Annisa Monicha Sari, A. Hidayanto, B. Purwandari, N. Budi, M. Kosandi
{"title":"Challenges and Issues of E-Participation Implementation: A Case Study of E-Complaint Indonesia","authors":"Annisa Monicha Sari, A. Hidayanto, B. Purwandari, N. Budi, M. Kosandi","doi":"10.1109/IAC.2018.8780467","DOIUrl":"https://doi.org/10.1109/IAC.2018.8780467","url":null,"abstract":"The evolution of information, communication and technology (ICT) brings many benefits in the relationship between citizens and governments to offer better services in public sector. But, in the fact e-participation implementation faces many challenges and issues especially in developing countries. Digital divide, lack skilled people and poor citizen education becomes a factor that impede e-participation implementation. This study aims to explore and analysis challenge and issues of e-complaint implementation in Indonesia. The interview was conducted using semi-structured in two weeks with the expert and citizen. The selection of the expert and citizen as participant was conducted using purposive sampling. STOPE framework used over this study to analyze and to keep objective of the research in context. There are 10 sub-domains based on STOPE framework analysis as the challenges and issues of e-complaint in Indonesia. Those are political bureaucracy & commitment, promoting strategy, security & privacy, lack skilled people in organization, team coordination, lack of training in organization, poor citizen education, trust, digital divide and social culture. Challenges and issues of e-complaint in Indonesia can analyze to provide insight that can be used for developing better e-complaint platform in Indonesia.","PeriodicalId":448672,"journal":{"name":"2018 Third International Conference on Informatics and Computing (ICIC)","volume":"70 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127349872","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}
T. Raharjo, B. Purwandari, Riri Satria, Iis Solichah
{"title":"Critical Success Factors for Project Management Office: An Insight from Indonesia","authors":"T. Raharjo, B. Purwandari, Riri Satria, Iis Solichah","doi":"10.1109/IAC.2018.8780504","DOIUrl":"https://doi.org/10.1109/IAC.2018.8780504","url":null,"abstract":"Recent surveys show that success rate of Information Technology (IT) projects implementation is relatively low i.e. below 50%. It is caused by weak implementation of project management standards and methodologies, which are highly dependent on Project Management Office (PMO). On the other hand, PMO implementation is prone to failures. To address this problem, this study aims to determine the rank of factors influencing PMO success in IT projects in Indonesia. Firstly, a qualitative approach was carried out by a literature study and expert reviews to identify the PMO success factors. Secondly, a quantitative approach was conducted using the Analytic Hierarchy Process (AHP) to model the PMO success criteria and critical success factors (CSF). The results indicate that the top level management support is the most critical factor for the PMO implementation, while the project success is the top rank of success criteria.","PeriodicalId":448672,"journal":{"name":"2018 Third International Conference on Informatics and Computing (ICIC)","volume":"71 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124858935","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}
P. Sukmasetya, B. Purwandari, Larastri Kumaralalita, Erni Juraida
{"title":"Adoption Factors of e-Government Services in Indonesia","authors":"P. Sukmasetya, B. Purwandari, Larastri Kumaralalita, Erni Juraida","doi":"10.1109/IAC.2018.8780477","DOIUrl":"https://doi.org/10.1109/IAC.2018.8780477","url":null,"abstract":"Governments around the world are working continuously in order to improve services for their citizens. However, the success of these initiatives is dependent not only on the government support but also on the citizens' willingness to use these services. To implement e-government successfully, it is important to determine the factors related to its adoption. This paper examines the adoption factors of e-government services in the entire world, then validate with current research in Indonesia. To identify the adoption factor we use meta-ethnography combining with desk research methodology. In this context, these papers describe and find the influencing adoption factors e-government service in Indonesia. We found 40 studies from journal articles and conference paper that obtain 272 concepts of adoption factors from it. After we reduce and found the similarity between the concepts now we have 96 lists of adoption factors. Those 96 lists of adoption factors are divided into eightdomains i.e. perceived of citizen, government support, technological support, organizational structure, technological acceptance, human resources, other stakeholder support, and culture, and there should become consideration in e-Government service implementation in Indonesia.","PeriodicalId":448672,"journal":{"name":"2018 Third International Conference on Informatics and Computing (ICIC)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125036068","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":"Students' Academic Performance Prediction using Data Mining","authors":"Fergie Joanda Kaunang, Reymon Rotikan","doi":"10.1109/IAC.2018.8780547","DOIUrl":"https://doi.org/10.1109/IAC.2018.8780547","url":null,"abstract":"Educational Data Mining has been an emerging topic nowadays due to the growth of educational data. This field makes it possible to develop methods in order to find out hidden patterns from educational data. The methods extracted from Educational Data Mining discipline are then used to understand students including their learning behavior as well as to predict their academic performance. This study proposes a model for predicting the academic performance of Computer Science students using Data Mining technique. The data were collected using questionnaires that contain the students' demographics, previous GPA, and family background information. Two Data Mining models (Decision Tree and Random Forest) are applied to the students' data to create the best student's academic performance prediction model. The result of this study shows that Decision Tree is the best model compared to Random Forest by receiving the highest accuracy value of 66.9%. This study shows that there are some relevant feature that influenced student's academic performance.","PeriodicalId":448672,"journal":{"name":"2018 Third International Conference on Informatics and Computing (ICIC)","volume":"103 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116107085","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":"Template Matching Algorithm For Noise Detection in Cargo Container","authors":"Doni S. Pambudi, Ruktin Handayani, L. Hidayah","doi":"10.1109/IAC.2018.8780543","DOIUrl":"https://doi.org/10.1109/IAC.2018.8780543","url":null,"abstract":"A seaport terminal providing services for inbound and outbound flow of cargo container often has issues with the handling of container. In some cases, the port is sued for damaged container which in fact has not been taken care properly before arriving in the port. Hence an automatic detection system for damaged container is needed. In this research, an algorithm to identify objects in a side-view of a cargo container is proposed. The objects include but not limited to company name of the cargo container, logo, identification code, signs, labels, and damages. By utilizing template matching algorithm, an algorithm to identify objects in container images has been developed. The achieved visual result was satisfactory as well as the computational aspect.","PeriodicalId":448672,"journal":{"name":"2018 Third International Conference on Informatics and Computing (ICIC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129565418","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":"Determinant factors of new investor intention for using online trading system","authors":"Fikri Pratama Afif, P. W. Handayani, A. Pinem","doi":"10.1109/IAC.2018.8780574","DOIUrl":"https://doi.org/10.1109/IAC.2018.8780574","url":null,"abstract":"The support of government in the improvement of stock investment has been encourage security firms to develop online trading system. The system is implemented to support the remote stock trading and suppose to boost transaction number. This study aims to analyses the factors that determine the intention of new investors in using such system. Three popular theories are used such as Technology Acceptance Model, Theory of Reasoned Action, Theory of Planned Behavior. The data collected with five Likert scale questionnaires and analyze with structural equation modeling (SEM) technique using SmartPLS. The result shows that perceived ease of use and perceived usefulness has significant effect towards attitude that will affect the intention. Trust also found to has influence toward subjective norm that ultimately affect intention. However, perceived behavior control is not proven to influence investor intention to use the system.","PeriodicalId":448672,"journal":{"name":"2018 Third International Conference on Informatics and Computing (ICIC)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129668798","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}
D. Suryani, A. Hidayat, Ause Labellapansa, Setia Wulandari
{"title":"Data Mining Classification of Intelligence Quotient in High School Students","authors":"D. Suryani, A. Hidayat, Ause Labellapansa, Setia Wulandari","doi":"10.1109/IAC.2018.8780432","DOIUrl":"https://doi.org/10.1109/IAC.2018.8780432","url":null,"abstract":"An educational institution of “SMA Negeri 1 Padang” has been conducting psychological tests using the Intelligenz Structure Test(IST) in determining the classification of Intelligence Quotient (IQ) of their own students high school. Up to this day classification process of student IQs has been handled manually by psychologists and took an average of 15 days. This study applies data mining study which is a series of processes of getting knowledge or pattern from data set using method of classification that is C4.5 algorithm, which is useful to simplify and save time for psychologists. This study consisted of 10 criteria involving 270 data and resulted in output in the form of four kinds of student'IQ classification consisting of very superior, superior, above average and average categories. Based on the test results classification of student IQs is obtained as 84%.","PeriodicalId":448672,"journal":{"name":"2018 Third International Conference on Informatics and Computing (ICIC)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128573896","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}
Akeem Olowolayemo, A. Alarood, Shee Niang Yap, T. Mantoro
{"title":"SPOT: A Low Cost Intelligent Parking System for Urban Malls","authors":"Akeem Olowolayemo, A. Alarood, Shee Niang Yap, T. Mantoro","doi":"10.1109/IAC.2018.8780413","DOIUrl":"https://doi.org/10.1109/IAC.2018.8780413","url":null,"abstract":"Manual searching for vacant parking spaces in shopping malls is a tedious process, especially during the weekends and public holidays. It is rather troublesome for consumers to drive around without any directions in search for an empty parking space. Previous studies have confirmed that majority of the drivers experience difficulties in finding a vacant parking space in multilevel car parks and often spend more than 10 minutes searching for parking lots on weekends and public holidays. Current solutions available are probably expensive to implement in terms of hardware and software requirements for shopping malls in developing economies such as Malaysia. Furthermore, the process of payment of parking fee at an Autopay machine is not only bothersome but avoidable. Although leaving a car park may not be as tedious as finding a vacant parking lot, it is still considered a time-consuming activity to most drivers. If given the chance, most people would choose to avoid the trouble of walking to an Autopay machine due to finding incessant long queues or shortage of change at the machine. Advanced societies have adopted Touch‘n'Go system which is more convenient than the conventional Autopay system. However, developing economies like Malaysia are still using the manual parking as well as Autopay machine. This study presents a low cost intelligent parking system for developing countries shopping malls to mitigate the above challenges. The system provides an added safety and security measures to prevent crimes such as thefts, kidnapping, vandalism and accidents. The system proposed takes advantage of the fact that smartphones have become pervasive.","PeriodicalId":448672,"journal":{"name":"2018 Third International Conference on Informatics and Computing (ICIC)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128978571","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}
Ericks Rachmat Swedia, A. Mutiara, M. Subali, Ernastuti
{"title":"Deep Learning Long-Short Term Memory (LSTM) for Indonesian Speech Digit Recognition using LPC and MFCC Feature","authors":"Ericks Rachmat Swedia, A. Mutiara, M. Subali, Ernastuti","doi":"10.1109/IAC.2018.8780566","DOIUrl":"https://doi.org/10.1109/IAC.2018.8780566","url":null,"abstract":"This paper presents Indonesian speech digit of decimal number (0–9) recognition using Deep Learning Long-Short Term Memory (LSTM). The LPC (Linear Predictive Coding) and MFCC (Mel-Frequency Cepstrum) feature extraction was used as an input on the LSTM model and the level of recognition accuracy was compared. The LPC feature extract speech feature based on a pitch or fundamental frequency, while MFCC extract speech feature based on the sound spectrum. We used 7990 speech digits consisted of 12 LPC coefficients and 12 MFCC coefficients as training data, while 790 data was used to classify on LSTM that had been trained. The results show that using LSTM for recognize Indonesian speech digit, the MFCC feature extraction gets better accuracy result of 96.58% compared to the LPC feature extraction which amounts to 93.79 %.","PeriodicalId":448672,"journal":{"name":"2018 Third International Conference on Informatics and Computing (ICIC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130291548","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":"Classification of Indonesian Government Budget Appropriations or Outlays for Research and Development (GBAORD) Using Decision Tree and Naive Bayes","authors":"A. F. Syafiandini, Hani Febri Mustika, Y. Rianto","doi":"10.1109/IAC.2018.8780563","DOIUrl":"https://doi.org/10.1109/IAC.2018.8780563","url":null,"abstract":"Indonesian Government Budget Appropriations or Outlays for Research and Development (GBAORD) is one of component from Indonesian Gross Expenditure on Research and Development (GERD). Calculation of GBAORD are made by classifying each government expenditure budget. Classification is done manually so that there are frequent inconsistencies. This study proposed an automated classification using decision tree and Naive bayes algorithm to handle problem in previous method. The result of study have conclusion that an automated classification using Naive bayes is more accurate than using decision tree. The highest average accuracy score given by Naive Bayes model in this experiment is 98.462 while decision tree gives about 90.236. This can be related to the number of features involved in classification process. Naive Bayes uses most of the features in data while decision tree only uses one feature. Although decision tree model only uses one feature, the accuracy score produced by the model is considered as high.","PeriodicalId":448672,"journal":{"name":"2018 Third International Conference on Informatics and Computing (ICIC)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126785731","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}