The map of distribution of water dispensers is a map depicting distributed water dispenser in UGM. 3D map of network distribution of DWSS is built by combining multiple datasets including UGM's Digital Terrain Model (DTM), 3D model of campus building, 3D model of pipeline network, 3D model of water dispensers and reservoirs. 3D network distribution modeling of DWSS is presented offline by using ArcScene 10.3.1 •software and presented online by using CityEngine Web Viewer. From GIS analysis, it is found that the planned water supply has not answered the need of drinking water in each faculty. It can be seen that need of drinking water has reached 218.870 litres per day, whereas the planned supply is only 3.889,30 liters per day.","PeriodicalId":382919,"journal":{"name":"2017 7th International Annual Engineering Seminar (InAES)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123585037","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}

{"title":"Recommendation of cloud computing use for the academic data storage in University in Lampung Province, Indonesia","authors":"Wasilah, L. Nugroho, P. Santosa, R. Ferdiana","doi":"10.1109/INAES.2017.8068552","DOIUrl":"https://doi.org/10.1109/INAES.2017.8068552","url":null,"abstract":"A number of benefits originate from the implementation of cloud computing. Cloud computing has been extensively implemented in organizations like companies and universities. However, in Indonesia cloud computing is not widely approved by universities yet. This condition can be caused by lack of trust of the universities in the risk of cloud computing implementation. This paper is intended to analyze the risk of current data management of academic data in university, as a baseline to create recommendation of cloud computing use in university. The result of the analysis is risk leveling of academic data of cloud computing implementation in Higher Education. The process used is COBIT 5 Framework. The result of this study is expected to help the IT staffs to manage academic data in universities more effectively. Eventually, it is able to give effects on the increase of service quality.","PeriodicalId":382919,"journal":{"name":"2017 7th International Annual Engineering Seminar (InAES)","volume":"35 11","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120821903","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}

{"title":"Gender recognition using PCA and LDA with improve preprocessing and classification technique","authors":"Reza Ferizal, S. Wibirama, N. A. Setiawan","doi":"10.1109/INAES.2017.8068547","DOIUrl":"https://doi.org/10.1109/INAES.2017.8068547","url":null,"abstract":"This paper explains the gender recognition system through a human facial image by using the basic method of Principal Component Analysis (PCA) combined with Linear Discriminant Analysis (LDA). PCA+LDA method performance can be improved by improvising the preprocessing techniques such as resizing the image, equalizing the histogram, and removing the variation of the image background by adding oval masking face. Furthermore, in classification process, using 9 nearest neighbors gives the better recognition accuracy rather than using only 1 nearest neighbor. The highest accuracy results obtained with the proposed method is superior to get 89.70% when compared to the PCA + LDA method without adding masking face, which only reached 84.16%.","PeriodicalId":382919,"journal":{"name":"2017 7th International Annual Engineering Seminar (InAES)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133528999","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}

{"title":"Twitter sentiment analysis using deep learning methods","authors":"Adyan Marendra Ramadhani, H. Goo","doi":"10.1109/INAES.2017.8068556","DOIUrl":"https://doi.org/10.1109/INAES.2017.8068556","url":null,"abstract":"The social media has Immense and popularity among all the services today. Data from SNS (Social Network Service) can be used for a lot of objectives such as prediction or sentiment analysis. Twitter is a SNS that has a huge data with user posting, with this significant amount of data, it has the potential of research related to text mining and could be subjected to sentiment analysis. But handling such a huge amount of unstructured data is a difficult task, machine learning is needed for handling such huge of data. Deep learning is of the machine learning method that use the deep feed forward neural network with many hidden layers in the term of neural network with the result of the experiment about 75%.","PeriodicalId":382919,"journal":{"name":"2017 7th International Annual Engineering Seminar (InAES)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130121059","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}

{"title":"LEO antenna ground station analysis using fast fourier transform","authors":"A. Hidayat, S. Munawar, S. Syarif, A. Achmad","doi":"10.1109/INAES.2017.8068548","DOIUrl":"https://doi.org/10.1109/INAES.2017.8068548","url":null,"abstract":"One of the quality requirements of a satellite low Earth orbit receiver antenna that maintain is pointing accuracy. The research is trying to using FFT to analyze the quality of the LEO satellite receiver antenna. Currently, the antenna tracking error analysis using graphs fault elevation and azimuth error compare to the time. This study is conducted find the effectiveness of the frequency and amplitude of the tracking error using (FFT). FFT is able to count the amplitude and frequency error. Calculation and plotting error by FFT in this study shows amplitude and frequency error. The goal system malfunction at data reception, recording satellite reception can be detected earlier. Solution and problem solving more efficient, availability of systems also data continuity can be maintained and protected.","PeriodicalId":382919,"journal":{"name":"2017 7th International Annual Engineering Seminar (InAES)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125878271","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}

{"title":"A comparison of different part-of-speech tagging technique for text in Bahasa Indonesia","authors":"Ahmad Zuli Amrullah, Rudy Hartanto, I. Mustika","doi":"10.1109/INAES.2017.8068538","DOIUrl":"https://doi.org/10.1109/INAES.2017.8068538","url":null,"abstract":"Part of speech tagging has some different methods or techniques to the problem in assigning each word of a text with a part-of-speech tag. In this paper, we conducted some part-of-speech tagging techniques for Bahasa Indonesia experiments using statistical approach (Unigram, Hidden Markov Models) and Brill's tagger. In this study, we used Supervised POS Tagging approach requiring a large number of annotated training corpuses to tag properly. We used some resource annotation corpus of Bahasa. Those corpuses were implemented with POS Tagging techniques. We subsequently compared and analyzed the results. We also compared the accuracy and highlighted some advantages and disadvantages for every technique we used. Unigram showed a higher accuracy compared to HMM and Brill tagger with 88,37% on a tagged corpus.","PeriodicalId":382919,"journal":{"name":"2017 7th International Annual Engineering Seminar (InAES)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129734792","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}